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(1)AGH UNIVERSITY OF SCIENCE AND TECHNOLOGY FACULTY OF ELECTRICAL ENGINEERING, AUTOMATICS, COMPUTER SCIENCE AND BIOMEDICAL ENGINEERING. M.Sc. Klaudia Proniewska. Doctoral thesis. Identification of human vital functions based on selected cardiac and respiratory signals. Promotor: Professor Piotr Augustyniak, PhD, DSc, Eng.. Kraków, 2014.

(2) This PhD thesis is dedicated to My Family and Friends.. At this point I would like to thank Professor Piotr Augustyniak, for a lot of help during the course of the study and for motivation, which helped finish the thesis.. I would also like to thank my dearest husband Bartosz, for his support, faith, and sacrifice..

(3) "I acknowledge that during the work on my thesis I have been a scholarship fellow of the "Doctus – Małopolski fundusz stypendialny dla doktorantów” project cofunded by EU funds within European Social Fund.". This work was partially funded by the Committee for Scientific Research as a research project No. N N518 426736.: "Investigation of multi-modal measurements of selected biological parameters and evaluation of their application in a domestic environment for people with disabilities".

(4) Table of contents:. 1. Introduction .....................................................................................................................................1 1.1. 2. 3. 4. Structure of the thesis ..............................................................................................................3. Methodological review ....................................................................................................................4 2.1. Telemedicine and remote monitoring of patients ...................................................................4. 2.2. Monitoring of cardiovascular diseases.....................................................................................5. 2.3. Identification of selected human vital function – breathing disorders....................................6. 2.4. Specific methods for identification of sleep disorders ......................................................... 11. Physiological background .............................................................................................................. 15 3.1. Anatomy and physiology of the heart ................................................................................... 15. 3.2. Anatomy and physiology of the vocal tract .......................................................................... 18. New method of monitoring of human sleep at home .................................................................. 21 4.1. Idea of the ECG/acoustic based sleep analysis system ......................................................... 21. 4.1.1. Acquisition of the ECG signal......................................................................................... 24. 4.1.2. Acquisition of the acoustic signal .................................................................................. 25. 4.2. Characterizing of cardiac parameters variability .................................................................. 26. 4.2.1. Commonly used HRV parameters ................................................................................. 26. 4.2.2. Implementation of HRV calculation procedures ........................................................... 29. 4.2.3. Estimating the respiration from ECG variations ............................................................ 30. 4.2.4. Implementation of EDR procedure ............................................................................... 33. 4.3. Characterizing of respiratory parameters variability ............................................................ 34. 4.3.1. Diagnostic parameters of the vocal tract ...................................................................... 34. 4.3.2. Voice recognition procedure ......................................................................................... 36. 4.3.3. Selected methods of voice feature extraction .............................................................. 38. 4.3.4. Time domain analysis .................................................................................................... 38. 4.3.5. Frequency domain analysis ........................................................................................... 40. 4.3.6. Acoustic analysis in time-frequency domain,................................................................ 42. 4.3.7. Mel Frequency Cepstral Coefficients ............................................................................ 43. 4.3.8. Implementation of acoustic analysis procedures ......................................................... 44. 4.4. Sleep recording and scoring at home.................................................................................... 48. 4.5. Statistical analysis methods used in sleep analysis system verification ............................... 50. 4.5.1. Statistical analysis.......................................................................................................... 50. 4.5.2. Tukey's Honestly Significant Difference (HSD) Test ...................................................... 51. 4.5.3. Logistic regression ......................................................................................................... 51. 4.5.4. Receiver Operating Characteristic................................................................................. 52.

(5) 4.5.5 5. 6. Experimental results...................................................................................................................... 57 5.1. Statistical data analysis ......................................................................................................... 57. 5.2. Data mining classification with constructed Random Forests models ................................. 65. Discussion ...................................................................................................................................... 74 6.1. Summary of main results ...................................................................................................... 74. 6.1.1. Qualitative assessment of data ..................................................................................... 74. 6.1.2. Quantitative assessment of data................................................................................... 77. 6.2 7. Data Mining with Random Forests ................................................................................ 53. The best model for recognition sleep disorders ................................................................... 79. Summary ....................................................................................................................................... 80 7.1. Proving the thesis .................................................................................................................. 80. 7.2. New things presented ........................................................................................................... 80. 7.3. Limitations of the thesis ........................................................................................................ 81. 7.4. Future perspectives ............................................................................................................... 82. 8. Annex............................................................................................................................................. 83. 9. List of figures ................................................................................................................................. 96. 10 List of tables .................................................................................................................................. 98 11 References ................................................................................................................................... 103 12 Streszczenie pracy doktorskiej .................................................................................................... 115.

(6) Identification of human vital functions based on selected cardiac and respiratory signals. 1 Introduction Current trends in patient care emphasize the overall quality of life as a goal for treatment outcome, therefore various ways of continuous patient monitoring have become increasingly popular in health care. Today, the acquisition of bioelectrical signals and the development of new signal processing techniques continue to excite physicians and engineers alike, as over the years these have helped to reveal new information regarding various diseases. Human body consists of a number of biological systems that carry out specific vital functions necessary for everyday living. In the context of this dissertation the circulatory system is presented as a representative of vital functions. Based on the circulatory system, selected cardiac and respiratory signals and dedicated parameters are identified in the case of improper functioning of vital functions. The circulatory system consists of three independent systems that work together: the heart (cardiovascular), lungs (pulmonary) and arteries, veins, coronary and peripheral vessels (systemic). The cardiovascular component of the circulatory system is made up with the heart, blood and blood vessels. It cooperates with the pulmonary circulation, through the lungs, where blood is oxygenated, as well as with the systemic circulation, which runs through the rest of the body providing oxygenated blood. A disturbance in the functionality of the circulatory system could lead to diseases of the cardiovascular system. Monitoring of several vital cardiac and respiratory parameters could help to identify such disturbances. With cardiovascular diseases as one of the main causes of death, the evaluation of bioelectrical signals is indispensable in the medical field providing valuable diagnostic information. One of the interesting topics in modern health care is sleep monitoring. Since an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall life quality. During the recent years, various sleep disorders were characterized with specific symptoms, signs and changes in obtained parameters. The influence of sleeping conditions on human health and well-being is not fully understood and still underestimated. Sleep disorder patients are showing a higher prevalence of heart diseases [1] and thus sleep monitoring often leads also to the diagnosis of cardiac diseases. Previous research was either focused on either sleep acoustic signal analysis or ECG analysis.. 1.

(7) Identification of human vital functions based on selected cardiac and respiratory signals. The aim of the thesis is to create valid statistical models for automatic detection of sleep disorders such as snoring, wheezing and/or sleep apnea using acoustic derived biosignals, correlated to the patients ECG signal acquired during sleep.. The following thesis has been presented and proven:. Simultaneously acquired acoustic and ECG signals can be used to quantify respiratory obstructions during sleep. Statistical predictive models can be used with high sensitivity and specificity to identify such events, allowing an inexpensive and accurate diagnosis.. More specifically, the current work is aimed at finding a correlation between two particular physiological signals (vital functions): acoustic signal of breathing and ECG signal represented by heart activity parameters acquired during the same period of breathing during sleep. Evaluation of the simultaneously recorded signals using different methods enables the assessment of variability parameters during sleep.. Three main expectations were taken into account: The first aim was to derive a set of descriptors (features, measurements) of measured signals using relevant methods of analysis of biomedical signals. The second goal was to select a specific set of features expected to provide high efficiency of breathing disorders recognition. The third and final requisite was to develop and validate predictive statistical models, in general sensitive to signal variability.. 2.

(8) Identification of human vital functions based on selected cardiac and respiratory signals. 1.1 Structure of the thesis The first Chapter describes the aims of this thesis: to investigate, develop, and validate predictive models. Continuous ECG recording and acoustic registration of human sleep is a method to identify sleeping disorders. Changes in the patterns of these signals identify possible underlying cardiac disease. The first part of the thesis (Chapters 2 and 3) describes the methodological review and physiological background of the presented vital human functions.. The second part of the thesis, comprising Chapter 4, presents a new method of monitoring human sleep patterns at home. The following issues were considered for clinical validation and application: . concept of the ECG/acoustic based sleep analysis system,. . characterization of cardiac parameters variability,. . characterization of respiratory parameters variability,. . sleep recording and scoring at home,. . statistical analysis tools used in sleep analysis system verification.. The last part of the thesis (Chapter 5) presents experimental results with statistical data analysis and validated models.. A discussion of these investigations, conclusions and recommendations for future work are presented in Chapters 6 and 7.. 3.

(9) Identification of human vital functions based on selected cardiac and respiratory signals. 2 Methodological review 2.1 Telemedicine and remote monitoring of patients Since the middle of the last century the technological abilities of observing patients is developing rapidly. Initially, the fast-growing computer aided systems were dedicated only for selected areas of the medical industry, but technological development naturally connected together these two different worlds, creating a common branch of e-health and telemedicine [2]. The rapid development of this new industry creates many opportunities to improve biomedical technologies in everyday use. Monitoring of patients in the hospital, remote monitoring at home or monitoring the patient's diet are just a few examples among many applications. These systems are also successfully applied for the treatment of chronic diseases (such as cardiovascular diseases, asthma and diabetes) in patients, preventing a required hospital admission. One of the most important aspects of the treatment of chronic diseases is a quick medical response to the observed abnormalities of vital functions. Remote monitoring systems are allowing such direct medical responses. These systems also allow direct communication with the patient's physician for consultation at any time, potentially lowering the number of unnecessary hospital visits due to false alarms. Using elements such as wireless, very light sensors, PDAs (Personal Digital Assistant) or mobile phones, these solutions are easy to use and does not overly burden the patient. Such a system should provide access to patient data anytime, anywhere, locate the patient and automatically or semi-automatically suggest the nearest medical center, automatically recognize the patient's condition. Any classic remote monitoring system consists of at least three layers [3]: data collection, data analysis and maintenance services. The first layer is responsible for the acquisition of the necessary biosignals of patient into the system. These signals could be e.g.: heart rate, temperature, ECG, EEG, SpO2, GPS signal of patient’s position and information from motion sensors. The second layer is a classification and analysis algorithm evaluating the collected signals in such a way that the third layer could properly interpret the results and take necessary action for further patient care. In this third layer, due to the decision-making nature, medical professionals are necessary. Modern monitoring systems also utilize artificial intelligence technology, used in the diagnosis of patients [4]. Such diagnostic techniques could help doctors, by introducing a greater objectivity and save valuable analysis time, otherwise required for the medical professional to analyze the patients data. It should be mentioned that the algorithms of automatic diagnosis can only indicate the disorder, diagnostic final decision belongs to the doctors authorizing the final diagnosis.. 4.

(10) Identification of human vital functions based on selected cardiac and respiratory signals. Nevertheless, automatic diagnosis of disorders of vital signs is currently a rapidly growing field of biomedical computer science. It leads to increased confidence for diagnostic support systems. Currently, remote monitoring and automatic diagnosis are mainly focused on cardiovascular diseases, diabetes and respiratory diseases, but they are also adapted for monitoring the elderly and athletes. Another, equally important aspect of telemedicine networks in the process of treating patients is that telemedicine systems provide fast access to information, and thus can be used in the process of raising awareness and teaching of patients [5], it also could be used to update the knowledge of doctors [6].. 2.2 Monitoring of cardiovascular diseases Monitoring of heart rate is one of the most applied methods of remote observation of patients. It is also the most common type of monitoring in commercial use [7] and it is worth mentioning that the first attempts to monitor the ECG signal in daily practice has already been taken place in the early 1960's. Patient monitoring was mostly evaluated off-line (i.e. analysis was performed after completing the registration of the signal), but it allowed for continuous observation of the patient. This test is called a Holter study from the name of the inventor. A so-called Holter-tape is a continuous recording of the patients ECG over a 24 hour period. The patient carries a portable device that records the ECGsignal from several leads. This battery powered device is small in size, and overall the electrodes do not hamper the movements of the patient during normal daily activities or sleep. By applying recursive mathematical calculation methods implemented in software algorithms with 4-5 leads only attached to the patient’s body, a retrospective reconstructed full 12-lead recording can be achieved, allowing a more in depth analysis and diagnoses of potential cardiac disease. Philips has developed and commercialized such a system called EASI [8] or CardioSecure active by Personal MedSystems GmbH [9]. In-depth analysis of the ECG signals can reveal many different types of cardiac anomalies which are associated to the cause of heart rhythm disorders. For these analyses many automated computer algorithms have been developed as manual analysis of 24 hour of recordings is a tedious and timeconsuming task. Some examples of automated detected disorders are: the type of ventricular beats PVC (Premature Ventricular Contraction) [10] [11], atrial fibrillation [12], ventricles [13] or any kind of arrhythmia. To validate these new algorithms, a database of reference signals accompanied with a reference diagnosis, which in this case was expert analysis, was established [14]. 5.

(11) Identification of human vital functions based on selected cardiac and respiratory signals. With such powerful automated tools for diagnosing and developing solutions for the transmission of information, it was easy to extend existing, standard cardiovascular monitoring systems to fullfledged e-health systems. Basic monitoring systems of the ECG signal [15] are extended to monitor oxygen saturation (SpO2), blood pressure and body temperature [16]. These systems are used in studies of Holter with a quick response (called PMA - Personal Medical Assistant) [17] and for hospital and ambulatory monitoring [18].. 2.3 Identification of selected human vital function – breathing disorders The genesis of breathing disorders during sleep is connected with the transition from light to deep sleep, the soft palate muscles and tongue relaxation. If the tissues in the throat, such as the tongue, relax too much, vibrations appear and create a sound commonly known as snoring. The more narrow the airway, the greater the force of airflow, contributing to increasing tissue vibrations. Figure 1 shows tongue position in various cases.. Figure 1 Tongue positions during sleep [19].. A few basic definitions of sleep respiratory parameters are listed below [20]: . Hypopnea – is a 10 second during continued breathing event but in which ventilation is reduced by at least 50% from normal.. 6.

(12) Identification of human vital functions based on selected cardiac and respiratory signals. . Arousal – sudden sleep state transition to lighter sleep stages or wakefulness, which terminates the apnea or hypopnea.. . Apnea – is a total cessation of airflow for at least 10 seconds.. . Apnea Index (AI) is defined as the number of apneas per hour during sleep. . Apnea/Hypopnea Index (AHI) is defined as the number of apneas and hypopneas per hour during sleep.. According to the literature [21], sleep apneas can be divided into three types: a) Obstructive – defined as cessation of airflow but with continued respiratory effort. Although the diaphragm and intercostal muscle activity continued. It is thought to be an obstruction at the URT of the patient. b) Central – defined as a state in which airflow and respiratory effort are both absent. Central apnea usually grows by the corruption of the central regulation of respiration. c) Mixed – defined by a lack of respiratory effort during initial apnea period followed by gradually increasing effort against an obstructed upper airway. It is the stage starting with central apnea and continuing the absence of air flow when the respiratory begins.. Medical consequences of sleep apnea are listed as below, although in some cases, precise data are lacking, but experts have at least the impression that obstructive sleep apnea (OSA) occurs more commonly in persons with the following [22]: 1) Cardiovascular consequences of OSA . hypertension (high blood pressure),. . heart failure add,. . atherosclerosis (heart attacks, angina),. . atherosclerosis (stroke),. . atrial fibrillation,. . ventricular arrhythmias,. . pulmonary hypertension.. 2) Other consequences of OSA . trauma (traffic accidents),. . glaucoma,. . snoring spouse syndrome,. . diminished libido,. 7.

(13) Identification of human vital functions based on selected cardiac and respiratory signals. . in children: illness like attention deficit hyperactivity disorder (ADHD),. . in children: slowed growth.. 3) Other associations with OSA . obesity,. . obesity syndromes, such as Prader-Willi syndrome,. . polycystic ovary disease,. . renal failure,. . hypothyroidism,. . Marfan syndrome,. . Charcot-Marie-Tooth disease,. . post-polio syndrome,. . gastro-esophageal reflux,. . worsening of epilepsy.. Sleep disorders e.g. snoring, wheezing or sleep apnea are widespread among people of all ages, although prevalent amidst the male population, obese people with a high value of BMI, people who suffered a stroke, have hypertension or other heart diseases. The symptoms of sleep apnea sometimes appear unnoticeable, that the person affected by sleep disorders may not note this serious problem. Sleepiness and sleep disorders occur because the brain wakes up when significant drops in airflow occur. As a result, the brain does not have long, uninterrupted periods of sleep, which are very important for a healthy lifestyle. Sleep is said to be "fragmented." Most patients with sleep apnea fall asleep quickly. Some, however, may complain of insomnia. It has to be mentioned that snoring is a common sign of obstructive sleep apnea. Figure 2 presents the cycle of Obstructive Sleep Apnea.. 8.

(14) Identification of human vital functions based on selected cardiac and respiratory signals. Figure 2 Cycle of Obstructive Sleep Apnea [19].. It is often possible to hear other abnormalities in the breathing sounds made by a person with sleep disorders and pauses in breathing sounds may be heard. Patients with obstructive sleep apnea usually make noises during the sleep. They may also make sounds with an almost explosive character, as their airway suddenly re-opens after being completely obstructed. Disseminating the diagnosis of sleep is needed in order to ensure a good night’s sleep for patients suffering from sleep disorders. Sleep monitoring is also very important in case of emergencies. List of disorders, which are accruing during sleep can be divided in two classes parasomnia and dissomnia (Table 1).. Table 1 Type of sleep disorders [23].. Sleep Disorders Dissomnias. Parasomnias. Insomnias. Dream Disorders. Hypersomnias. Somnambulism. Sleep Disorders in Sleep – Awake Cycle Sleep Disorders in Respiration. In 1973 Sleep Apnea Syndrome was defined as a separate disease by Guilleminault [24]. Later it was renamed as “Sleep Apnea-Hypopnea Syndrome” in 1988 because of identification of hypopneas in polysomnography methods [25]. American Sleep Disorder Association (ASDA) has defined obstructive sleep apnea syndrome as “A syndrome characterized by recurrent obstructions in upper respiratory tract (URT) during sleep and seen often with a decrease in oxygen saturation”. The 9.

(15) Identification of human vital functions based on selected cardiac and respiratory signals. definition of sleep apnea is a 10 seconds interruption of the patients breathing pattern. The lack of breath can last even longer and usually happens repeatedly during the night. In each period of apnea, the brain receives alarm signals and interrupts the sleep to gasp for air. Table 2 lists the possible obstructive sleep apnea symptoms.. Table 2 Possible OSA symptoms [26].. During night. During day. . Loud, irregular snoring. . A feeling of lack of sleep. . Excessive movement during sleep. . Fatigue upon awakening, poor. . Sweating. . Frequent awakenings. . Restless sleep. . Nightmares. concentration . Drying of the mucous membranes of the mouth pharynx and sore throat. . Head pain. . Excessive sleepiness during the day. . Cognitive impairment. . Excessive irritability. . Tendency to depressive reactions. Hypertension and heart failure are strongly correlated with sleep apnea. Obstructive Sleep Apnea (OSA) appears to be a cause of hypertension. Sleep apnea can be either a cause or a symptom of heart failure. Recent trials have demonstrated that treating sleep apnea in patients with heart failure is beneficial. To help physicians in this diagnostic process, many studies on the topic of the detection and quantitative characteristics of sleep were performed. In this study physiological signals of the patients were recorded simultaneously with the use of basic devices: a microphone and an ECG Holter system using a multi-channel data acquisition system. Upper Airway Resistance Syndrome (UARS) presents symptoms similar to that found in OSA. Upper airway resistance syndrome was a term first applied to patients who were identified having excessive daytime sleepiness without a clear cause [27]. These patients often showed to have idiopathic hypersomnia [26]. Due to the resistance of the airflow in the upper airway, arousals could occur during the night. Snoring has been observed in association with these brief arousals, but is not necessarily identifying UARS. UARS events are noted to be typically brief events, e.g. 1 to 3 breaths in duration. These. 10.

(16) Identification of human vital functions based on selected cardiac and respiratory signals. events have been termed respiratory effort-related arousals (RERAs) and must be periods of 10 seconds or even longer. Obstructive sleep apnea-hypopnea syndrome (OSAHS) is characterized by recurrent episodes of partial or complete airway obstruction due to repetitive blockage of the upper airway. This complete airway obstruction, or partial, manifests itself in: a) a reduction in airflow (termed hypopnea), b) a complete cessation of airflow (termed apnea). An obstructive sleep apnea-hypopnea syndrome, caused by a possible sleep-disordered breathing problem, is ruled out when the patient has at least 5 obstructed breathing events per hour or 30 events during a 6 hours period of sleep during the entire night. These events can be a combination of obstructive apnea and hypopnea and additional inclusion of respiratory effort-related arousals. Changes in the configuration and properties of the upper airway that occur during sleep are causing snoring sounds. Snoring is usually an inspiratory sound, but it can also occur during expiration [28]. It could occur during any stage of sleep but usually during stages 2, 3, and 4. This is because the upper airway walls are deforming during rapid eye movement (REM) and non-REM sleep [29].. 2.4 Specific methods for identification of sleep disorders Polysomnography (PSG) is a standard method for diagnosis of sleep disorders, but requires a fullnight hospital stay in a specifically equipped bedroom. Patients physiological signals are recorded by attaching over 15 sensor channels. PSG is an expensive examination and thus not suitable for large scale application to screen large populations, also because of limited PSG facilities around the world. Therefore a clinical need for a reliable diagnosis/screening of OSA at home is present.. One of the outcomes of PSG is the apnea/hypopnea index (AHI) [30]. Apnea-Hypopnea index AHI is used to quantify sleep-hypopnea syndrome, which is defined by the number of apneas and hypopneas per hour during sleep. AHI values are typically categorized as: . 5–15/h = mild,. . 15–30/h = moderate,. . > 30/h = severe.. Obstructive Sleep Apnea is characterized when AHI > 5 during sleep. Obstructive Sleep Apnea Syndrome is defined when AHI>5 and symptoms such as snoring, witnessed apnea, excessive daytime sleepiness or drowsiness are met together [31] [32] [33]. 11.

(17) Identification of human vital functions based on selected cardiac and respiratory signals. It was noticed that snoring is mostly accompanied by Obstructive Sleep Apnea (OSA) and is recognized as an early symptom [34] [35] [36]. Snoring should provide us with the earliest opportunity to detect possible cardiovascular diseases. The major advantage of detecting snoring abnormalities is the application of low-cost non-contact equipment (e.g. in-air microphones). However, quantitative analysis of snoring sounds is currently not a common practice for OSA detection [36]. Therefore, the potential of snoring analysis in the diagnosis of OSA is still not fully exploited. Several studies were dedicated to analyze snoring sounds with and without OSA [37] [38] [39] [40] [41] [42] [43]. These studies showed significant differences in the snoring sound power spectrum, intensity pitch, pitch-jitter, pitch jump probability, formant structure and bispectrum between patients with and without apnea [40] [39] [41] [37] [43] [44] [45] [46]. These studies have provided information that could be used in the detection of OSA. However, none of these methods have been clinically adopted for automated analysis of snoring sounds in routine screening for OSA yet. One of the most fundamental issue of all automated snore analysis techniques is to separate the snore related sound. Snoring-related sounds are divided into three main classes [47]: . snoring (voiced non-silence),. . breathing (unvoiced non-silence),. . silence.. Figure 3 shows how snore related sounds segments belonging to these classes: silence (S), unvoiced non-silence (UVNS) and voiced non-silence (VNS). However, snore related sounds (SRS) data could contain also other sounds such as speech, wheezing and/or crackles. It is appropriate to classify the raw SRS data into silence (S), unvoiced non-silence (UVNS) and voiced non-silence (VNS).. 12.

(18) Identification of human vital functions based on selected cardiac and respiratory signals. Figure 3 A snoring episode, illustrating the concepts of voiced-snore, unvoiced-snore and silence segments.. Several methods of snore segmentation and classification have been reported [48]. The method presented by these authors used Mel-frequency-spectral coefficients (MFCCs). These features were applied in a hidden Markov model (HMM) based calcification (82–89% accuracy in identifying snores recordings). Another study was able to classify snores with an accuracy ranging from 86.8% to 97.3%. However this study has not taken into account the Signal Noise Ratio of the signals on segmentation [49]. Patient with OSA and other breathing disturbances can be diagnosed using ECG analysis as well. To monitor the patients vital functions can be performed e automatically and inexpensively using information derived from ECG recordings acquired at the patient’s home. It has been shown that the changes of the time intervals between heart beats (HRV), extracted from the ECG, are physiologically related to sleep apnea events [50]. During an apnea event and breathing cessation, the vagus activity increases causing the heart to slow down (bradycardia), followed by a period of arousal and an increase of the heart rate (tachycardia). In addition from an ECG recording also the patients respiratory signals can be derived referred to as ECG-derived respiration (EDR).. 13.

(19) Identification of human vital functions based on selected cardiac and respiratory signals. During the breathing cycle, the positions of the ECG electrodes on the chest surface move relative to the heart and this effect is seen as a slow modulation of the ECG amplitude at the same frequency as that of the breathing cycle. Previous studies showed that features based on HRV can be useful to detect periods of sleep apnea. Combination of features derived from the HRV and the EDR signals provides good classification results for sleep apnea detection [51]. Several methods have been proposed for OSA syndrome detection by using ECG used features based on heartbeat interval (R-R interval) and an EDR signal [52] [53] [54]. A system for predicting OSA syndrome was presented, based on time–frequency analysis of HRV with wavelet decomposition and CART (classification and regression trees [54]). Another report proposed an automated model for recognition of OSA syndrome using features extracted from wavelet decomposition of HRV and EDR signals was based on vector machine (SVM) classifier [55]. Mendez et al. have developed an autoregressive method model to screen sleep apneas from single ECG lead. Their methodology was based on RR intervals and the area under the QRS complex. But according to one group, identification of sleep apnea can be achieved using variations on heart rate variability (HRV) signal [56]. These studies were the motivation for this current work by re-emphasizing the importance of accurate automatic classification of snore-related-sounds for both clinical and research problems using two methods: 1) acoustic and 2) ECG signal analysis.. 14.

(20) Identification of human vital functions based on selected cardiac and respiratory signals. 3 Physiological background 3.1 Anatomy and physiology of the heart The heart is a pump made up of muscle tissue. Pumping action is controlled by an electrical conduction system which coordinates the contraction sequence of the four chambers of the heart (Figure 4). Heart contraction is initiated by a fast electrical depolarization (activation) of the membranes of the cardiac cells. After the depolarization phase, the myocardial cells start to repolarize (recover). Currents generated by electrical processes within the heart, are conducted through various body tissues and as a result these currents generate potential differences on the body surface. Recordings of these potential differences are called electrocardiograms or ECGs, a major clinical tool in assessing the (dis)functioning of the heart [57] [58].. Figure 4 Four chambers of the heart [59].. In normal conditions of the heart, the sinus node generates an electrical stimulus regularly at a pace of 60 to 100 beats per minute. This electrical stimulus travels down through the conduction pathways, where the right and left atria are stimulated first and contract a short period of time (150 ms) prior to the ventricles. The impulse generated by the sinoatrial node (SA) is propagated to the myocardium. Next, the electrical stimulus transmits throughout the atria to achieve the atrioventricular node (AV). The AV node functions as a critical delay of the conduction system and forms much of the PR segment (the isoelectric tracing that follows the P wave and ends with the deflection of the Q wave). The distal portion of the AV node is known as the Bundle of His, which splits into two branches right and left in the interventricular septum. If electrical conductivity 15.

(21) Identification of human vital functions based on selected cardiac and respiratory signals. is impaired a cardiac conduction abnormality can be observed. The electrical activity of the heart (depolarization and repolarization) can be measured using electrodes placed on the body surface, the method referred to as electrocardiography or ECG. The ECG signal is a time-varying voltage, generated by the stimulatory and conductive system of the heart. Like all such phenomena, the ECG can be described using a mathematical apparatus, for example by specifying time-dependent functions. The ECG signal carries information about the nature of heart activity over a period of time (Table 3). This signal is acquired with electrodes attached to the chest and limbs. Automatic analysis of the ECG is crucial in diagnostics of cardiovascular diseases, where it helps in detection of signal disturbances. The recording speed is standardized and it is possible to calculate the heart rate from the intervals between different subsequent R waves (a "normal" typical ECG is shown on Figure 5). Typically, the paper speed is set at 25 mm/sec and the voltages are calibrated so that 1 mV corresponds to 10 mm in the vertical direction. Figure 5 presents an ECG recording and a scheme of where the ECG signal is generated within the heart.. Figure 5 Generation of ECG signal and Electrocardiogram [60].. 16.

(22) Identification of human vital functions based on selected cardiac and respiratory signals. Table 3 Description of waves, intervals and segments of ECG [61].. RR interval P wave. PR interval. PR segment. QRS complex J-point ST segment T wave. ST interval QT interval. U wave. J wave. Description The interval between an R wave and the next R wave; normal resting heart rate is between 60 and 100 bpm. During normal atrial depolarization, the main electrical vector is directed from the SA node towards the AV node and spreads from the right atrium to the left atrium. This turns into the P wave on the ECG. The PR interval is measured from the beginning of the P wave to the beginning of the QRS complex. The PR interval reflects the time the electrical impulse takes to travel from the sinus node through the AV node and entering the ventricles. The PR interval is, therefore, a good estimate of AV node function. The PR segment connects the P wave and the QRS complex. The impulse vector is from the AV node to the bundle of His to the bundle branches and then to the Purkinje fibers. This electrical activity does not produce a contraction directly and is merely traveling down towards the ventricles, and this shows up flat on the ECG. The PR interval is more clinically relevant. The QRS complex reflects the rapid depolarization of the right and left ventricles. The ventricles have a large muscle mass compared to the atria, so the QRS complex usually has a much larger amplitude than the P-wave. The point at which the QRS complex finishes and the ST segment begins. It is used to measure the degree of ST elevation or depression present. The ST segment connects the QRS complex and the T wave. The ST segment represents the period when the ventricles are depolarized. It is isoelectric. The T wave represents the repolarization (or recovery) of the ventricles. The interval from the beginning of the QRS complex to the apex of the T wave is referred to as the absolute refractory period. The last half of the T wave is referred to as the relative refractory period (or vulnerable period). The ST interval is measured from the J point to the end of the T wave.. Duration 60 to 120 ms. The QT interval is measured from the beginning of the QRS complex to the end of the T wave. A prolonged QT interval is a risk factor for ventricular tachyarrhythmias and sudden death. It varies with heart rate and, for clinical relevance, requires a correction for this, giving the QTc. The U wave is hypothesized to be caused by the repolarization of the interventricular septum. It normally has a low amplitude, and even more often is completely absent. It always follows the T wave, and also follows the same direction in amplitude. If it is too prominent, suspect hypokalemia, hypercalcemia or hyperthyroidism. The J wave, elevated J-point or Osborn wave appears as a late delta wave following the QRS or as a small secondary R wave. It is considered pathognomonic of hypothermia or hypercalcemia.. Up to 420 ms in heart rate of 60 bpm. 17. 80ms. 120 to 200 ms. 50 to 120 ms. 80 to 120 ms. N/A 80 to 120 ms 160 ms. 320 ms.

(23) Identification of human vital functions based on selected cardiac and respiratory signals. The Holter ECG method is the most common for monitoring long-term heart activity. In such a case, a portable device continuously monitors various electrical activity of the cardiovascular system. Its extended recording period is useful for observing occasional cardiac arrhythmias or other abnormalities connected with the cardiovascular system. This method helped to find sleep disorders and its correlation with other biomedical signals during entire night sleep in study subjects. Figure 6 shows an example of a short period ECG record taken during the night, using a Holter apparatus.. Figure 6 ECG signal recorded during the night.. Electrocardiography is a widely used technique to diagnose cardiovascular pathologies. The cardiovascular system has an essential role in the body function. The need for automated support to analyze the ECG using computer methods is required. Unfortunately, cardiovascular pathologies e.g. ischemia or infarct, often are not easily identifiable because of artifacts present in the measurement. Thus it is important to aggregate both physicians’ knowledge and programming methods for automatic analysis of ECG signals. In this area, the search for new diagnostic indicators and the development of risk assessment methods is particularly important.. 3.2 Anatomy and physiology of the vocal tract To understand sound abnormalities during sleep it is very important to know how normal voice is being produced. Creation of voice is a complicated process, which consists of many stages. Also many organs of articulation take part in signal creation, although this is a natural and automatic process requiring no effort from a healthy person. Articulation process starts from the delivery of air through the lungs, which are included in the vocal tract. Bronchi and trachea lead the stream of air into the larynx where the vocal cord vibrations give rise to sound. Subsequently the cavities modulate sound [62]. The general structure of the voice tract is shown in Figure 7.. 18.

(24) Identification of human vital functions based on selected cardiac and respiratory signals. Figure 7 The voice tract [63].. Organs below the vocal folds: lungs, trachea, bronchial tree and part of the larynx, are not directly involved in the formation of the generated sound and their role is a source of energy [62], [64]. The movements of various organs of vocal tract shape the final image of the spectrum of the voice signal [65]. Schematic formation of the acoustic signal in the organs of voice articulation is shown in Figure 8. The flow of air through the larynx, where it comes to boosting vibration of the vocal folds, give rise to the fundamental tone.. Figure 8 Schematic formation of the acoustic signal in the organ of speech articulation [62].. The larynx acts as a sound generator. Here the fundamental tone is generated during articulation changes of its frequency. Shape the intonation of voice is dependent on individual characteristics 19.

(25) Identification of human vital functions based on selected cardiac and respiratory signals. such as gender, age and emotional state. The vibrations of the vocal folds are held without further participation of the nervous system [62], [64], [65]. Effectors, like muscles, ligaments vocal cords and smooth muscles under the influence of nerve impulses, control, coordinate and synchronize the movements of articulation, influencing the generation of voice vibrations [62]. Voice Mechanism is composed of three subsystems, each having a specific role in voice production (Table 4). Table 4 Three systems of voice mechanism [66].. Subsystem Air pressure The ability to produce voice with system airflow from the lungs, which is coordinated by the action of the diaphragm as well as abdominal and chest muscles.. Voice organs. Role in sound production. Diaphragm, chest Provides and regulates air muscles, ribs, pressure to cause vibrations abdominal of vocal folds muscles, lungs. Vibratory system. The larynx and vocal folds Voice box (larynx), Vocal folds vibrate, changing comprise the vibratory system Vocal folds air pressure to sound waves of the voice mechanism. producing "voiced sound," frequently described as a "buzzy sound", Varies pitch of sound. Resonating System. The vocal tract is comprised of resonators which give a personal features to the voice/sound, and the modifiers or articulators which form sound into voiced sounds.. Vocal tract: throat Changes the "buzzy sound" (pharynx), oral into a person's recognizable cavity, nasal voice or other sounds like cavities snoring. 20.

(26) Identification of human vital functions based on selected cardiac and respiratory signals. 4 New method of monitoring of human sleep at home 4.1 Idea of the ECG/acoustic based sleep analysis system Review of the methodology carried out in Chapter 3 has led to the original concept of the infrastructure of the home-care system for sleep identification and sleep event scoring [67], [68]. Specific system was proposed by the Author of a commercially available computer and peripherals. Figure 9 shows a diagram overlying the idea of presented method of sleep disorders identification.. Figure 9 Block diagram of the equipment used in the proposed home care sleep scoring system.. The following Chapter 4 has been logically divided into sections corresponding to the diagram presented in Figure 10. These sections are intended to follow each step of the development process, beginning at the concept stage and documenting the software development, experiment planning and performing until the final results was ready. The beginning stage of the experiment design process corresponds to a specific requirement and the initial idea of an acceptable solution. The measurement devices having direct electrical contact to the subject were isolated from other, mains-operated, system components.. The measurement of time-domain variability of the sinus node requires the acquisition of a singlelead electrocardiogram. The missing RR intervals were interpolated by a successive procedure minimizing their short-term variability, accordingly to the guidelines listed in [69]. Linear regression on preceding/successive beats on the HRV signals or on its autocorrelation function has reduced this error. The time-domain statistical parameters of successive NN intervals (normal-to-normal RR intervals) over one, two and half, five and 10 minutes periods were calculated from basic timedomain heart rate variability (HRV) parameters as representing short-time and long-time variability respectively. 21.

(27) Identification of human vital functions based on selected cardiac and respiratory signals. Acquisition of ECG signal. Acquisition of acoustic signal. Automatic processing of ECG signal. Automatic processing of acoustic signal. Data from analysis. Qualitative and Quanitative analysis of ECG signal. Qualitative and Quanitative analysis of acoustic signal. Database. Automatic classifier. Figure 10 Design and release of the home care sleep scoring system.. Measurement of the acoustic signal was performed using a microphone to record the signal in Cool Edit Pro with 44100 Hz sampling rate. The set of obtained acoustic parameters were based on time, frequency and Mel-frequency cepstral coefficients analysis.. The variety of data provided by sensors in a multimodal system requires the appropriate software design including: • sensor-specific software, translating the acquired signal into a mode specific message of unified format, • system-specific data structures and exchange rules, considering the informative properties of particular sources, • data mining and/or artificial intelligence based decision methods.. Since the contribution of specific parameters were not known and significant inter-subject variability was expected, a decision system allowing adaptation for subject and environment conditions had to be designed for this study. Such system had been trained in its desirable functionality prior to be. 22.

(28) Identification of human vital functions based on selected cardiac and respiratory signals. used for sleep evaluation. Thus, it seems reasonable to use a decision system, e.g. based on Data Mining or Artificial Neural Networks allowing the adaptation for subject and environment conditions. Acoustic diagnosis of sleep disorders, especially the Obstructive Sleep Apnea Syndrome, based on proposed method using two complementary modalities (acoustic and ECG signals collected simultaneously during sleep) may lead to specify system scoring of breathing disorder during sleep. The integration of these information related to the laboratory sleep scoring results made by the subject and/or by the artificial intelligence-supported sleep evaluation system may help find a way of snoring rehabilitation and making decisions concerning future treatment and have influence on the quality of night sleep. The measurement of breathing through simultaneously acquired acoustic and ECG signals has been used to quantify the respiratory obstruction during sleep [70], [71]. The information collected by synchronized recording of acoustic effects and the ECG signal partly overlaps, giving an opportunity to improve accuracy of the measurement. Vital signs, within the meaning of presented dissertation, are measurements of the body's basic functions related to cardiovascular and respiratory systems. The vital signs monitored during this study include the following: • ECG signal, • acoustic signal. Breathing sounds and ECGs of 15 study subjects with different degrees of obstructive sleep disorders were captured and analyzed. The sounds were recorded by a microphone placed over the mouth, simultaneously with ECG device placed on thorax over the entire night. There is freely available extensive database PhysioNet [14], but it does not include databases associated with audio signals registered during sleep in correlation with the ECG signal. The Author has established a final database of normal-disorder-related breathing sounds (different level of obstruction) with corresponding ECG signal. Database contains approximately 650 normal and disordered breathing short recordings (sound strips). Duration of recording – one breath episode. The simultaneous measurement of presented vital signs was used to quantify the respiratory obstruction during sleep. To acquire these data the following equipment was used:  Holter Recorder AsPEKT 702, Aspel - Electrocardiographic recording – 3-channel, battery operated personal recorder with 12-bit 128 Hz is designed for long-record-term ECG data (Figure 11 Aspect 702 ).. 23.

(29) Identification of human vital functions based on selected cardiac and respiratory signals. Figure 11 Aspect 702 [72]..  Small microphone - attached to the subject’s chin, measured acoustic effects with 44100 Hz sampling in Cool Edit Pro software. The microphone was hung in front of the patient’s mouth at a distance of about 5cm. The signal was recorded and sent through the analog-to-digital converter directly to the computer system and subsequent analysis was performed.. Figure 12 Personal microphone [73].. 4.1.1. Acquisition of the ECG signal. The heart rate (HR) was derived from the ECG, by an algorithm detecting the single heart beats. The heart beat is represented in the ECG by a QRS complex. The inverse of the time difference between the so-called normal heart beats (QRS complexes resulting from sinus node depolarization) gives the heart rate. The heart rate is then sampled between consecutive intervals (NN intervals). Figure 13 shows the layout of leads (electrode position type XYZ) used during ECG data acquisition:. 24.

(30) Identification of human vital functions based on selected cardiac and respiratory signals. . Chanel 1 – axis X 1 (-) white electrode 2 (+) red electrode. . Chanel 2 – axis Y 3 (-) black electrode 4 (+) brown electrode. . Chanel 3 – axis Z 5 (-) yellow electrode 6 (+) blue electrode. . neutral electrode 7 green electrode. Figure 13 Electrode positions of ECG system Aspect 702 [74].. 4.1.2. Acquisition of the acoustic signal. The first necessary step was to register the signal representing waveforms of acoustic pressure. Air vibrations correspond to changes in voltage at the output of the microphone. Then the signal was input to the A/D converter. Before saving the analog audio signal in digital form, anti-aliasing filtering was performed. All three systems responsible for these processes, i.e. a pre-amplifier, an anti-aliasing filter and analog-to-digital converter are elements of standard computer sound card [75], [76]. Due to the availability of good equipment to record sound with particular characteristics, it can be assumed that the registration of acoustic signals during sleep uses the sampling rate of 44100 Hz. Such frequency is applied to recording music on regular CD [62].. 25.

(31) Identification of human vital functions based on selected cardiac and respiratory signals. The next step was quantization, which consists of allocating the value of the amplitude within the intervals defined by a number of bits which determines the accuracy with which the sample value was stored. The most commonly used numbers of bits are 8 or 16. For the registration of acoustic signals during sleep, 16 bits was used to obtain high-precision recordings. An important parameter in signal recording is the number of channels used. For the registration of acoustic signals one audio channel was used due to the ease of processing the signal in the following steps. Extra channels were not necessary, because a point source of sound had a welldefined position (subject’s chin). Another parameter associated with the registration of the acoustic signal was the time of recording. It should be long enough to be suitable for recognition. On the other hand, the time was kept to a minimum in order to not increase unnecessarily the amount of data for further processing (acquisition only during sleep at night). Then, this signal was split into smaller fragments (sound strips). A typical human can distinguish sounds within the range of 20 Hz to 20,000 Hz. Instruments for recording acoustic signals in 50 Hz to 20,000 Hz range are inexpensive and easily accessible. It was therefore envisaged that the registration of acoustic signals takes place in that frequency range. The last element important in digital registration of the acoustic signal is the recording format. The recorded acoustic signal was saved in the WAVE format (PCM waveform audio format called pulse-code modulation), because it is not compressed [77].. 4.2 Characterizing of cardiac parameters variability Heart rate variability (HRV) can be expressed in different ways in both time and frequency domains. In presented dissertation only time-domain parameters were used in creating statistical models to find the best breathing disorders classifier. These methods, regarded as statistical, use statistical concepts that have proven useful. Time domain transformations are straightforward to calculate and include the mean duration of normal-to-normal RR intervals in the entire recording and statistical measures of the variance between NN intervals.. 4.2.1. Commonly used HRV parameters. For heart rates or cycle intervals, longer periods, complex statistical time-domain measures can be calculated. Time domain statistical parameters were derived from analysis of the recording period during sleep for all subjects involved in presented study. All commonly used statistical variables calculated from segments of the total monitoring period are included in Table 5.. 26.

(32) Identification of human vital functions based on selected cardiac and respiratory signals. Table 5 Commonly used time-domain measures.. AVNN*. Average of all NN intervals [ms]. SDNN*. Standard deviation of all NN intervals** [ms]. SDANN. SDNNIDX. rMSSD*. pNN50*. Standard deviation of the averages of NN intervals in all 5-minute segments of a 24-hour recording [ms]. Mean of the standard deviations of NN intervals in all 5-minute segments of a 24-hour recording [ms]. Square root of the mean of the squares of differences between adjacent NN intervals [ms]. Percentage of differences between adjacent NN intervals that are greater than 50 ms; a member of the larger pNNx family [%]. * Short-term HRV statistics, ** NN intervals - normal-to-normal RR intervals. Description of used parameters in creating statistical models to find the best breathing disorders classifier [78], [79], [80]: . AVNN 𝑁. 1 𝑅̅ = ∑ 𝑅𝑅𝑖 𝑁. (1). 𝑖=1. The interval from the peak of one QRS complex to the peak of the next as shown on an electrocardiogram. It is used to assess the ventricular rate. . SDNN 𝑁. 1 ̅̅̅̅ − 𝑅𝑅)2 𝑆𝐷𝑁𝑁 = √ ∑(𝑅𝑅 𝑁−1. (2). 𝑖=1. 27.

(33) Identification of human vital functions based on selected cardiac and respiratory signals. SDNN is a standard deviation of the NN intervals, which is the square root of their variance. A variance is mathematically equivalent to the total power of spectral analysis, so it reflects all cyclic components of the variability in recorded series of NN intervals. The actual values of SDNN depend on the length of recording - the longer recording is, the higher SDNN values are. Thus, in practice it is inappropriate to compare SDNN values derived from the NN recording of different length. A 24 hours’ SDNN reflects and flows of all factors that contribute to the heart rate variability during a 24-hour recording, including the slow oscillations. . SDANN. 𝑆𝐷𝐴𝑁𝑁. (3). During the analysis of long-time ECG, mean NN interval calculated within each subsequent period, which may be the subject of analysis of variation in place within the next RR interval. The standard deviation of all RR intervals averaged in 5-minute segments of the entire recording time. This measurement estimates low-frequency variations in heart rate. . SDNNIDX. 𝑆𝐷𝐴𝑁𝑁𝑖𝑛𝑑𝑒𝑥. (4). SDANN Index is the mean of the standard of all the normal RR intervals of the standard deviations of all the normal RR intervals for each 5-minute segment of a 24-hour ECG recording. The Index is believed to be a measure primarily of autonomic influence on heart rate variability. . RMSSD 𝑁−1. 1 𝑅𝑀𝑆𝑆𝐷 = √ ∑(𝑅𝑅𝑖+1 −𝑅𝑅𝑖 )2 𝑁−1. (5). 𝑖=1. In each ECG with a duration exceeding 10 seconds the average value of the difference between RR intervals is non-zero only within the specified phase of breathing. While running the same speed of adaptation heart rhythm in the case of abrupt changes body burden was introduced a measure of the mean difference successive intervals RMSSD. RMS-SD is the square root of the mean squared differences of successive NN intervals. This measure estimates high-frequency variations in heart rate in short term NN recordings that reflects an estimate of parasympathetic regulation of the heart. . pNN50 𝑁−1. 𝑁𝑁50 = ∑ 𝑓𝑖 𝑤ℎ𝑒𝑟𝑒 𝑓𝑖 = { 𝑖=1. 1 𝑤ℎ𝑒𝑛 |𝑅𝑅𝑖+1 − 𝑅𝑅𝑖 | > 50 0 𝑤ℎ𝑒𝑛 |𝑅𝑅𝑖+1 − 𝑅𝑅𝑖 | ≤ 50. 28. (6).

(34) Identification of human vital functions based on selected cardiac and respiratory signals. NN50 Number of normal-to-normal RR intervals, differing by more than 50 ms 𝑝𝑁𝑁50 =. 𝑁𝑁50 × 100% 𝑁−1. (7). where:  N - number of samples in tachogram,  RRi - the value of the i-th RR interval. Heart rate variability (HRV) has been widely applied in basic and clinical research studies. The long term statistics of time domain parameters SDANN, SDNNIDX were calculated for shorter data lengths. For short-term data (less than 15 minutes in length), only the time domain measures of AVNN, SDNN, RMSSD and pNN50 should be used. Quantitative description of the observed variability of the series of measurement results in assignment of basic descriptive statistics, given a sequence of RR intervals or successive measured values of heart rate. Extracting a series of intervals on the basis of the ECG were done automatically and the quality of the signal were taken into account.. 4.2.2. Implementation of HRV calculation procedures. Electrocardiogram analysis begins with reading the signal. Next the signal is put into the main ECG analysis, that in the end outputs the information about heart rate variability (HRV). %Loads the ECG file stored in a text file load rr_intervals_k.txt figure %%Function hrvseq(tachogram,seqlength)returns time sequence % INPUT: tachogram - monotonic sequence of RR intervals (in % seqlength - length of the time epoch (>5min for SDindex % OUTPUT: time sequence of HRV parameters: [start_pt end_pt. %% length % % 150000: % 300000: % 600000: seqlength. of time_domain HRV parameters milliseconds) and SDANN) (in milliseconds) sdnn rmssd pNN50 sdi sdann]. of the time epochs used for analysis 2.5 min time epochs, have high time precision, but cannot provide SDindex and SDANN parameters 5 min time epochs, standard time precision, but still cannot provide SDindex and SDANN parameters 10 min time epochs, standard time precision, provides SDindex and SDANN parameters = (150000,300000,600000);. %% loops thru the seqlength time epochs lengths to generate all data for i = 0:2 y=hrvseq(rr_intervals_k, seqlength(i)); % displays HRV parameters (sequence according to function comments) % time axis scaled in epoch length units plot(y(:,3:7)); % calculation of the numer of heartbeats that occur during the epoch x=(y(:,2)-y(:,1)) fig_title=fprintf('%1.1f minute period',seqlength(i)/60000); title (fig_title) figure end. 29.

(35) Identification of human vital functions based on selected cardiac and respiratory signals. 4.2.3. Estimating the respiration from ECG variations. Based on the long-term ECG signal it is possible to derive other vital parameters. To extend ECG analysis Electrocardiogram-Derived Respiratory (EDR) method was performed. The EDR method leads to the conclusion that amplitude of R (Rampl) generates the best approximation of the respiratory signal, which is very important to control sleep disorders like sleep apnea. An important feature of Electrocardiogram-Derived Respiratory method comprises the ability to extract sighs from the ECG signal. Based on breath-to-breath (BB) intervals, composed of the respiratory signal, respiratory variability (RV) measures were defined. It is used in this research by the capacity to distinguish periods of normal breathing and snoring periods during sleep. Research on respiratory variability (RV) measures may contribute to the knowledge about the respiratory system. Several techniques can be used to calculate a respiration signal from an ECG, i.e. [81], [82], [83]: a) AMEA (Angle of Mean Cardiac Electrical Axis) Breathing causes the modulation signal on cardiac electrical axis of the heart. The breathing process also affects the occurrence of RR interval variability in cardiac signal. The described method emphasizes the size of the peak of the QRS complex, rather than their frequency. There are several variations of this method, which allows to obtain the respiratory signal by measuring the angle of the mean electrical axis of the heart. The main assumptions of the method are: . areas of the QRS complex in the ECG signal obtained from the two leads are calculated in the prescribed time window,. . subsequent strip of discrete EDR signal EDR - one strip for one cardiac cycle,. . continuous signal is obtained through the use of third-degree spline interpolation.. b) Wavelet Transform of the ECG signal Wavelet analysis is based on the decomposition of the signal and presenting it as a linear combination of basic functions. The distinguishing features of this method of signal analysis from others are: . multi-signal decomposition,. . variable time resolution and frequency,. . and possibility of using basis functions different than harmonic functions.. c) Application of adaptive filter ECG signal usually exhibits noise and classical signal filtering may change the shape of the waves. However, if it is possible to record a "copy" of noise (with a different amplitude and 30.

(36) Identification of human vital functions based on selected cardiac and respiratory signals. phase than the original signal), then it is possible to minimize its effects. Respiratory signal is changing in time, therefore, the only solution is to narrow the pass band of the filter whose center frequency is tunable (adapted) in such a way that coincides with the instantaneous frequency of the signal.. The first method was used in presented study. The ECG signal from the chest surface was disturbed by the position change of the heart versus the chest electrode system and by the impedance changes associated with inhalation and exhalation. It was also possible to observe the breathing cycle: slow oscillation frequency of QRS amplitudes correspond to the breathing cycle, thus it was possible to observe these changes in ECG [84], [85]. Amplitude method made use of the fact that the movements of the chest during respiration causes change the recording conditions of the measured ECG signal. In the inhalation phase, electrodes placed on the chest recede from the heart, thus decreasing the amplitude of the recorded signal. And in exhalation the chest drops, therefore the distance between the heart and the electrodes is reduced and the amplitude of the measured signal increases. The intervals method relied on getting a signal from changes of respiratory intervals between successive heartbeats, which were represented by the QRS complex [86]. During inhalation heart rate speeds up, thus spacing between successive QRS decreases. During exhalation on opposite situation is happening [87]. The EDR signal calculated by the detected QRS area, based on R-S amplitude, which was measured as the difference between the minimum of the S and maximum of the R waves:. 𝑎𝑚𝑝(1) = 𝑅𝑎𝑚𝑝 (𝑖) − 𝑆𝑎𝑚𝑝 (𝑖), 𝑖 = 1,2, … , 𝑛. (8). where represents the distance between peaks of subsequent QRS complexes, and was used to assess the ventricular rate. In the case of one breathing disorders i.e. central apnea there is absence of breathing movements and no change in the amplitude and HR are observed. In the case of peripheral respiratory movements RR and amplitude variability are present. As an example, RR intervals for the case of sleep apnea and normal breathing are shown in Figure 14 and Figure 15 respectively.. 31.

(37) Identification of human vital functions based on selected cardiac and respiratory signals. Figure 14 RR intervals for the case of sleep apnea [88].. Figure 15 RR intervals for the case of normal breath [88].. Based on ECG signals, EDR signal was derived and used to evaluate different breathing events during sleep. There are a number of factors carrying potentially valuable diagnostic information, which may include the following: the value of average length, standard deviation, variance of the RR interval, etc. This approach is characterized by simplicity and relatively low computational complexity, but it is also characterized by a high sensitivity to noise and artifacts present in the signal, which in turn may contribute to errors during detection.. 32.

(38) Identification of human vital functions based on selected cardiac and respiratory signals. 4.2.4. Implementation of EDR procedure. Electrocardiogram derived respiratory information is based on MATLAB code. This routine calls dependent functions to filter the ECG signal, detect the RR distances and extract the EDR information. The specific code can be found below. clear all;close all;clc % % % signal sampling frequency [Hz] samp_freq = 128; %% Loading of the sample stored in the MATLAB matrix load('recording.mat'); %Change of amplitude units into mV sig=y(1,1:3000)/100; %Signal lenght determination len = length(sig); %Adding the time axis tt = 1/samp_freq:1/samp_freq:ceil(len/samp_freq); t = tt(1:len); %% Determination and removal of the isoelectric baseline is carried out by baseline_subtract function that uses the inversed signal and its time course sig = sig'; sig = baseline_subtract(sig, t);. Detection of the QRS complex and the fundamental parameters is carried out by the RRdetect function: % INPUT: sig – vector storing the signal of ECG. % t – time vector % OUTPUT: HRVf – vector with signal tachogram – RR intervals % HRVt – vector storing Times for respective HRVf % R – Matrix storing the times (1,:) and amplitudes (2,:) of the R waves % ampRS – vector with amplitudes of R+S waves % R_ind – vector with indexes of R waves [HRVf HRVt R ampRS R_ind] = RRdetect(sig,t);. Next, parameter normalization and interpolation, HRV and RS amplitudes interpolation using the "cubic spline" method and finally, data visualization is performed. % HRVf normalization (range 0 - 1) maxHRVf = max(HRVf); minHRVf = min(HRVf); for i = 1:length(HRVf) HRVf(i) = (HRVf(i)-minHRVf)/(maxHRVf-minHRVf); end % ampRS normalization (range 0 - 1) maxampRS = max(ampRS); minampRS = min(ampRS); for i = 1:length(ampRS) ampRS(i) = (ampRS(i)-minampRS)/(maxampRS-minampRS); end % Removal of possible disturbances from the beginning and end of signal HRVf = horzcat(0.5,HRVf,0.5); HRVt = vertcat(0,HRVt,t(length(t))); %HRV and RS amplitudes interpolation hrv = interp1(HRVt,HRVf,t,'spline'); amplitude = interp1(R(1,:),ampRS,t,'spline'); % Adds the data from both interpolated parameters; the inverted EDR signal is obtained from both methods y = hrv + amplitude; % Normalization and inversion of the EDR signal to obtain the final result maxy = max(y); miny = min(y); for i = 1:length(y) y(i) = (y(i)-miny)/(maxy-miny); end y = 1-y;. 33.

(39) Identification of human vital functions based on selected cardiac and respiratory signals. 4.3 Characterizing of respiratory parameters variability The study of acoustics phenomena contains several stages: the generation, propagation and reception of mechanical waves and vibrations. Diversified vibration centered in time and space is called an acoustic wave. The sense of hearing records these waves, as the only sound experience in a specific range of frequency [89]. The Figure 16 presents the stages of an acoustic event.. Figure 16 Steps of acoustical process.. 4.3.1. Diagnostic parameters of the vocal tract. Sounds occurring during the sleep are produced in the vocal tract, similarly to speech. Thanks to that analogy, existing techniques for speech analysis have been applied to evaluate snoring sounds [90] [91], [92], [93]. Acoustic analysis is aimed to distinguish different types of sounds which can be observed e.g. snoring, wheezing, stopped breathing etc [94]. Acoustic analysis techniques give information on the mechanism, loudness, intensity and it is possible to extract relevant parameters to describe the signal [95]. Acoustic signal provides information about acoustic energy in two forms: kinetic energy (𝐸𝑘 ) and potential energy (𝐸𝑝 ). Transportation of potential energy is associated with thickening and weakening of medium, while simultaneous transport of kinetic energy concerns accompanying the propagation of oscillating motion. Acoustic impedance facilitates the description of the properties of the acoustic medium. According to the definition, the acoustic impedance is a measure of the reaction of medium to the acoustic waves [96]: 𝑍=. 𝑝 kg = 𝜌0 𝑐 [Rayl = 2 ] 𝑣 m s. (9). 34.

(40) Identification of human vital functions based on selected cardiac and respiratory signals. where: p – acoustic pressure [Pa], v - acoustic particle velocity [m/s], ρ0 – density of the medium [kg/m2s]. Acoustic impedance described by Eqn. (9), is a measure of resistance. Acoustic impedance is characterized by the medium, as well as the acoustic properties of the edges, which reflect a wave. The intensity of the wave is oriented perpendicularly to the direction of propagation [97]. The instantaneous intensity can be presented depending on the velocity of the particles of medium 𝑣(𝑡) and the sound pressure 𝑝(𝑡): 𝐼(𝑡) = 𝑝(𝑡)2 /𝑍. (10). 𝐼(𝑡) = 𝑣(𝑡)2 ∙ 𝑍. (11). where: I(t) - momentary intensity [W/m2], p(t) - acoustic pressure [Pa], v(t) - acoustic particle velocity [m/s], Z - acoustic impedance [Rayl]. The sound volume is its most prominent feature. The volume increases linearly with the intensity of the quiet sound. With the increase in the intensity, volume growth is weak. It turns out that the loud sounds of a double increase in the intensity does not change the impression of sound volume [97] [2], [89], [96]. The average value of sound intensity is a subjective feature of the sound intensity. The impression of the sound intensity does not change in time, the instantaneous change in the intensity of the wave I(t) are averaged on auditory pathways. The process of averaging for periodic changes in the intensity is described by the relationship [97]: 𝑁. 1 ⟨𝐼⟩ = ∑ 𝐼(𝑡𝑗 ) 𝑁. (12). 𝑗=1. where: ⟨𝐼⟩ - the average sound intensity [. 𝑊 ], 𝑚2. 𝑁 - sampling density, 𝑊. 𝐼(𝑡𝑗 ) - sound intensity at any time for 𝑗 = 𝑁 [𝑚2 ].. 35.

(41) Identification of human vital functions based on selected cardiac and respiratory signals. Physical phenomena of acoustic signal described above was the basis for the analysis and extraction of dedicated acoustic parameters. Derived values of acoustic parameters used for further analysis are described in Chapter 4.3.2.. 4.3.2. Voice recognition procedure. Two stages of data processing were performed to recognize the acoustic signal: •. creation of breathing events patterns by expert,. •. identification of these patterns.. A general framework to study the acoustic signals was proposed, as shown in Figure 17:. Figure 17 Stages of acoustic analysis. Each step is described in the text.. This plan has been prepared in analogy to the procedures used to recognize the identity of the speaker, animals and musical instruments [98], [99], [100], [101], [102], [103], [104], [105]. Filtration was performed to avoid anti-aliasing, then the acoustic signals were converted from analog to digital form and recorded in a computer [106], [107], [105], [108]. Filtering was also applied to remove unwanted noise from the signal frequency spectrum. Next step consisted of dividing the recorded acoustic signal (sound track) into smaller strips called sound strip. Then, in each strip the signal amplitude was normalized. Sequentially, each audio strip was divided into smaller segments of data called frames. Then, the signals in the frames are multiplied by a window function to reduce the "unevenness" appearing at the beginning and end. Finally the extraction of features was carried out. Its purpose was to obtain the signal characteristics that can best describe the strip. The final stage of the study was the classification of acoustic signals. Identification of pathological changes is not always possible during the first pass of audible examination of the acoustic signal recorded during night. This is because the differences between pathological and normal signals are too subtle to be 36.