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(1)-Hutnicza Katedra Automaty. Streszczenie rozprawy doktorskiej Automatyczne elektrokardiograficznego (EKG) przy wykorzystaniu systemu ewolucyjno-neuronowego. Badania prowadzone w ramach enia, tematyki -. rytmu. -. celu wzmocnienia charakterystycznych cech etnej transformacji Fouriera..

(2) kappa, czasu klasyfikacji, itp.) uzyskanych na podstawie. Otrzymane - skutecznego i szybkiego rozpoznawania patologii uzysk. rozpoznawania. ,. 17 klas, na poziomie: SEN = 94.62% (ERRsum = ), przy czasie. klasyfikowania jednego fragmentu. Ck = 0.8736 [s]. Na tle aktualnej.

(3) -Hutnicza Katedra Automaty. Streszczenie rozprawy doktorskiej Automatic diagnosis of myocardial dysfunction based on analysis of the electrocardiogram signal (ECG) using evolutionary-neural system The conducted research within PhD dissertation are focused on prevention of circulatory diseases. The subject matter is very important from the social point of view, the circulatory diseases are the most common cause of death in the world. The aim of the research was to create efficient algorithms that provide automatic recognition of myocardium dysfunctions (normal sinus rhythm, rhythm of the pacemaker and fifteen cardiac disorders). Algorithms were designed for their use in telemedicine and mobile devices, supporting preventive treatment of cardiovascular diseases. Main focus has been placed on the efficacy of the designed solution. However, because of the intention to implement designed algorithms in mobile devices, an additional criterion was considered, such as the analysis time duration, which should be carried out in the real time. It should be noted that currently described in the scientific literature solutions do not present a satisfying effectiveness. To design and to test the algorithms there was developed a database containing 744 fragments (10 seconds) of ECG signals from 29 patients from a public database: MIH-BIH Arrhythmia Database. The analyzed fragments of ECG signals, were derived from only one lead: MLII. In the research was applied an original methodology involving the analysis of the longer (10 seconds) fragments of the ECG signal. In order to strengthen the characteristic features of the ECG signal, spectral power density was estimated using the Welch method, and a discrete Fourier transform. In the research was conducted six experiments involving basic analysis of ECG signals, genetic optimization of parameters, genetic selection of features, and various types of.

(4) original ensemble of classifiers. On the basis of conducted experiments, the methods were analyzed and were indicated the best ones for: pre-processing the signal, normalization, feature extraction and selection (frequency components), cross-validation, classifiers (SVM, KNN, PNN, RBFNN), ensemble of classifiers and optimization of parameters. As a result of detailed research the solution path was delineated (the combination of methods) which achieved the highest sensitivity of recognition of the myocardium dysfunctions. The results were verified using the determined factors (the sum of the errors, sensitivity, kappa factor, classification time, etc.) obtained from the confusion matrix. The obtained results fully confirm the validity of the conducted research and hypotheses. The results prove the achievement of the goal. the efficiency and the fast recognition of. myocardium pathologies in a real time. The best system deep genetic ensemble of classifiers obtained sensitivity of recognition 17 classes at the level of: SEN = 94.62% (ERRsum = 40 errors on 744 classifications, accuracy: ACC = 99.37%, specificity: SPE = 99.66%), with time of classification of one of the ECG signal fragment at the level of: Ck = 0.8736 [s]. Against a backdrop of current scientific literature it is a very good result (second best), confirming the merit of further research in this field on the basis of the proposed methodology..

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