Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare

• Autorzy: Qaisar Saeed Mian , Khan Sibghatullah I. , Dallet Dominique , Tadeusiewicz Ryszard , Pławiak Paweł

Identyfikatory

DOI

10.1016/j.bbe.2022.05.006

• Języki publikacji: EN

Abstrakty

EN

The next generation healthcare systems will be based on the cloud connected wireless biomedical wearables. The key performance indicators of such systems are the compression, computational efficiency, transmission and power effectiveness with precision. The electrocardiogram (ECG) signals processing based novel technique is presented for the diagnosis of arrhythmia. It employs a novel mix of the Level-Crossing Sampling (LCS), Enhanced Activity Selection (EAS) based QRS complex selection, multirate processing, Wavelet Decomposition (WD), Metaheuristic Optimization (MO), and machine learning. The MIT-BIH dataset is used for experimentation. Dataset contains 5 classes namely, ‘‘Atrial premature contraction”, ‘‘premature ventricular contraction”, ‘‘right bundle branch block”, ‘‘left bundle branch block” and ‘‘normal sinus”. For each class, 450 cardiac pulses are collected from 3 different subjects. The performance of Marine Predators Algorithm (MPA) and Artificial Butterfly Optimization Algorithm (ABOA) is investigated for features selection. The selected features sets are passed to classifiers that use machine learning for an automated diagnosis. The performance is tested by using multiple evaluation metrics while following the 10-fold cross validation (10-CV). The LCS and EAS results in a 4.04-times diminishing in the average count of collected samples. The multirate processing lead to a more than 7-times computational effectiveness over the conventional fix-rate counter parts. The respective dimension reduction ratios and classification accuracies, for the MPA and ABOA algorithms, are 29.59-times & 22.19-times and 98.38% & 98.86%.

Słowa kluczowe

EN

arrhythmia classification; dimension reduction; feature extraction; mobile healthcare; level crossing sampling; multirate processing; metaheuristic optimization; machine learning; QRS selection; wavelet decomposition

PL

klasyfikacja arytmii serca; redukcja wymiarów; ekstrakcja cech; mobilna opieka zdrowotna; optymalizacja metaheurystyczna; uczenie maszynowe; rozkład falkowy

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 2
• Strony: 681--694
• Opis fizyczny: Bibliogr. 53 poz., rys., tab., wykr.

Twórcy

autor

Qaisar Saeed Mian

• Dept. of Electrical and Computer Engineering, Effat University, Jeddah, Saudi Arabia

• https://orcid.org/0000-0002-4268-3482

autor

Khan Sibghatullah I.

• Department of Electronics and Communication Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, India

autor

Dallet Dominique

• University of Bordeaux, CNRS, Bordeaux INP, IMS, UMR, Talence, France

autor

Tadeusiewicz Ryszard

• Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Krakow, Poland

autor

Pławiak Paweł

• Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
• Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland

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