Signal-piloted processing metaheuristic optimization and wavelet decomposition based elucidation of arrhythmia for mobile healthcare
Authors:
- Saeed Mian Qaisar,
- Sibgatullah I. Khan,
- Dominique Dallet,
- Ryszard Tadeusiewicz,
- Paweł Pławiak
Abstract
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 counterparts. 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%.
- Record ID
- CUTb6c1dd0d3c114cf3b668ce8def9aad0a
- Publication categories
- ;
- Author
- Journal series
- Biocybernetics and Biomedical Engineering, ISSN 0208-5216
- Issue year
- 2022
- Vol
- 42
- No
- 2
- Pages
- 681-694
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 693-694; Bibliografia (liczba pozycji) - 53; Oznaczenie streszczenia - Abstr.; Data udostępnienia on-line - 2022-05-18; Numeracja w czasopiśmie - Vol. 42, Iss. 2
- Substantive notes
- The dataset is publicly available at: https://physionet.org/content/mitdb/1.0.0/
- Keywords in English
- arrhythmia classification, compression, dimension reduction, electrocardiogram (ECG), feature extraction, healthcare, level-crossing sampling, multirate processing, metaheuristic optimization, machine learning
- ASJC Classification
- DOI
- DOI:10.1016/j.bbe.2022.05.006 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0208521622000444 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 140
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 24
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUTb6c1dd0d3c114cf3b668ce8def9aad0a/
- URN
urn:pkr-prod:CUTb6c1dd0d3c114cf3b668ce8def9aad0a
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.