{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:56:22Z","timestamp":1773932182368,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T00:00:00Z","timestamp":1613952000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The usage of wearable gadgets is growing in the cloud-based health monitoring systems. The signal compression, computational and power efficiencies play an imperative part in this scenario. In this context, we propose an efficient method for the diagnosis of cardiovascular diseases based on electrocardiogram (ECG) signals. The method combines multirate processing, wavelet decomposition and frequency content-based subband coefficient selection and machine learning techniques. Multirate processing and features selection is used to reduce the amount of information processed thus reducing the computational complexity of the proposed system relative to the equivalent fixed-rate solutions. Frequency content-dependent subband coefficient selection enhances the compression gain and reduces the transmission activity and computational cost of the post cloud-based classification. We have used MIT-BIH dataset for our experiments. To avoid overfitting and biasness, the performance of considered classifiers is studied by using five-fold cross validation (5CV) and a novel proposed partial blind protocol. The designed method achieves more than 12-fold computational gain while assuring an appropriate signal reconstruction. The compression gain is 13 times compared to fixed-rate counterparts and the highest classification accuracies are 97.06% and 92.08% for the 5CV and partial blind cases, respectively. Results suggest the feasibility of detecting cardiac arrhythmias using the proposed approach.<\/jats:p>","DOI":"10.3390\/s21041511","type":"journal-article","created":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T20:42:51Z","timestamp":1614026571000},"page":"1511","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Multirate Processing with Selective Subbands and Machine Learning for Efficient Arrhythmia Classification"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4268-3482","authenticated-orcid":false,"given":"Saeed","family":"Qaisar","sequence":"first","affiliation":[{"name":"College of Engineering, Effat University, Jeddah 21478, Saudi Arabia"},{"name":"Communication &amp; Signal processing Lab, Energy and Technology Centre, Effat University, Jeddah 21478, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1450-6606","authenticated-orcid":false,"given":"Alaeddine","family":"Mihoub","sequence":"additional","affiliation":[{"name":"Department of Management Information System and Production Management, College of Business and Economics, Qassim University, P.O. Box 6640, Buraidah 51452, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8873-9755","authenticated-orcid":false,"given":"Moez","family":"Krichen","sequence":"additional","affiliation":[{"name":"Faculty of CSIT, Al-Baha University, Al-Baha 65731, Saudi Arabia"},{"name":"ReDCAD Laboratory, University of Sfax, Sfax 3029, Tunisia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2026-5666","authenticated-orcid":false,"given":"Humaira","family":"Nisar","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia"},{"name":"Centre for Healthcare Science and Technology, Universiti Tunku Abdul Rahman, Kajang 43000, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e56","DOI":"10.1161\/CIR.0000000000000659","article-title":"Heart disease and stroke Statistics-2019 update a report from the American Heart Association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1109\/TIM.2013.2279001","article-title":"Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification","volume":"63","author":"Banerjee","year":"2014","journal-title":"IEEE Trans. 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