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As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life\u2010threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals\u2019 autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two\u2010event\u2010related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state\u2010of\u2010the\u2010art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross\u2010database training and testing with improved characteristics.<\/jats:p>","DOI":"10.1155\/2021\/7677568","type":"journal-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T00:35:34Z","timestamp":1640910934000},"update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-3558-423X","authenticated-orcid":false,"given":"Ahmed I.","family":"Taloba","sequence":"first","affiliation":[]},{"given":"Rayan","family":"Alanazi","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8475-9828","authenticated-orcid":false,"given":"Osama R.","family":"Shahin","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6425-7460","authenticated-orcid":false,"given":"Ahmed","family":"Elhadad","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9632-3602","authenticated-orcid":false,"given":"Amr","family":"Abozeid","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8975-6052","authenticated-orcid":false,"given":"Rasha M.","family":"Abd El-Aziz","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2018.03.057"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2019.12.001"},{"key":"e_1_2_9_3_2","unstructured":"VuksanovicB.andAlhamdiM. 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