{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:13:14Z","timestamp":1779383594991,"version":"3.53.1"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Left bundle branch block (LBBB) is a common disorder in the heart\u2019s electrical conduction system that leads to the ventricles\u2019 uncoordinated contraction. The complete LBBB is usually associated with underlying heart failure and other cardiac diseases. Therefore, early automated detection is vital. This work aimed to detect the LBBB through the QRS electrocardiogram (ECG) complex segments taken from the MIT-BIH arrhythmia database. The used data contain 2655 LBBB (abnormal) and 1470 normal signals (i.e., 4125 total signals). The proposed method was employed in the following steps: (i) QRS segmentation and filtration, (ii) application of the Maximal Overlapped Discrete Wavelet Transform (MODWT) on the ECG R wave, (iii) selection of the detailed coefficients of the MODWT (D2, D3, D4), kurtosis, and skewness as extracted features to be fed into the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier. The obtained results proved that the proposed method performed well based on the achieved sensitivity, specificity, and classification accuracies of 99.81%, 100%, and 99.88%, respectively (F-Score is equal to 0.9990). Our results showed that the proposed method was robust and effective and could be used in real clinical situations.<\/jats:p>","DOI":"10.3390\/computers11060093","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T10:25:12Z","timestamp":1654856712000},"page":"93","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Automated Detection of Left Bundle Branch Block from ECG Signal Utilizing the Maximal Overlap Discrete Wavelet Transform with ANFIS"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1249-009X","authenticated-orcid":false,"given":"Bassam","family":"Al-Naami","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Faculty of Engineering, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossam","family":"Fraihat","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6943-6134","authenticated-orcid":false,"given":"Hamza Abu","family":"Owida","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Al-Ahliyya Amman University, Amman 19328, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4173-708X","authenticated-orcid":false,"given":"Khalid","family":"Al-Hamad","sequence":"additional","affiliation":[{"name":"Department of Medical Engineering, Dr. Soliman Fakeeh Hospital, Jeddah 23323, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0893-138X","authenticated-orcid":false,"given":"Roberto","family":"De Fazio","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4058-4042","authenticated-orcid":false,"given":"Paolo","family":"Visconti","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2018). 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