{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T13:23:38Z","timestamp":1777555418960,"version":"3.51.4"},"reference-count":70,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Data Science"],"published-print":{"date-parts":[[2024,6,26]]},"abstract":"<jats:p>Many approaches to computer-aided electrocardiogram (ECG) arrhythmia detection have been performed, several of which combine persistent homology and machine learning. We present a novel ECG signal processing pipeline and method of constructing predictor variables for use in statistical models. Specifically, we introduce an isoelectric baseline to yield non-trivial topological features corresponding to the P, Q, S, and T-waves (if they exist) and utilize the N-most persistent 1-dimensional homological features and their corresponding area-minimal cycle representatives to construct predictor variables derived from the persistent homology of the ECG signal for some choice of N. The binary classification of (1) Atrial Fibrillation vs. Non-Atrial Fibrillation, (2) Arrhythmia vs. Normal Sinus Rhythm, and (3) Arrhythmias with Morphological Changes vs. Sinus Rhythm with Bradycardia and Tachycardia Treated as Non-Arrhythmia was performed using Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis, Naive Bayes, Random Forest, Gradient Boosted Decision Tree, K-Nearest Neighbors, and Support Vector Machine with a linear, radial, and polynomial kernel Models with stratified 5-fold cross validation. The Gradient Boosted Decision Tree Model attained the best results with a mean F1-score and mean Accuracy of [Formula: see text], [Formula: see text], and [Formula: see text] across the five folds for binary classifications of (1), (2), and (3), respectively.<\/jats:p>","DOI":"10.3233\/ds-240061","type":"journal-article","created":{"date-parts":[[2024,6,7]],"date-time":"2024-06-07T11:23:38Z","timestamp":1717759418000},"page":"29-53","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Electrocardiogram arrhythmia detection with novel signal processing and           persistent homology-derived predictors"],"prefix":"10.1177","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6819-0045","authenticated-orcid":false,"given":"Hunter","family":"Dlugas","sequence":"first","affiliation":[{"name":"Department of Mathematics, Wayne State University,\r          MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,6,1]]},"reference":[{"key":"ref001","first-page":"218","volume":"18","author":"Adams H.","year":"2017","journal-title":"J. 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