{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:14:24Z","timestamp":1760148864448,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"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>Background: Internet-of-things technologies are reshaping healthcare applications. We take a special interest in long-term, out-of-clinic, electrocardiogram (ECG)-based heart health management and propose a machine learning framework to extract crucial patterns from noisy mobile ECG signals. Methods: A three-stage hybrid machine learning framework is proposed for estimating heart-disease-related ECG QRS duration. First, raw heartbeats are recognized from the mobile ECG using a support vector machine (SVM). Then, the QRS boundaries are located using a novel pattern recognition approach, multiview dynamic time warping (MV-DTW). To enhance robustness with motion artifacts in the signal, the MV-DTW path distance is also used to quantize heartbeat-specific distortion conditions. Finally, a regression model is trained to transform the mobile ECG QRS duration into the commonly used standard chest ECG QRS durations. Results: With the proposed framework, the performance of ECG QRS duration estimation is very encouraging, and the correlation coefficient, mean error\/standard deviation, mean absolute error, and root mean absolute error are 91.2%, 0.4 \u00b1 2.6, 1.7, and 2.6 ms, respectively, compared with the traditional chest ECG-based measurements. Conclusions: Promising experimental results are demonstrated to indicate the effectiveness of the framework. This study will greatly advance machine-learning-enabled ECG data mining towards smart medical decision support.<\/jats:p>","DOI":"10.3390\/s23125723","type":"journal-article","created":{"date-parts":[[2023,6,20]],"date-time":"2023-06-20T02:26:56Z","timestamp":1687228016000},"page":"5723","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction"],"prefix":"10.3390","volume":"23","author":[{"given":"Qingxue","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Department of Biomedical Engineering, Purdue School of Engineering and Technology, 723 W. Michigan St., Indianapolis, IN 46202, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"ref_1","unstructured":"WHO (2023, May 20). The 10 Leading Causes of Death in the World. 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