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A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation.

, , , , , and . IEEE J. Biomed. Health Informatics, 23 (5): 1980-1989 (2019)

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Risk Assessment of All-Cause Mortality in Patients with an Implantable Cardioverter Defibrillator Using a Novel QRS Fragmentation Score., , , , , and . CinC, page 1-4. www.cinc.org, (2018)Successes and challenges of artificial intelligence in cardiology., , , , and . Frontiers Digit. Health, (March 2023)Temporal Beat-to-Beat Variability of Repolarization Changes Predict Non-Sustained Ventricular Tachycardia in Ischemic Heart Disease Patients., , , , , , and . CinC, page 1-4. www.cinc.org, (2018)Automated T Wave End Detection Methods - Comparison of Four Different Methods for T Wave End Detection., , , , , and . BIOSIGNALS, page 92-98. SciTePress, (2017)A Machine-Learning Approach for Detection and Quantification of QRS Fragmentation., , , , , and . IEEE J. Biomed. Health Informatics, 23 (5): 1980-1989 (2019)Tensor-based Detection of T Wave Alternans in Multilead ECG Signals., , , , and . CinC, page 185-188. www.cinc.org, (2014)Tensor-based detection of T wave alternans using ECG., , , and . EMBC, page 6991-6994. IEEE, (2015)Phase-Rectified Signal Averaging for Automatic Detection of QRS Fragmentation., , , , and . CinC, www.cinc.org, (2016)Detection of Irregular Heartbeats Using Tensors., , , , and . CinC, page 573-576. www.cinc.org, (2015)