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Feature subset selection and classification of intracardiac electrograms during atrial fibrillation.

, , , , , and . Biomed. Signal Process. Control., (2017)

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Swallowing disorders analysis using surface EMG biomarkers and classification models., , and . Digit. Signal Process., (March 2023)Scalogram-energy based segmentation of surface electromyography signals from swallowing related muscles., , and . Comput. Methods Programs Biomed., (2020)Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography., , , , , and . Sensors, 22 (12): 4513 (2022)Electrophysiological and Mechanical Approaches to the Swallowing Analysis., , , , and . WEA, volume 1274 of Communications in Computer and Information Science, page 281-290. Springer, (2020)Fractionated electrograms and rotors detection in chronic atrial fibrillation using model-based clustering., , , , , , , , and . EMBC, page 1579-1582. IEEE, (2014)Simple Ablation Guided by ApEn Mapping in a 2D Model during Permanent Atrial Fibrillation., , , , , , , , and . CinC, page 1029-1032. www.cinc.org, (2014)Support Vector Machine and Artificial Neural Network Implementation in Embedded Systems for Real Time Arrhythmias Detection., , , , , and . BIOSIGNALS, page 310-313. SciTePress, (2013)Dominant Frequency, Regularity and Organization Indexes Response to Preprocessing Filter Variations on Simulated Electrograms During Atrial Fibrillation., , , , , and . BIOSIGNALS, page 306-309. SciTePress, (2013)Lead and Carbon Monoxide Effects on Human Atrial Action Potential. In Silico Study., , , , and . CinC, www.cinc.org, (2017)Analysis of electrophysiological and mechanical dimensions of swallowing by non-invasive biosignals., , , , and . Biomed. Signal Process. Control., (April 2023)