Mohammad Reza Homaeinezhad

TL;DR: Compared with peer-reviewed studies, a new supervised noise-artifact-robust heart arrhythmia fusion classification solution that consists of structurally diverse classifiers with a new QRS complex geometrical feature extraction technique proves a marginal progress in computerized heart arrHythmia recognition technologies.

TL;DR: A robust multi-lead ECG wave detection-delineation algorithm developed in this study on the basis of discrete wavelet transform (DWT) that has considerable capability in cases of low signal-to-noise ratio, high baseline wander, and abnormal morphologies.

TL;DR: A simple, low-latency, and accurate algorithm for real-time detection of P-QRS-T waves in the electrocardiogram (ECG) signal and it will be shown that the results of the proposed method are reliable for a minimum signal quality value of 70%.

TL;DR: A new method to detect and to delineate phonocardiogram (PCG) sounds was presented and a new DS was regenerated from the signal whose S1 and S2 were eliminated to detect occasional S3 and S4 sounds.

TL;DR: In this article, a simple mathematical-statistical based metric called Multiple Higher Order Moments (MHOM) is introduced enabling the electrocardiogram (ECG) detection-delineation algorithm to yield acceptable results in the cases of ambulatory holter ECG including strong noise, motion artifacts, and severe arrhythmia(s).