4 citations · 4 across the 3 of their papers we have counts for
5 papers
RPnet: A Deep Learning approach for robust R Peak detection in noisy ECG
Sricharan Vijayarangan, Vignesh R, Balamurali Murugesan +3
Automatic detection of R-peaks in an Electrocardiogram signal is crucial in a multitude of applications including Heart Rate Variability (HRV) analysis and Cardio Vascular Disease(…
Interpreting Deep Neural Networks for Single-Lead ECG Arrhythmia Classification
Sricharan Vijayarangan, Balamurali Murugesan, Vignesh R +3
Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients sufferi…
Deep Network for Capacitive ECG Denoising
Vignesh Ravichandran, Balamurali Murugesan, Sharath M Shankaranarayana +4
Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high c…
PPGnet: Deep Network for Device Independent Heart Rate Estimation from Photoplethysmogram
Shyam A, Vignesh Ravichandran, Preejith S. P +2
Photoplethysmogram (PPG) is increasingly used to provide monitoring of the cardiovascular system under ambulatory conditions. Wearable devices like smartwatches use PPG to allow lo…
RespNet: A deep learning model for extraction of respiration from photoplethysmogram
Vignesh Ravichandran, Balamurali Murugesan, Vaishali Balakarthikeyan +5
Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is se…