Human-Centric Analytics and Systems Impacting Quality of Life: ECG Analytics and beyond

Abstract

With the proliferation of Internet of Things (IoT), sensor technologies and advancement in the analytics systems mainly in the form of deep learning algorithms, the long quest for developing human-centric applications like automated disease diagnosis, privacy-enabled analytics is becoming a reality. Knowledge-Driven Analytics and Systems Impacting Human Quality of Life (KDAH) is indeed an attractive proposition. For example, Phonocardiogram or heart sound-based detection of heart abnormality or Myocardial Infarction (MI) prediction using single lead Electrocardiogram (ECG) signals are signifying the capability of analytics algorithm to directly solve human-centric problems. We have proposed privacy-preserved analytics with phonocardiogram-based heart condition detection, established benchmark performance of MI detection using single lead ECG signals as well as demonstrated deep residual learning based algorithm to help carbon footprint management.