title: ECG data compression using Hebbian neural networks.
abstract: Principal component analysis has long been used for a variety of signal processing applications, including signal compression. Neural network implementations of principal component analysis provide a means for unsupervised feature discovery and dimension reduction. In this paper, we describe a method for the compression of ECG data using principal component analysis. Hebbian neural networks were used for principal components computation. A variety of examples of normal and pathological ECGs obtained from the MIT ECG database demonstrate that the proposed method can provide compression ratio up to 30 with PRD% less than 5%.
authors.affiliation: Hyatt Corporation, Northville, MI 48167, USA.
authors.forename: E
authors.initials: E
authors.lastname: al-Hujazi
authors.forename: H
authors.initials: H
authors.lastname: al-Nashash
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000503:physiopathology / Q000628:therapy; D002037:Bundle-Branch Block / Q000503:physiopathology; D002304:Cardiac Pacing, Artificial; D003198:Computer Simulation; D004562:Electrocardiography / Q000145:classification; D006801:Humans; D007256:Information Systems; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted