title: Arrhythmia detection in single-lead ECG by combining beat and rhythm-level information.
abstract: In this paper, we propose a method for detecting arrhythmia in single-lead electro-cardiogram (ECG) signal. By applying a sequence of pre-processing steps (filtering, baseline correction), beat classification and rhythm identification, six different beat-types and four abnormal rhythms are detected. Beat classification uses fast Fourier transform (FFT) as the feature and a support vector machine (SVM) classifier. Subsequently rhythm identification uses a deterministic finite state machine to detect abnormal rhythms. We evaluate the performance of our technique on the MIT-BIH database, to obtain 97% beat classification accuracy and perfect rhythm identification result.
authors.forename: Vinod
authors.initials: V
authors.lastname: Pathangay
authors.forename: Satish Prasad
authors.initials: SP
authors.lastname: Rath
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology*; D004562:Electrocardiography / Q000379:methods*; D004566:Electrodes; D005583:Fourier Analysis; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted