title: A patient-adaptive profiling scheme for ECG beat classification.
abstract: Recent trends in clinical and telemedicine applications highly demand automation in electrocardiogram (ECG) signal processing and heart beat classification. A patient-adaptive cardiac profiling scheme using repetition-detection concept is proposed in this paper. We first employ an efficient wavelet-based beat-detection mechanism to extract precise fiducial ECG points. Then, we implement a novel local ECG beat classifier to profile each patient's normal cardiac behavior. ECG morphologies vary from person to person and even for each person, it can vary over time depending on the person's physical condition and/or environment. Having such profile is essential for various diagnosis (e.g., arrhythmia) purposes. One application of such profiling scheme is to automatically raise an early warning flag for the abnormal cardiac behavior of any individual. Our extensive experimental results on the MIT-BIH arrhythmia database show that our technique can detect the beats with 99.59% accuracy and can identify abnormalities with a high classification accuracy of 97.42%.
authors.affiliation: Quality of Life Technology Laboratory, The University of Texas at Dallas, Richardson, TX 75083, USA. mxf042000@utdallas.edu
authors.forename: Miad
authors.initials: M
authors.lastname: Faezipour
authors.forename: Adnan
authors.initials: A
authors.lastname: Saeed
authors.forename: Suma Chandrika
authors.initials: SC
authors.lastname: Bulusu
authors.forename: Mehrdad
authors.initials: M
authors.lastname: Nourani
authors.forename: Hlaing
authors.initials: H
authors.lastname: Minn
authors.forename: Lakshman
authors.initials: L
authors.lastname: Tamil
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D015716:Electrocardiography, Ambulatory / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D012815:Signal Processing, Computer-Assisted