Current methods in electrocardiogram characterization

• Published in: Computers in biology and medicine Vol. 48; pp. 133 - 149
• Main Authors: Martis, Roshan Joy, Acharya, U. Rajendra, Adeli, Hojjat
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.05.2014; Elsevier Limited
• Subjects: Arrhythmia; Arrhythmias, Cardiac - diagnosis; Arrhythmias, Cardiac - physiopathology; Cardiac arrhythmia; Cardiovascular disease; Cardiovascular diseases (CVD); Computer aided cardiac diagnosis (CACD); Diagnosis, Computer-Assisted - methods; Electrocardiogram; Electrocardiography; Electrocardiography - methods; Exercise; Heart attacks; Humans; Internal Medicine; Methods; Mortality; Non-linear methods; Other; Signal Processing, Computer-Assisted; Transform domain techniques; Wavelets; Wavelets; Arrhythmia; Electrocardiogram; Transform domain techniques; Computer aided cardiac diagnosis (CACD); Non-linear methods; Cardiovascular diseases (CVD)
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2014.02.012

Abstract The Electrocardiogram (ECG) is the P-QRS-T wave depicting the cardiac activity of the heart. The subtle changes in the electric potential patterns of repolarization and depolarization are indicative of the disease afflicting the patient. These clinical time domain features of the ECG waveform can be used in cardiac health diagnosis. Due to the presence of noise and minute morphological parameter values, it is very difficult to identify the ECG classes accurately by the naked eye. Various computer aided cardiac diagnosis (CACD) systems, analysis methods, challenges addressed and the future of cardiovascular disease screening are reviewed in this paper. Methods developed for time domain, frequency transform domain, and time-frequency domain analysis, such as the wavelet transform, cannot by themselves represent the inherent distinguishing features accurately. Hence, nonlinear methods which can capture the small variations in the ECG signal and provide improved accuracy in the presence of noise are discussed in greater detail in this review. A CACD system exploiting these nonlinear features can help clinicians to diagnose cardiovascular disease more accurately.