An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features

• Published in: Knowledge-based systems 2015-07, Vol.83, p.149-158
• Main Authors: Acharya, U. Rajendra, Fujita, Hamido, Sudarshan, Vidya K., Sree, Vinitha S., Eugene, Lim Wei Jie, Ghista, Dhanjoo N., Tan, Ru San
• Format: Article
• Language: English
• Subjects: Cardiac death; DWT; ECG; Nonlinear; SCD; Ventricular fibrillation
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2015.03.015

[Display omitted] •Novel Sudden Cardiac Death Index (SCDI) is proposed using ECG signals.•Nonlinear features are extracted from DWT coefficients.•SCDI is formulated using nonlinear features.•SCDI predicts accurately SCD 4min before its onset. Early prediction of person at risk of Sudden Cardiac Death (SCD) with or without the onset of Ventricular Tachycardia (VT) or Ventricular Fibrillation (VF) still remains a continuing challenge to clinicians. In this work, we have presented a novel integrated index for prediction of SCD with a high level of accuracy by using electrocardiogram (ECG) signals. To achieve this, nonlinear features (Fractal Dimension (FD), Hurst’s exponent (H), Detrended Fluctuation Analysis (DFA), Approximate Entropy (ApproxEnt), Sample Entropy (SampEnt), and Correlation Dimension (CD)) are first extracted from the second level Discrete Wavelet Transform (DWT) decomposed ECG signal. The extracted nonlinear features are ranked using t-value and then, a combination of highly ranked features are used in the formulation and employment of an integrated Sudden Cardiac Death Index (SCDI). This calculated novel SCDI can be used to accurately predict SCD (four minutes before the occurrence) by using just one numerical value four minutes before the SCD episode. Also, the nonlinear features are fed to the following classifiers: Decision Tree (DT), k-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The combination of DWT and nonlinear analysis of ECG signals is able to predict SCD with an accuracy of 92.11% (KNN), 98.68% (SVM), 93.42% (KNN) and 92.11% (SVM) for first, second, third and fourth minutes before the occurrence of SCD, respectively. The proposed SCDI will constitute a valuable tool for the medical professionals to enable them in SCD prediction.