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Jasson, S., Medigue, C., Maison-Blanche, P., Montano, N., Meyer, L., Vermeiren, C., Mansier, P., Coumel, P., Malliani, A. and Swynghedauw, B. (1997) Instant power spectrum analysis of heart rate variability during orthostatic tilt using a time-frequency-domain method. Circulation. 96, 3521-3526.

has been cited by the following article:

• TITLE: Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals

  AUTHORS: Elias Ebrahimzadeh, Mohammad Pooyan

  KEYWORDS: Sudden Cardiac Death; Heart Rate Variability; Time-Frequency Transform; Electrocardiogram Signal; Linear Processing

  JOURNAL NAME: Journal of Biomedical Science and Engineering, Vol.4 No.11, November 17, 2011

  ABSTRACT: Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.