VENTRICULAR FIBRILLATION DETECTION AND OPTIMAL PARAMETER SET SELECTION BY MEANS OF DISCRIMINANT ANALYSIS

Reliable and correct external electrocardiogram (ECG) signal analysis is of crucial importance for further development of automatic external defibrillators (AED) and their use by non-specialists. We proposed and evaluate a set of ECG parameters, derived from the output signal of a band-pass digital filter and from an in-house developed wave detection method. The extracted parameters were evaluated by means of discriminant analysis. It attained specificity between 92.1% and 95.4% and sensitivity between 96.8% and 93.4% respectively for different combinations of the proposed parameters. The parameter evaluation and the detection ability assessment were performed on ECG recordings from the widely recognized databases of the American Heart Association (AHA) and Massachusetts Institute of Technology (MIT).

[1]  Daniel J. Strauss,et al.  Identification of ventricular tachycardias by means of fast wavelet analysis , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[2]  J. N. Watson,et al.  Evaluating arrhythmias in ECG signals using wavelet transforms , 2000, IEEE Engineering in Medicine and Biology Magazine.

[3]  H. Nakajima,et al.  Real-time discrimination of ventricular tachyarrhythmia with Fourier-transform neural network , 1999, IEEE Transactions on Biomedical Engineering.

[4]  A. Murray,et al.  Recognition of ventricular fibrillation using neural networks , 1994, Medical and Biological Engineering and Computing.

[5]  J. Millet-Roig,et al.  Study of frequency and time domain parameters extracted by means of wavelet transform applied to ECG to distinguish between VF and other arrhythmias , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[6]  N. V. Thakor,et al.  Ventricular fibrillation detection by a regression test on the autocorrelation function , 1987, Medical and Biological Engineering and Computing.

[7]  I. Jekova,et al.  Real time detection of ventricular fibrillation and tachycardia , 2004, Physiological measurement.

[8]  S Barro,et al.  Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. , 1989, Journal of biomedical engineering.

[9]  N. Thakor,et al.  Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm , 1990, IEEE Transactions on Biomedical Engineering.