A novel algorithm for ventricular arrhythmia classification using a fuzzy logic approach

Abstract

In the present study, it has been shown that an unnecessary implantable cardioverter-defibrillator (ICD) shock is often delivered to patients with an ambiguous ECG rhythm in the overlap zone between ventricular tachycardia (VT) and ventricular fibrillation (VF); these shocks significantly increase mortality. Therefore, accurate classification of the arrhythmia into VT, organized VF (OVF) or disorganized VF (DVF) is crucial to assist ICDs to deliver appropriate therapy. A classification algorithm using a fuzzy logic classifier was developed for accurately classifying the arrhythmias into VT, OVF or DVF. Compared with other studies, our method aims to combine ten ECG detectors that are calculated in the time domain and the frequency domain in addition to different levels of complexity for detecting subtle structure differences between VT, OVF and DVF. The classification in the overlap zone between VT and VF is refined by this study to avoid ambiguous identification. The present method was trained and tested using public ECG signal databases. A two-level classification was performed to first detect VT with an accuracy of 92.6 %, and then the discrimination between OVF and DVF was detected with an accuracy of 84.5 %. The validation results indicate that the proposed method has superior performance in identifying the organization level between the three types of arrhythmias (VT, OVF and DVF) and is promising for improving the appropriate therapy choice and decreasing the possibility of sudden cardiac death.

Appendix: Fisher criterion

Fisher criterion measures the ability of the jth feature to separate between two sets of labeled data (positive and negatives instances) by computing the F-score as.

$$F(j) = \frac{{\mu (y + )^{2} + \mu (y - )^{2} }}{{\sigma^{2} (y + ) + \sigma^{2} (y - )}}$$

(14)

where μ(y±)=μ_j,±−μ_j represents the difference between the average of the jth feature for the positive/negative classes μ_j,± and the whole set of samples μ_j. In the denominator, σ2 (y ±) is the sample variance of the positives/negative instances and can be calculated as.

$$\sigma^{2} (y \pm ) = \frac{1}{{n_{ \pm } - 1}}\sum\limits_{i = 1}^{{n_{ \pm } }} {(x_{i, \pm }^{(j)} - \mu_{j, \pm } )^{2} }$$

(15)

where n_± is the number of positive/negative samples. The larger the value of F(j), the more likely this feature is discriminative.