An arrhythmia is defined as the irregular heartbeat pattern in human cardiovascular system. This system gets weaker with the age which arises the threat of heart disorders. An electrocardiogram tool is used to record the ECG signals from heart. These informative ECG signals are used to detect the arrhythmias. The wavelet bases are found to be very effective and flexible atoms in analysis of biosignals. Therefore, to get the optimal wavelet bases, we have used optimal 13/7 parametrized biorthogonal wavelet filter bank. In our work, we have decomposed the sequences into subbands using 13/7 optimal filter bank. We have computed renyi entropy, fuzzy entropy, sample entropy, norm and energy of all subbands. These are used as discriminating features. These features are fed to quadratic support vector machine (Q-SVM) for the classification of normal, atrial fibrillation, atrial flutter and ventricular fibrillation class. For validation, we have used 10-fold cross validation strategy. In our work, we have used two seconds and five seconds ECG signals. We achieved the accuracy, sensitivity and Positive Predictive Value (PPV) of 98:00%, 91:20% and 94:72% for five seconds data respectively and for the two seconds data, the accuracy, sensitivity and PPV are as 96:30%, 88:61% and 89:48% respectively.
Signals & Processing
Processing Results (Figures)
Design intervention coming soon.
Read full paper here!