Distinguishing between ventricular Tachycardia and Ventricular Fibrillation from compressed ECG signal in wireless Body Sensor Networks

Since ECG is huge in size sending large volume data over resource constrained wireless networks is power consuming and will reduce the energy of nodes in Body Sensor Networks (BSN). Therefore, compression of ECGs and diagnosis of diseases from compressed ECGs will play key roles in enhancing the life-time of body sensor networks. Moreover, discrimination between ventricular Tachycardia and Ventricular Fibrillation is of crucial importance to save human life. Existing algorithms work only on plain text ECGs to distinguish between the two, and therefore, not suitable in BSN. VT and VF are often similar in patterns and in filtration of noise and improper attribute selection in compressed ECGs will make it even harder to classify them properly. In this paper, a supervised attribute selection algorithm called Correlation Based Feature Selection (CFS) is used to filter the unwanted attributes and select the most relevant attributes. We then use the selected attributes to train and classify VT and VF using Radial Basis Function (RBF) Neural Network and k-nearest neighbour techniques. We experimented with 103 ECG samples taken from MIT-BIH Malignant Ventricular Ectopy Database. Results showed that accuracy can be as high as 93.3% when attribute selection is used and large number of training samples are provided.