Cardiac abnormalities detection from compressed ECG in wireless telemonitoring using principal components analysis (PCA)

In Wireless telecardiology applications ECG signal is compressed before transmission to support faster data delivery and reduce consumption of bandwidth. However, most of the ECG analysis and diagnosis algorithms are based on processing of the original ECG signal. Therefore, compressed ECG data needs to be decompressed first before the existing algorithms and tools can be applied to detect cardiovascular abnormalities. Decompression will cause delay on the doctor's mobile device and in wireless nodes that have the responsibilities to detect and prioritize abnormal data for faster processing. This is undesirable in body sensor networks (BSNs) as high processing involved in decompression will waste valuable energy in the resource and power constrained sensor nodes. In this paper, in order to diagnose cardiac abnormality such as Ventricular tachycardia, we applied a novel system to analyse and classify compressed ECG signal by using a PCA for feature extraction and k-mean for clustering of normal and abnormal ECG signals.