ECG Data Compression for Telecardiology

Abstract

The electrical signal generated by heart and acquired from the body surface is known as Electrocardiogram (ECG). It is used to know the status of heart to diagnose its malfunctioning at an early stage, so that corrective action can be taken to prevent any major non-reversible failure. For critical cardiac patients, persons under cardiac surveillance, ambulatory patients and for creation of ECG database, continuous recording of ECG is required. The recorded data becomes so voluminous that it becomes practically impossible to handle it without compression. The importance of data compression further increases by the fact that the rate at which cardiac patients are increasing all over the world, we do not have a matching number of cardiologists to provide the required healthcare, especially in remote and rural areas. One way to overcome this problem is to transmit ECG, along with other vital statistics of a patient, over internet to a cardiologist for expert advice. For one day s continuous recording, the amount of multichannel ECG data exceeds several gigabytes. Moreover if this data is to be transmitted over a telephone line or a slower digital communication network, the time of transmission goes beyond the human patience. Compressing the data is the only solution to this problem. The main goal of any compression technique is to achieve maximum data volume reduction while preserving the significant signal morphology on reconstruction. Our work starts with literature survey of the techniques used for compression of ECG signal, and identifies a wavelet compression method of Set Partitioning In Hierarchical Trees (SPIHT) as superior to any other technique reported so far. We have proposed two additional steps in the SPIHT algorithm, which are Blank-fire removal and Polishing . These additional steps increase the compression ratio and reduce the percentage root-mean-square difference, while retaining all features of the existing SPIHT algorithm.