New Improved Methodology for ECG Signal Compression

Abstract

ECG of a person is being recorded for diagnosis of heart diseases, regular checkups, fitness and many other diseases also. Hence, huge amount of ECG data is being generated daily in hospitals. ECG recording and monitoring consume lots of memory space of digital computers. Data compression plays a vital role in reducing storage space and utilizing transmission bandwidth effectively. Objective of the research work is to propose a robust and effective method for ECG signal compression. This paper includes extraction of key morphological features, statistical features from decomposed signal, analysis in wavelet domain and classification of feature set. To reduce dimensionality of feature set, principle component analysis (PCA) is applied. Accuracy achieved with 15 principle components is same as pure wavelet transform with significant improvement in compression ratio (CR) by a factor of 4:1. MITBIH Arrhythmia database is used for experimentation. Accuracy obtained with K nearest neighborhood (KNN) classifier is 82.60% and sensitivity 92.30%. Research work carried out here is an improvement in the existing telemedicine technology.

New Ecg Signal Compression Model Based on Set Theory Applied to Images

“…Discrete Wavelet Transform (DWT) is a multi-resolution/multi-frequency representation [13] [14]. It makes it possible to efficiently analyze signals where phenomena of very different scales combine.…”

Electrocardiography signal compression using non-decimated stationary wavelet transform-based technique

“…Parameter extraction uses modeling and entropy coding to extract crucial data from the ECG signal to recover the characteristics of the signal. To elicit the features from the samples different techniques such as adaptive linear prediction (ALP) with Golomb-rice code encoding scheme [18], EDLZW [19], zero-order predictor [20], set partitioning in hierarchical trees (SPIHT) [21,22] and principal component analysis [23,24] were studied in the literature. A transform-domain method is the most focused and popular method among the other lossy compression technique.…”