Lossless and near-lossless compression of ECG signals

Abstract-Physiological monitoring has been used in a wide range of scenarios to assist in disease diagnosis, athlete monitoring and other activities. There are also many opportunities in analysing aggregate data from groups of people rather than individuals such as public event monitoring or athletic team performance optimisation. Numerous difficulties exist pertaining to this, particularly concerning how to process and transform the resulting physiological data in real-time when many devices are producing data. This paper proposes a system that is designed to monitor, analyse and report physiological data in real-time by leveraging mobile devices as distributed processors.

show abstract

Wavelet-Based Lossy-to-Lossless ECG Compression in a Unified Vector Quantization Framework

Miaou

Chao

2005

IEEE Trans. Biomed. Eng.

View full text Add to dashboard Cite

In a prior work, a wavelet-based vector quantization (VQ) approach was proposed to perform lossy compression of electrocardiogram (ECG) signals. In this paper, we investigate and fix its coding inefficiency problem in lossless compression and extend it to allow both lossy and lossless compression in a unified coding framework. The well-known 9/7 filters and 5/3 integer filters are used to implement the wavelet transform (WT) for lossy and lossless compression, respectively. The codebook updating mechanism, originally designed for lossy compression, is modified to allow lossless compression as well. In addition, a new and cost-effective coding strategy is proposed to enhance the coding efficiency of set partitioning in hierarchical tree (SPIHT) at the less significant bit representation of a WT coefficient. ECG records from the MIT/BIH Arrhythmia and European ST-T Databases are selected as test data. In terms of the coding efficiency for lossless compression, experimental results show that the proposed codec improves the direct SPIHT approach and the prior work by about 33% and 26%, respectively.

show abstract

Lossless and near-lossless compression of ECG signals

Cited by 6 publications

References 9 publications

Lossless compression of electromyographic signal

Lossless compression of electromyographic signal

Towards distributed real-time physiological processing in mobile environments

Wavelet-Based Lossy-to-Lossless ECG Compression in a Unified Vector Quantization Framework

Contact Info

Product

Resources

About