Compression Algorithms for Biomedical SignalsGarry Higgins is a PhD student in Electrical & Electronic Engineering, in the College of Engineering and Informatics at NUI Galway. He is also a researcher in the Bioelectronics Research Cluster of the National Centre for Biomedical Engineering Science, also at NUI Galway. His current research focuses on efficient implementation of compression algorithms for biomedical signals, particularly EEG, using algorithms based on the wavelet transform-based framework contained in JPEG-2000 standard.
Compression Algorithms for Biomedical Signals
Multichannel electroencephalogram (EEG) is a tool for measuring the electrical activity of the brain, and the use of EEG to diagnose a variety of neurological conditions such as Epilepsy  and Alzheimer’s disease  has long been established. However, this often requires periods of prolonged EEG recording and monitoring. This usually involves patients spending long periods of time in medical facilities, tying up resources and clinicians.
Due to the nature of EEG, even short periods of capture can result in large amounts of data being recorded. This can prove a problem for the transmission and/or recording of this data. Minimising the size of this data provides a measurable advantage. In comparison to other methods of measuring electrical activity in the human body for physiological diagnosis, such as ECG, relatively little research has been done in the compression of EEG signals.
My work is currently examining the use of near-lossless and lossy methods for compression, utilising wavelet transform-based approaches. The method I am currently investigating is an adaptation of the JPEG2000 Part 1 image compression algorithm. The core of this method involves a number of modern compression methods such as Discrete Wavelet Transform and Adaptive Binary Arithmetic Coding. A thresholding step has been added to this to provide a trade off between achieving a higher compression ratio, and maintaining signal integrity. Figure 1 provides an overview of the compression algorithm.
Figure 2. High-Level Block diagram of ECG Processing System
The main focus of the research is concerned with the three final stages, feature extraction, classification and compression. Although these areas have been extensively researched, comparatively little work has been carried out from the point of view of low complexity and power consumption with portable devices.
The three main stages in the system require signal processing, and the methods which we hope to implement are all based on the wavelet transform. For feature extraction and classification the wavelet transform produces a time-frequency decomposition of the signal which separates individual signal components more effectively than traditional methods. Its advantages for compression methods allow for good data de-correlation to highlight the redundancy in the waveform.
At present the research has mainly focused on compression of ECG with various state of the art methods implemented. The two main methods studied were the set partitioning in hierarchical tress (SPIHT) and the embedded zero-tree wavelet (EZW) method. The SPIHT and EZW algorithms are also based around the wavelet transform and the two major strengths of these are:
1) The bit-stream is embedded, therefore the coefficients are sent in order of importance to the decoder, allowing the encoding process to be stopped at any stage.
2) It exploits the similarity of the coefficients of similar orientation between the subbands of the wavelet transform to provide greater compression.
These advantages allow variation of the compression rate of the signal depending on the final signal quality required. The graph in Figure 3 shows the performance of SPIHT in relation to compression ratio and percentage root-mean-square difference (PRD); as the compression ratio increases the PRD (signal distortion) also increases.
Figure 1. Illustrative diagram of heart beat, showing major morphological elements.
My project is concerned initially with the compression of ECG signals, and will also examine the automatic processing of ECG in order to extract information about e.g. possible heart problems. Of particular interest is efficient processing in an ambulatory environment. A high-level block diagram of the various elements is shown in Figure 2.