This paper provides a comprehensive review of compressed sensing or compressive sampling (CS) in bioelectric signal compression applications. The aim is to provide a detailed analysis of the current trends in CS, focusing on the advantages and disadvantages in compressing different biosignals and its suitability for deployment in embedded hardware. Performance metrics such as percent root-mean-squared difference (PRD), signal-to-noise ratio (SNR), and power consumption are used to objectively quantify the capabilities of CS. Furthermore, CS is compared to state-of-the-art compression algorithms in compressing electrocardiogram (ECG) and electroencephalography (EEG) as examples of typical biosignals. The main technical challenges associated with CS are discussed along with the predicted future trends.

Compressed Sensing for Bioelectric Signals: A Review

The assessment of ECG signal quality after compression is an essential part of the compression process. Compression facilitates the signal archiving, speeds up signal transmission, and reduces the energy consumption. Conversely, lossy compression distorts the signals. Therefore, it is necessary to express the compression performance through both compression efficiency and signal quality. This paper provides an overview of objective algorithms for the assessment of both ECG signal quality after compression and compression efficiency. In this area, there is a lack of standardization, and there is no extensive review as such. 40 methods were tested in terms of their suitability for quality assessment. For this purpose, the whole CSE database was used. The tested signals were compressed using an algorithm based on SPIHT with varying efficiency. As a reference, compressed signals were manually assessed by two experts and classified into three quality groups. Owing to the experts’ classification, we determined corresponding ranges of selected quality evaluation methods’ values. The suitability of the methods for quality assessment was evaluated based on five criteria. For the assessment of ECG signal quality after compression, we recommend using a combination of these methods: PSim SDNN, QS, SNR1, MSE, PRDN1, MAX, STDERR, and WEDD SWT.

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A Comparative Analysis of Methods for Evaluation of ECG Signal Quality after Compression

The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person's health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.

An energy efficient compressed sensing framework for the compression of electroencephalogram signals.

A novel technique for real-time electroencephalogram (EEG) compression is proposed in this paper. This technique makes use of the redundancy between the different frequency subbands present in EEG segments of one channel. It uses discrete wavelet transform (DWT) and dynamic reference lists to compute and send the decorrelated subband coefficients. Set partitioning in hierarchical trees (SPIHT) is also used as source coder. Experimental results showed that the proposed method can not only compress EEG channels in one dimension (1-D), but also detect seizure-like activity. A diagnostics-oriented performance assessment was performed to evaluate the performance of both the compression and detection capabilities of the proposed method. In this paper, we show that the algorithm can positively detect seizure sections in the recordings at bitrates down to 2 bits per sample.

Dynamic Dictionary for Combined EEG Compression and Seizure Detection

An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression

A method of compressing electroencephalographic signals using the set partitioning in hierarchical trees (SPIHT) algorithm is described. The signals were compressed at a variety of different compression ratios (CRs), with the loss of signal integrity at each CR determined using the percentage root-mean squared difference between the reconstructed signal and the original. An analysis of the computational complexity of the SPIHT algorithm is also presented, using the Blackfin processor as an example implementation target.

Lossy compression of EEG signals using SPIHT

This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.

Noel Walsh

Papers

Lossy compression of EEG signals using SPIHT

The effect of lossy ECG compression on QRS and HRV feature extraction

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)