Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression

Manikandan, M. Sabarimalai; Dandapat, Samarendra

doi:10.1016/j.bspc.2007.09.003

Cited by 53 publications

(34 citation statements)

References 65 publications

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“…The amount of retained energy in above threshold is decided according to the clinical importance of the subband for ECG signals [17]. The sparsity in each subband is measured as the percentage of zero's in the sparse subband vector and is given as [9]:…”

Section: B Multiscale Compressed Sensing For High Frequency Eigenspamentioning

confidence: 99%

Compressed sensing framework of data reduction at multiscale level for eigenspace multichannel ECG signals

Singh

Nallikuzhy

Dandapat

2015

2015 Twenty First National Conference on Communications (NCC)

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Multichannel elctrocardiogram (MECG) signals are correlated both in spatial domain as well as in temporal domain and this correlation becomes even higher at multiscale levels. This work presents a MECG compression method in order to exploit the inherent inter-channel correlation more efficiently, using a multiscale compressive sensing (MSCS) based approach. Principal component analysis (PCA) is used to decorrelate the subband signals from different channels at each wavelet scale and then the significant eigenspace signals from higher frequency subbands are undergone through multiscale compressed sensing (CS). Since CS is well known for its effective representation of high dimensional sparse signals in terms of few random projections, here it confines the noise dominated high frequency clinical information of MECG signals to few compressed measurements which readily reduces the data size at the encoder side. Eigenspace is taken as the sparsifying basis for high frequency subband ECG signals. The proposed encoding strategy is implemented using a uniform scalar quantizer and a entropy encoder. Sparse signal recovery is done using a greedy sparse recovery algorithm called orthogonal matching pursuit (OMP). Performance evaluation of the coder is mainly carried out in terms of compression ratio (CR), root mean square difference (PRD), and wavelet energy based diagnostic distortion (WEDD). Simulation results give the lowest PRD value, 4.72% and WEDD value 3.28% at CR=10.84, for lead aVF for CSE multi-lead measurement library database.

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Section: B Multiscale Compressed Sensing For High Frequency Eigenspamentioning

confidence: 99%

Compressed sensing framework of data reduction at multiscale level for eigenspace multichannel ECG signals

Singh

Nallikuzhy

Dandapat

2015

2015 Twenty First National Conference on Communications (NCC)

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“…In 2-D ECG compression algorithms, 2-D transformation is applied on 2-D representation of I-D ECG signals. Detailed classifications of ECG compression algorithms (1-D, 2-D) are listed in [2].…”

Section: Compression Techniquesmentioning

confidence: 99%

“…ECG signals are digitized at sampling rates ranging from 100 to 1000 Hz with resolution of 8-12 bits per sample [2]. As a result, the produced ECG recording amounts to huge data sizes that quickly fill up available storage space.…”

Section: Introductionmentioning

confidence: 99%

An enhanced method of lossless ECG data compression using ASCII character encoding

B’charri

Latif

Dliou

et al. 2014

2014 Second World Conference on Complex Systems (WCCS)

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Cardiac arrest is quoted as the major contributor to sudden and unexpected death around the globe. For detection and identification of cardiac problems, the electrocardiogram (ECG), which represents electrical activity of the heart, has to be continuously monitored typically over 24-h period. As a result, the produced ECG recording amounts to huge data sizes. In this paper, a new ECG data compression algorithm for real-time applications is proposed. The whole algorithm is written in C platform. The algorithm is tested on various ECG data taken from PTB Diagnostic ECG Database. The compression scheme is such that the compressed file contains only ASCII characters. It is observed that the compression module gives a high compression ratio (CR = 18.48). The data reconstruction algorithm has also been developed using the reversed logic and it is seen that the difference between original and reconstructed ECG signal is almost negligible which leads to an excellent Quality Score.

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“…Digitizing the ECG signals is performed at sampling rates ranging from 100 to 1000 Hz with a resolution of 8 or 12 bits per sample [3]. As a result, the data sizes of the produced ECG recording will enormously increase and fill up available storage space.…”

Section: Introductionmentioning

confidence: 99%

An Efficent Lossless Compression Scheme for ECG Signal

B’charri¹,

Latif²,

Abenaou³

et al. 2016

ijacsa

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Abstract-Cardiac diseases constitute the main cause of mortality around the globe. For detection and identification of cardiac problems, it is very important to monitor the patient's heart activities for long periods during his normal daily life. The recorded signal that contains information about the condition of the heart called electrocardiogram (ECG). As a result, long recording of ECG signal amounts to huge data sizes. In this work, a robust lossless ECG data compression scheme for realtime applications is proposed. The developed algorithm has the advantages of lossy compression without introducing any distortion to the reconstructed signal. The ECG signals under test were taken from the PTB Diagnostic ECG Database. The compression procedure is simple and provides a high compression ratio compared to other lossless ECG compression methods. The compressed ECG data is generated as a text file. The decompression scheme has also been developed using the reverse logic and it is observed that there is no difference between original and reconstructed ECG signal.

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Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression

Cited by 53 publications

References 65 publications

Compressed sensing framework of data reduction at multiscale level for eigenspace multichannel ECG signals

Compressed sensing framework of data reduction at multiscale level for eigenspace multichannel ECG signals

An enhanced method of lossless ECG data compression using ASCII character encoding

An Efficent Lossless Compression Scheme for ECG Signal

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