Electrocardiogram Compression Technique Using DWT-Based Residue Encoder with Desired Reconstruction Quality

doi:10.1109/EAIT.2018.8470445

Proceedings Article•DOI•

Electrocardiogram Compression Technique Using DWT-Based Residue Encoder with Desired Reconstruction Quality

Sohel Das¹, Priyanka Bera², Rajarshi Gupta²•Institutions (2)

National Institute of Technology, Rourkela¹, University of Calcutta²

01 Jan 2018-

TL;DR: A new compression technique which exploits the high correlations between the consecutive beats of an Electrocardiogram (ECG) and subjected to compression using Discrete Wavelet Transform (DWT).

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Abstract: We propose a new compression technique which exploits the high correlations between the consecutive beats of an Electrocardiogram (ECG). A single progressive average beat (PAB) and residues were computed from every 10 ECG beats and subjected to compression using Discrete Wavelet Transform (DWT). Reconstruction errors were controlled by selective reconstruction of the wavelet coefficients from the PAB and residues using 5% PRDN and 99% energy reconstruction efficiency (ERE) criteria. Finally, the selected coefficients were encoded using a lossless compressor, a combination of delta and run length encoder. An average CR and absolute maximum error of 8.96, and 0.08 mV respectively were obtained with 32 numbers of MITA data under Physionet. Major clinical features like QRS amplitude, T height, P height and QT interval had low distortion when the target PRDN limit was relaxed up to 15%.

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Journal Article•DOI•

Preserving Abnormal Beat Morphology in Long-Term ECG Recording: An Efficient Hybrid Compression Approach

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Priyanka Bera¹, Rajarshi Gupta¹, Jayanta Saha²•Institutions (2)

University of Calcutta¹, Medical College and Hospital, Kolkata²

01 May 2020-IEEE Transactions on Instrumentation and Measurement

TL;DR: A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio of ECG data, and a useful outcome is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.

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Abstract: In long-term electrocardiogram (ECG) recording for arrhythmia monitoring, using a uniform compression strategy throughout the entire data to achieve high compression efficiency may result in unacceptable distortion of abnormal beats. The presented work addressed a solution to this problem, rarely discussed in published research. A support vector machine (SVM)-based binary classifier was implemented to identify the abnormal beats, achieving a classifier sensitivity (SE) and negative predictive value (NPV) of 99.89% and 0.003%, respectively with 34 records from MIT-BIH Arrhythmia database (mitdb). A hybrid lossy compression technique was implemented to ensure on-demand quality, either in terms of distortion or compression ratio (CR) of ECG data. A wavelet-based compression for the abnormal beats was implemented, while the consecutive normal beats were compressed in groups using a hybrid encoder, employing a combination of wavelet and principal component analysis. Finally, a neural network-based intelligent model was used, which was offline tuned by a particle swarm optimization (PSO) technique, to allocate optimal quantization level of transform domain coefficients generated from the hybrid encoder. The proposed technique was evaluated with four types of morphology tags, “A,” “F,” “L,” and “V,” from mitdb database, achieving less than 2% PRDN and less than 1% in two diagnostic distortion measures for abnormal beats. Overall, an average CR of 19.78 and PRDN of 3.34% was obtained. A useful outcome of the proposed technique is the low reconstruction time in rapid screening of long arrhythmia records, while only abnormal beats are presented for evaluation.

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...For this, a group of consecutive 10 normal beats (N) was formed to compute an average and residuals [21]....
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Comprar ECG Acquisition and Automated Remote Processing | R. Gupta | 9788132215561 | Springer

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R. Gupta, M. Mitra, Jitendranath Bera

01 Jan 2014

TL;DR: Gupta et al. as discussed by the authors presented an online donde Comprar ECG Acquisition and Automated Remote Processing al precio 104,99 € de R. Gupta | M. Mitra | J. Bera, tienda de libros de Medicina, Libros de Cardiologia - Electrocardiografia

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Abstract: Tienda online donde Comprar ECG Acquisition and Automated Remote Processing al precio 104,99 € de R. Gupta | M. Mitra | J. Bera, tienda de Libros de Medicina, Libros de Cardiologia - Electrocardiografia

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"Electrocardiogram Compression Techn..." refers background in this paper

...4 squared difference normalized (PRDN) values at 3% and 5%, and calculated the performance indices like Compression ratio (CR), mean square error (MSE), and maximum absolute error (MAE) [20]....
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...For example, for MITA 117 beat-by-beat compression yielded CR, PRD, PRDN, MSE and MAE as 6.54, 2.07, 3.05, 4.27×10-2 and 0.004 mV respectively Target PRDN 5...
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...With limiting PRDN of 5%, an average CR, MSE and MAE of 8.96, 0.229×10 -3 and 0.08 mV, were obtained respectively....
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...Since the primary focus of the proposed work is to limit the reconstruction error within pre-specified values, we imposed limit to percentage root mean squared difference normalized (PRDN) values at 3% and 5%, and calculated the performance indices like Compression ratio (CR), mean square error (MSE), and maximum absolute error (MAE) [20]....
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Electrocardiogram Compression Technique Using DWT-Based Residue Encoder with Desired Reconstruction Quality

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