An electrocardiogram signal compression techniques: a comprehensive review

Rajankar, Supriya O.; Talbar, Sanjay N.

doi:10.1007/s10470-018-1323-1

Cited by 44 publications

(17 citation statements)

References 96 publications

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“…The modified folded undecimator based ECG detector is implemented and simulated using Xilinx System Generator (XSG) and tested with standard MIT-BIH Arrhythmia database 22 . MIT-BIH Arrhythmia database consists of 48 recordings sampled at 360 Hz and 11 bits resolution with a range of 10 mV [23][24][25] . The modified architecture is tested with signals of MIT-BIH short time database (10-s), medium database (1-min) and full length database (1-h) 22,26 .The performance of both conventional and modified ECG detectors are evaluated using sensitivity S e , Positive prediction P + and Detection Error Rate(DER) 9,23,27 .…”

Section: Resultsmentioning

confidence: 99%

“…MIT-BIH Arrhythmia database consists of 48 recordings sampled at 360 Hz and 11 bits resolution with a range of 10 mV 23 – 25 . The modified architecture is tested with signals of MIT-BIH short time database (10-s), medium database (1-min) and full length database (1-h) 22 , 26 .The performance of both conventional and modified ECG detectors are evaluated using sensitivity S e , Positive prediction P + and Detection Error Rate(DER) 9 , 23 , 27 . A beat by beat comparison is performed for signals present in the database and calculated using ( 18 )–( 19 ).…”

Section: Resultsmentioning

confidence: 99%

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Area efficient folded undecimator based ECG detector

Uma

Kalpana

2021

Sci Rep

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This paper presents an area-efficient folded wavelet filter-based Electrocardiogram (ECG) detector for cardiac pacemakers. The modified folded undecimator based detector consists of Wavelet Filter Bank, QRS complex detector with Generalized Likelihood Ratio Test (GLRT) block and noise detector. A high-level transformation technique such as folding transformation and Cutset retiming are applied to the GLRT block in order to reduce the silicon area. Folding is a high-level transformation applied at the architectural level to enhance the performance of DSP architectures. It reduces the number of adders, multipliers and delay elements in the architecture. The Cutset retiming reduces clock period of the architecture by changing position of delay elements in the critical path. The folding transformation and cutset retiming implement the functional blocks of the GLRT circuit with minimum hardware. The modified folded ECG detector is tested for short term and long-term MIT-BIH databases. The results show that the modified folded undecimator detector has hardware savings and achieves sensitivity of 99.95%, positive prediction of 99.97% and Detection Error Rate (DER) of 0.061. The folded GLRT block architecture is synthesized with FPGA Zed board XC7Z010CLG484-1. Results show that the device utilization and power consumption are lesser than the conventional GLRT structure.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Area efficient folded undecimator based ECG detector

Uma

Kalpana

2021

Sci Rep

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“…DDC with HE [ 28 ] encodes the pre-processed signal directly without any transformation. HE was performed directly on the data [ 33 ].…”

Section: Methodsmentioning

confidence: 99%

“…SVD is used to factorize the signal into three smaller sets of values [ 27 ], which preserves features for digital biomarker development. BD-WT is widely used in ECG data compression [ 14 , 16 , 23 , 28 ]. DCT-DOST is a more recent method that has shown to be very robust for ECG signal compression [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Biosignal Compression Toolbox for Digital Biomarker Discovery

Bent

Kim

et al. 2021

Sensors

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A critical challenge to using longitudinal wearable sensor biosignal data for healthcare applications and digital biomarker development is the exacerbation of the healthcare “data deluge,” leading to new data storage and organization challenges and costs. Data aggregation, sampling rate minimization, and effective data compression are all methods for consolidating wearable sensor data to reduce data volumes. There has been limited research on appropriate, effective, and efficient data compression methods for biosignal data. Here, we examine the application of different data compression pipelines built using combinations of algorithmic- and encoding-based methods to biosignal data from wearable sensors and explore how these implementations affect data recoverability and storage footprint. Algorithmic methods tested include singular value decomposition, the discrete cosine transform, and the biorthogonal discrete wavelet transform. Encoding methods tested include run-length encoding and Huffman encoding. We apply these methods to common wearable sensor data, including electrocardiogram (ECG), photoplethysmography (PPG), accelerometry, electrodermal activity (EDA), and skin temperature measurements. Of the methods examined in this study and in line with the characteristics of the different data types, we recommend direct data compression with Huffman encoding for ECG, and PPG, singular value decomposition with Huffman encoding for EDA and accelerometry, and the biorthogonal discrete wavelet transform with Huffman encoding for skin temperature to maximize data recoverability after compression. We also report the best methods for maximizing the compression ratio. Finally, we develop and document open-source code and data for each compression method tested here, which can be accessed through the Digital Biomarker Discovery Pipeline as the “Biosignal Data Compression Toolbox,” an open-source, accessible software platform for compressing biosignal data.

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“…More complex reviews on compression methods can be found in studies by Jalaleddine 40 (1990), Manikandan 12 (2014), Kale 16 (2016), Rajankar 41 (2018), Tiwari 42 (2019). Summary of quality assessment of ECG signals after compression are in reviews by Dandapat 43 (2015) and Nemcova 15 (2018).…”

Section: Introductionmentioning

confidence: 99%

Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT

Němcová

Vítek

Novàkovâ

2020

Sci Rep

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Compression of ECG signal is essential especially in the area of signal transmission in telemedicine. There exist many compression algorithms which are described in various details, tested on various datasets and their performance is expressed by different ways. There is a lack of standardization in this area. This study points out these drawbacks and presents new compression algorithm which is properly described, tested and objectively compared with other authors. This study serves as an example how the standardization should look like. Single-cycle fractal-based (SCyF) compression algorithm is introduced and tested on 4 different databases—CSE database, MIT-BIH arrhythmia database, High-frequency signal and Brno University of Technology ECG quality database (BUT QDB). SCyF algorithm is always compared with well-known algorithm based on wavelet transform and set partitioning in hierarchical trees in terms of efficiency (2 methods) and quality/distortion of the signal after compression (12 methods). Detail analysis of the results is provided. The results of SCyF compression algorithm reach up to avL = 0.4460 bps and PRDN = 2.8236%.

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An electrocardiogram signal compression techniques: a comprehensive review

Cited by 44 publications

References 96 publications

Area efficient folded undecimator based ECG detector

Area efficient folded undecimator based ECG detector

Biosignal Compression Toolbox for Digital Biomarker Discovery

Complex study on compression of ECG signals using novel single-cycle fractal-based algorithm and SPIHT

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