Literature DB >> 32431956

A compressed-sensing-based compressor for ECG.

Vahi Izadi1, Pouria Karimi Shahri1, Hamed Ahani1.   

Abstract

Electrocardiogram (ECG) data compression has numerous applications. The time for generating compressed samples is a vital factor when we consider ambulatory devices, with the fact that data should be sent to the physician as soon as possible. In addition, there are some wearable ECG recorders that have limited power, and may only be capable of doing simple algorithms. With the aim of increasing the speed and simplicity of the compressors, we propose a system architecture that can generate compressed ECG samples, in a linear method and with CR 75%. We used sparsity of the ECG signal and proposed a system based on compressed sensing (CS) that can compress ECG samples, almost in real-time. We applied CS in a very small size in order to accelerate the compression phase and accordingly reducing the power consumption. Also, in the recovery phase, we used the recently developed Kronecker technique to improve the quality of the recovered signal. The system designed based on full-adder/subtractor (FAS) and shift registers, without using any external processor or any training algorithm. © Korean Society of Medical and Biological Engineering 2020.

Entities:  

Keywords:  Compressed sensing (CS); Compressor; Electrocardiogram (ECG)

Year:  2020        PMID: 32431956      PMCID: PMC7235110          DOI: 10.1007/s13534-020-00148-7

Source DB:  PubMed          Journal:  Biomed Eng Lett        ISSN: 2093-9868


  8 in total

1.  The weighted diagnostic distortion (WDD) measure for ECG signal compression.

Authors:  Y Zigel; A Cohen; A Katz
Journal:  IEEE Trans Biomed Eng       Date:  2000-11       Impact factor: 4.538

2.  Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm.

Authors:  Z Lu; D Y Kim; W A Pearlman
Journal:  IEEE Trans Biomed Eng       Date:  2000-07       Impact factor: 4.538

3.  ECG data compression using truncated singular value decomposition.

Authors:  J J Wei; C J Chang; N K Chou; G J Jan
Journal:  IEEE Trans Inf Technol Biomed       Date:  2001-12

4.  Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes.

Authors:  Hossein Mamaghanian; Nadia Khaled; David Atienza; Pierre Vandergheynst
Journal:  IEEE Trans Biomed Eng       Date:  2011-05-19       Impact factor: 4.538

5.  Wavelet and wavelet packet compression of electrocardiograms.

Authors:  M L Hilton
Journal:  IEEE Trans Biomed Eng       Date:  1997-05       Impact factor: 4.538

6.  Increasing the quality of reconstructed signal in compressive sensing utilizing Kronecker technique.

Authors:  H Zanddizari; S Rajan; Houman Zarrabi
Journal:  Biomed Eng Lett       Date:  2018-01-31

7.  Blind compressive sensing dynamic MRI.

Authors:  Sajan Goud Lingala; Mathews Jacob
Journal:  IEEE Trans Med Imaging       Date:  2013-03-27       Impact factor: 10.048

8.  Block sparsity-based joint compressed sensing recovery of multi-channel ECG signals.

Authors:  Anurag Singh; Samarendra Dandapat
Journal:  Healthc Technol Lett       Date:  2017-02-17
  8 in total
  1 in total

1.  Deep Compressive Sensing on ECG Signals with Modified Inception Block and LSTM.

Authors:  Jing Hua; Jue Rao; Yingqiong Peng; Jizhong Liu; Jianjun Tang
Journal:  Entropy (Basel)       Date:  2022-07-25       Impact factor: 2.738

  1 in total

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