research-article

ADDHard: Arrhythmia Detection with Digital Hardware by Learning ECG Signal

Authors:

Sai Manoj Pudukotai Dinakarrao,

Axel JantschAuthors Info & Claims

GLSVLSI '18: Proceedings of the 2018 Great Lakes Symposium on VLSI

Pages 495 - 498

https://doi.org/10.1145/3194554.3194647

Published: 30 May 2018 Publication History

Abstract

Anomaly detection in Electrocardiogram (ECG) signals facilitates the diagnosis of cardiovascular diseases i.e., arrhythmias. Existing methods, although fairly accurate, demand a large number of computational resources. Based on the pre-processing of ECG signal, we present a low-complex digital hardware implementation (ADDHard) for arrhythmia detection. ADDHard has the advantages of low-power consumption and a small foot print. ADDHard is suitable especially for resource constrained systems such as body wearable devices. Its implementation was tested with the MIT-BIH arrhythmia database and achieved an accuracy of 97.28% with a specificity of 98.25% on average.

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Cited By

Srivastava RKumar BAlenezi FAlhudhaif AAlthubiti SPolat K(2022)Automatic Arrhythmia Detection Based on the Probabilistic Neural Network with FPGA ImplementationMathematical Problems in Engineering10.1155/2022/75640362022(1-11)Online publication date: 22-Mar-2022
https://doi.org/10.1155/2022/7564036
Lee MLee J(2022)A robust fusion algorithm of LBP and IMF with recursive feature elimination-based ECG processing for QRS and arrhythmia detectionApplied Intelligence10.1007/s10489-021-02368-552:1(939-953)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02368-5
Sa PAidos HRoma NTomas P(2019)Heart Disease Detection Architecture for Lead I Off-the-Person ECG Monitoring Devices2019 27th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO.2019.8902791(1-5)Online publication date: Sep-2019
https://doi.org/10.23919/EUSIPCO.2019.8902791
Show More Cited By

Index Terms

ADDHard: Arrhythmia Detection with Digital Hardware by Learning ECG Signal
1. Hardware
  1. Very large scale integration design
    1. Full-custom circuits
    2. On-chip sensors

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Published In

cover image ACM Conferences

GLSVLSI '18: Proceedings of the 2018 Great Lakes Symposium on VLSI

May 2018

533 pages

ISBN:9781450357241

DOI:10.1145/3194554

General Chair:
Deming Chen
University of Illinois, USA
,
Program Chairs:
Houman Homayoun
George Mason University, USA
,
Baris Taskin
Drexel University, USA

Copyright © 2018 ACM.

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Published: 30 May 2018

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GLSVLSI '18

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SIGDA

GLSVLSI '18: Great Lakes Symposium on VLSI 2018

May 23 - 25, 2018

IL, Chicago, USA

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GLSVLSI '18 Paper Acceptance Rate 48 of 197 submissions, 24%;

Overall Acceptance Rate 312 of 1,156 submissions, 27%

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GLSVLSI '25

Sponsor:
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Great Lakes Symposium on VLSI 2025

June 30 - July 2, 2025

New Orleans , LA , USA

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Cited By

Srivastava RKumar BAlenezi FAlhudhaif AAlthubiti SPolat K(2022)Automatic Arrhythmia Detection Based on the Probabilistic Neural Network with FPGA ImplementationMathematical Problems in Engineering10.1155/2022/75640362022(1-11)Online publication date: 22-Mar-2022
https://doi.org/10.1155/2022/7564036
Lee MLee J(2022)A robust fusion algorithm of LBP and IMF with recursive feature elimination-based ECG processing for QRS and arrhythmia detectionApplied Intelligence10.1007/s10489-021-02368-552:1(939-953)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1007/s10489-021-02368-5
Sa PAidos HRoma NTomas P(2019)Heart Disease Detection Architecture for Lead I Off-the-Person ECG Monitoring Devices2019 27th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO.2019.8902791(1-5)Online publication date: Sep-2019
https://doi.org/10.23919/EUSIPCO.2019.8902791
Dinakarrao SJantsch AShafique M(2019)Computer-aided Arrhythmia Diagnosis with Bio-signal ProcessingACM Computing Surveys10.1145/329771152:2(1-37)Online publication date: 27-Mar-2019
https://dl.acm.org/doi/10.1145/3297711
Fujita HCimr D(2019)Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessingApplied Intelligence10.1007/s10489-019-01461-049:9(3383-3391)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10489-019-01461-0

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