Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques

Usha Desai, Roshan Joy Martis, C. Gurudas Nayak, K. Sarika, G. Seshikala

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. Likewise, minute variations in time-domain features viz. amplitude, segments and intervals are difficult to interpret by naked eye. The current paper, describes a machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features. Further, methodology comprises dimensionality reduction using Independent Component Analysis (ICA), ten-fold cross-validation and classification using Support Vector Machine (SVM) kernel functions. Using ANOVA significant features are selected and reliability of accuracy is measured by Cohen's kappa statistic. Large dataset of 110,093 heartbeats from 48 records of MIT-BIH arrhythmia database recommended by ANSI/AAMI EC57:1998, which are grouped into five classes of arrhythmia beats viz. Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) are classified with class specific accuracy of 99.57%, 97.91%, 92.18%, 76.54% and 97.22% respectively and an overall average accuracy of 98.49%, using SVM quadratic kernel. The developed methodology is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.

Original languageEnglish
Title of host publication12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control
Subtitle of host publication(E3-C3), INDICON 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467373999
DOIs
Publication statusPublished - 29-03-2016
Event12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015 - New Delhi, India
Duration: 17-12-201520-12-2015

Conference

Conference12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015
CountryIndia
CityNew Delhi
Period17-12-1520-12-15

Fingerprint

Discrete wavelet transforms
Independent component analysis
Electrocardiography
Support vector machines
Analysis of variance (ANOVA)
Learning systems
Screening
Fusion reactions
Health
Statistics
Costs

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Desai, U., Martis, R. J., Nayak, C. G., Sarika, K., & Seshikala, G. (2016). Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. In 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015 [7443220] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INDICON.2015.7443220
Desai, Usha ; Martis, Roshan Joy ; Nayak, C. Gurudas ; Sarika, K. ; Seshikala, G. / Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015. Institute of Electrical and Electronics Engineers Inc., 2016.
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abstract = "Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. Likewise, minute variations in time-domain features viz. amplitude, segments and intervals are difficult to interpret by naked eye. The current paper, describes a machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features. Further, methodology comprises dimensionality reduction using Independent Component Analysis (ICA), ten-fold cross-validation and classification using Support Vector Machine (SVM) kernel functions. Using ANOVA significant features are selected and reliability of accuracy is measured by Cohen's kappa statistic. Large dataset of 110,093 heartbeats from 48 records of MIT-BIH arrhythmia database recommended by ANSI/AAMI EC57:1998, which are grouped into five classes of arrhythmia beats viz. Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) are classified with class specific accuracy of 99.57{\%}, 97.91{\%}, 92.18{\%}, 76.54{\%} and 97.22{\%} respectively and an overall average accuracy of 98.49{\%}, using SVM quadratic kernel. The developed methodology is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.",
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Desai, U, Martis, RJ, Nayak, CG, Sarika, K & Seshikala, G 2016, Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015., 7443220, Institute of Electrical and Electronics Engineers Inc., 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015, New Delhi, India, 17-12-15. https://doi.org/10.1109/INDICON.2015.7443220

Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. / Desai, Usha; Martis, Roshan Joy; Nayak, C. Gurudas; Sarika, K.; Seshikala, G.

12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7443220.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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T1 - Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques

AU - Desai, Usha

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AU - Seshikala, G.

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AB - Electrocardiogram (ECG) remains the most reliable and low-cost diagnostic tool to evaluate the patients with cardiac arrhythmias. Manual diagnosis of arrhythmia beats is very tedious due to the nonlinear and complex nature of ECG. Likewise, minute variations in time-domain features viz. amplitude, segments and intervals are difficult to interpret by naked eye. The current paper, describes a machine learning-based approach for computer-assisted detection of five classes of ECG arrhythmia beats using Discrete Wavelet Transform (DWT) features. Further, methodology comprises dimensionality reduction using Independent Component Analysis (ICA), ten-fold cross-validation and classification using Support Vector Machine (SVM) kernel functions. Using ANOVA significant features are selected and reliability of accuracy is measured by Cohen's kappa statistic. Large dataset of 110,093 heartbeats from 48 records of MIT-BIH arrhythmia database recommended by ANSI/AAMI EC57:1998, which are grouped into five classes of arrhythmia beats viz. Non-ectopic (N), Supraventricular ectopic (S), Ventricular ectopic (V), Fusion (F) and Unknown (U) are classified with class specific accuracy of 99.57%, 97.91%, 92.18%, 76.54% and 97.22% respectively and an overall average accuracy of 98.49%, using SVM quadratic kernel. The developed methodology is an efficient tool, which has intensive applications in early diagnosis and mass screening of cardiac health.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Desai U, Martis RJ, Nayak CG, Sarika K, Seshikala G. Machine intelligent diagnosis of ECG for arrhythmia classification using DWT, ICA and SVM techniques. In 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015. Institute of Electrical and Electronics Engineers Inc. 2016. 7443220 https://doi.org/10.1109/INDICON.2015.7443220