Multirate ECG Processing and k-Nearest
Neighbor Classifier Based Efficient Arrhythmia
Diagnosis
Saeed Mian Qaisar
1(&) , Moez Krichen
2,3 , and Fatma Jallouli
4
1 College of Engineering, Effat University, Jeddah, Kingdom of Saudi Arabia
sqaisar@effatuniversity.edu.sa
2 Faculty of CSIT, Al-Baha University, Al Bahah, Saudi Arabia
3 ReDCAD Laboratory, University of Sfax, Sfax, Tunisia
moez.krichen@redcad.org
4 Faculty of Medicine of Sfax, Sfax, Tunisia
fatma.jallouli@gmail.com
Abstract. The goal of this work is to make a contribution to the development of
computationally efficient multirate Electrocardiogram (ECG) automated detectors
of arrhythmia. It utilizes an intelligent combination of multirate denoising plus
wavelet decomposition for an effective realization of the ECG wireless implants.
The decomposed signal subband features are mined and in next step these are
utilized by the mature k-Nearest Neighbor (KNN) classifier for arrhythmia diagnosis. The multirate nature substantially reduces the processing activity of the
system and thus allows a dramatic decrease in energy consumption compared to
traditional counterparts. The performance of the system is estimated also in terms
of the classification performance. Obtained results reveal an overall 22.5-fold
compression gain and 4-folds processing outperformance over the traditional
equals while securing 93.2% highest classification accuracy and specificity of
0.956. Findings confirm that the proposed solution could potentially be embedded
in contemporary automatic and mobile cardiac diseases diagnosis systems.
Keywords: Multirate processing Á ECG Á Arrhythmia Á Wavelet Á Features
extraction Á Classification
1 Introduction
Cardiovascular diseases have drawn global attention. This is due to its increasing
prevalence and incidence [1, 14]. Electrocardiogram (ECG) measures electrical activities with respect to time. Manual examination of cardiac arrhythmias can be time
consuming and complicated. This challenge may be solved using computer-aided
automatic cardiac decision tools. The computer-aided or pattern-based recognition
systems could increase the effectiveness of cardiac health analysis by detecting subtle
differences in frequency and amplitude components of the heartbeat [2].
Many scientists have previously explored computer-assisted solutions for cardiac
health monitoring as reviewed in [7]. Preprocessing is the first ECG processing stage.
The popular ECG denoising methods are the finite impulse response (FIR) filtering,
© The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 329–337, 2020.
https://doi.org/10.1007/978-3-030-51517-1_29
Neighbor Classifier Based Efficient Arrhythmia
Diagnosis
Saeed Mian Qaisar
1(&) , Moez Krichen
2,3 , and Fatma Jallouli
4
1 College of Engineering, Effat University, Jeddah, Kingdom of Saudi Arabia
sqaisar@effatuniversity.edu.sa
2 Faculty of CSIT, Al-Baha University, Al Bahah, Saudi Arabia
3 ReDCAD Laboratory, University of Sfax, Sfax, Tunisia
moez.krichen@redcad.org
4 Faculty of Medicine of Sfax, Sfax, Tunisia
fatma.jallouli@gmail.com
Abstract. The goal of this work is to make a contribution to the development of
computationally efficient multirate Electrocardiogram (ECG) automated detectors
of arrhythmia. It utilizes an intelligent combination of multirate denoising plus
wavelet decomposition for an effective realization of the ECG wireless implants.
The decomposed signal subband features are mined and in next step these are
utilized by the mature k-Nearest Neighbor (KNN) classifier for arrhythmia diagnosis. The multirate nature substantially reduces the processing activity of the
system and thus allows a dramatic decrease in energy consumption compared to
traditional counterparts. The performance of the system is estimated also in terms
of the classification performance. Obtained results reveal an overall 22.5-fold
compression gain and 4-folds processing outperformance over the traditional
equals while securing 93.2% highest classification accuracy and specificity of
0.956. Findings confirm that the proposed solution could potentially be embedded
in contemporary automatic and mobile cardiac diseases diagnosis systems.
Keywords: Multirate processing Á ECG Á Arrhythmia Á Wavelet Á Features
extraction Á Classification
1 Introduction
Cardiovascular diseases have drawn global attention. This is due to its increasing
prevalence and incidence [1, 14]. Electrocardiogram (ECG) measures electrical activities with respect to time. Manual examination of cardiac arrhythmias can be time
consuming and complicated. This challenge may be solved using computer-aided
automatic cardiac decision tools. The computer-aided or pattern-based recognition
systems could increase the effectiveness of cardiac health analysis by detecting subtle
differences in frequency and amplitude components of the heartbeat [2].
Many scientists have previously explored computer-assisted solutions for cardiac
health monitoring as reviewed in [7]. Preprocessing is the first ECG processing stage.
The popular ECG denoising methods are the finite impulse response (FIR) filtering,
© The Author(s) 2020
M. Jmaiel et al. (Eds.): ICOST 2020, LNCS 12157, pp. 329–337, 2020.
https://doi.org/10.1007/978-3-030-51517-1_29