Hybrid Mechanism to Detect Paroxysmal Stage of Atrial Fibrillation Using Adaptive Threshold-Based Algorithm with Artificial Neural Network

Mohamad Sabri bin SINAL; Eiji KAMIOKA

doi:10.1587/transinf.2017EDP7235

Hybrid Mechanism to Detect Paroxysmal Stage of Atrial Fibrillation Using Adaptive Threshold-Based Algorithm with Artificial Neural Network

Mohamad Sabri bin SINAL, Eiji KAMIOKA

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Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.

Publication: IEICE TRANSACTIONS on Information Vol.E101-D No.6 pp.1666-1676

Publication Date: 2018/06/01

Publicized: 2018/03/14

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2017EDP7235

Type of Manuscript: PAPER

Category: Biological Engineering

Authors

Mohamad Sabri bin SINAL
Shibaura Institute of Technology
Eiji KAMIOKA
Shibaura Institute of Technology

Keyword

electrocardiogram (ECG), door-to-door algorithm, paroxysmal stage of Atrial Fibrillation, adaptive threshold of detecting heart beat cycle

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Mohamad Sabri bin SINAL, Eiji KAMIOKA, "Hybrid Mechanism to Detect Paroxysmal Stage of Atrial Fibrillation Using Adaptive Threshold-Based Algorithm with Artificial Neural Network" in IEICE TRANSACTIONS on Information, vol. E101-D, no. 6, pp. 1666-1676, June 2018, doi: 10.1587/transinf.2017EDP7235.
Abstract: Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7235/_p

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@ARTICLE{e101-d_6_1666,
author={Mohamad Sabri bin SINAL, Eiji KAMIOKA, },
journal={IEICE TRANSACTIONS on Information},
title={Hybrid Mechanism to Detect Paroxysmal Stage of Atrial Fibrillation Using Adaptive Threshold-Based Algorithm with Artificial Neural Network},
year={2018},
volume={E101-D},
number={6},
pages={1666-1676},
abstract={Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.},
keywords={},
doi={10.1587/transinf.2017EDP7235},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Hybrid Mechanism to Detect Paroxysmal Stage of Atrial Fibrillation Using Adaptive Threshold-Based Algorithm with Artificial Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 1666
EP - 1676
AU - Mohamad Sabri bin SINAL
AU - Eiji KAMIOKA
PY - 2018
DO - 10.1587/transinf.2017EDP7235
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2018
AB - Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is difficult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect significant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artificial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the significant peaks of heart cycle in 17 NS ECG data. In addition, it was verified that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was confirmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose early stage of heart diseases.
ER -