Detection and classification of arrhythmia using an explainable deep learning model

Jo, Yong Yeon; Kwon, Joon; Jeon, Ki‐Hyun; Cho, Yong Hyeon; Shin, Jae Hyun; Lee, Yoon Ji; Jung, Min Seung; Ban, Jang Hyeon; Kim, Kyung Hee; Lee, Soo-Youn; Park, Jinsik; Oh, Byung Hee

doi:10.1016/j.jelectrocard.2021.06.006

Cited by 31 publications

(9 citation statements)

References 22 publications

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“…Through our understandable prediction and feature selection phase, it is possible to determine which features of datasets were more important in prediction. This explainable COVID-19 disease diagnosis model has higher transparency and explainability than previous black box methods [ [67] , [68] , [69] , [70] , [103] , [104] , [105] ] that can improve the acceptance rate and trustworthy of intelligent model for physicians.…”

Section: Discussionmentioning

confidence: 97%

“…In overall, we noticed that in the diagnosis of COVID-19, complex machine-learning models such as deep learning perform better than simple models such as linear regression and decision trees. Nevertheless, the deep learning-based approaches proposed in previous works were indeed black boxes that did not explain their prediction in a manner a human could understand [ [67] , [68] , [69] , [70] ]. It is therefore important to endow the highly performing deep-learning models with explainability and interpretability ability to accommodate the new EU data protection directive and ensure their widespread adoption by healthcare authorities.…”

Section: Introductionmentioning

confidence: 99%

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A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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“…Attia et al [20] showed that an AI-enabled ECG acquired during normal sinus rhythm permitted identification at point of care of individuals with atrial fibrillation with an area under the curve of 0.9 and an overall accuracy of 83%. Jo et al [21] describe an average area under the receiver operating characteristic curve using a 12-lead ECG for arrhythmia classification of 0.98. AI is already outperforming cardiologists in arrhythmia detection and classification.…”

Section: Arrhythmiasmentioning

confidence: 99%

Current and Future Use of Artificial Intelligence in Electrocardiography

Sellés

Marina-Breysse

2023

JCDD

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Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.

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“…However, some researchers have noted that accurate early detection of CHF increases treatment options and reduces mortality rates. Another serious disorder caused by an irregular heart rate is irregular heartbeats called arrhythmias (ARR) (Jo et al, 2021;Hu ve ark., 2020). In the literature research, it has been observed that many researchers have conducted various studies for the diagnosis of heart diseases using ECG signals, and the studies have made signi cant contributions to the early diagnosis of heart diseases.…”

Section: Literature Studiesmentioning

confidence: 99%

A novel approach for congestive heart failure and arrhythmia classification using OD- 1D-LBP with LSTM and 1D-CNN

Tekin

Kaya

2023

Preprint

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Congestive heart failure (CHF) and heart rhythm disorders (ARR) are known to be the most important heart diseases of the last decades, which have had negative effects on human health directly or indirectly. The early diagnosis of these types of heart diseases and rapid and practical medical intervention is very vital. For the diagnosis of such diseases, it is so difficult for doctors to analyze long ECG signals quickly and detect instantaneous parameter changes in the ECG signal with manual observation technique. Therefore, it is necessary to develop efficient Computer Aided Diagnosis (CAD) systems for the early diagnosis of diseases such as ARR and CHF. For this purpose, within the scope of digital signal processing, various statistical and mathematical algorithms have been developed to extract features from signals and various machine learning methods are applied for classification. In this study, a new method is proposed to analyze and classify the ECG signals of CHF, ARR ,and NSR (Normal Sinus Rhythm). This method is based on the local-binary-pattern (LBP) algorithm and is called Orthogonal Difference One Dimensional Local Binary Pattern (OD-1D-LBP). This method is an approach that uses binary information obtained by comparing each point on the signal with its neighbors. Histograms of new signals that are obtained from the proposed method (OD-1D-LBP) are given as input to Long Short-Term Memory (LSTM) and one-dimensional Convolutional Neural Networks (1D-CNN) for classification. If 70% of the input data is applied as training data and 30% as test data, the accuracy rates are obtained at 98.63% for LSTM and 98.86% for 1D-CNN. If only ACF and ARR data are classified; the accuracy rate is obtained at 98.94% for LSTM; and 97.40% for 1D-CNN. Similarly, when ACF and NSR ddataare classified; the accuracy rate is obtained at 99.4% for LSTM; and 98.9% for 1D-CNN. In case ARR and NSR data are classified; the accuracy rates are obtained at 99.2% for LSTM; and 95.2% for 1D-CNN.

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Detection and classification of arrhythmia using an explainable deep learning model

Cited by 31 publications

References 22 publications

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Current and Future Use of Artificial Intelligence in Electrocardiography

A novel approach for congestive heart failure and arrhythmia classification using OD- 1D-LBP with LSTM and 1D-CNN

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