Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram

Jo, Yong Yeon; Cho, Younghoon; Lee, Soo-Youn; Kwon, Joon; Kim, Kyung Hee; Jeon, Ki‐Hyun; Cho, Soohyun; Park, Jinsik; Oh, Byung Hee

doi:10.1016/j.ijcard.2020.11.053

Cited by 73 publications

(45 citation statements)

References 22 publications

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“…The most important aspect of deep learning is its ability to extract features and develop an algorithm using various types of data, such as images, 2D data, and waveforms. In previous studies, Attia and colleagues and our study group developed a deep learning‐based model to screen for heart failure, arrhythmia, valvular heart disease, left ventricular hypertrophy, and anemia (Attia, Friedman, et al., 2019; Attia, Kapa, et al., 2019; Attia, Noseworthy, et al., 2019; Cho et al., 2020; Galloway et al., 2019; Jo et al., 2020; Kwon, Cho, et al., 2020; Kwon, Kim, et al., 2020; Kwon, Lee, et al., 2020). In recent studies, Attia and colleagues showed that hyperkalemia and hypokalemia could be detected using ECG based on a deep learning model (Galloway et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

“…It is not easy to make diagnostic tools based on conventional statistical methods using such subtle ECG changes. Deep learning has previously been used in the medical field to identify lesions and is currently used to analyze ECGs to diagnose heart failure, valvular heart disease, anemia, and coronary artery disease (Attia, Friedman, et al., 2019; Attia, Kapa, et al., 2019; Attia, Noseworthy, et al., 2019; Cho et al., 2020; Galloway et al., 2019; Jo et al., 2020; Kwon, Cho, et al., 2020; Kwon, Kim, et al., 2020; Kwon, Lee, et al., 2020). Recent studies have shown that deep learning models can detect dyskalemia using ECG (Galloway et al., 2019; Lin et al., 2020).…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Kwon

Jung²,

Kim

et al. 2021

Noninvasive Electrocardiol

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Introduction:The detection and monitoring of electrolyte imbalance is essential for appropriate management of many metabolic diseases; however, there is no tool that detects such imbalances reliably and noninvasively. In this study, we developed a deep learning model (DLM) using electrocardiography (ECG) for detecting electrolyte imbalance and validated its performance in a multicenter study. Methods and Results: This retrospective cohort study included two hospitals: 92,140 patients who underwent a laboratory electrolyte examination and an ECG within 30 min were included in this study. A DLM was developed using 83,449 ECGs of 48,356 patients; the internal validation included 12,091 ECGs of 12,091 patients.We conducted an external validation with 31,693 ECGs of 31,693 patients from another hospital, and the result was electrolyte imbalance detection. During internal, the area under the receiving operating characteristic curve (AUC) of a DLM using a 12-lead ECG for detecting hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.945, 0.866, 0.944, 0.885, 0.905, and 0.901, respectively. The values during external validation of the AUC of hyperkalemia, hypokalemia, hypernatremia, hyponatremia, hypercalcemia, and hypocalcemia were 0.873, 0.857, 0.839, 0.856, 0.831, and 0.813 respectively. The DLM helped to visualize the important ECG region for detecting each electrolyte imbalance, and it showed how the P wave, QRS complex, or T wave differs in importance in detecting each electrolyte imbalance. Conclusion:The proposed DLM demonstrated high performance in detecting electrolyte imbalance. These results suggest that a DLM can be used for detecting and monitoring electrolyte imbalance using ECG on a daily basis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Kwon

Jung²,

Kim

et al. 2021

Noninvasive Electrocardiol

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“…Jo et al . 94 proposed a DeepNN model based on variational autoencoders that predicts AF highly accurately and provides some model interpretability. Da Poian et al .…”

Section: ML For Detecting Afmentioning

confidence: 99%

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

Olier

Ortega‐Martorell

Pieroni

et al. 2021

Cardiovascular Research

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There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centered on applying ML for detecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modeling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centered in aims such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g., blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.

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“…The obtained results were for KNN − ACC = 93.3% and DT − ACC = 96.3%. Various deep learning models for the examination of the ECG signal have also been proposed for atrial fibrillation, obtaining the result of ACC = 0.992 [ 19 ]. It is worth noting that the presented model successfully detected atrial fibrillation, and the tests were carried out with the use of various ECG signals.…”

Section: Introductionmentioning

confidence: 99%

ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset

Śmigiel

Pałczyński

Ledziński

2021

Entropy

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The analysis and processing of ECG signals are a key approach in the diagnosis of cardiovascular diseases. The main field of work in this area is classification, which is increasingly supported by machine learning-based algorithms. In this work, a deep neural network was developed for the automatic classification of primary ECG signals. The research was carried out on the data contained in a PTB-XL database. Three neural network architectures were proposed: the first based on the convolutional network, the second on SincNet, and the third on the convolutional network, but with additional entropy-based features. The dataset was divided into training, validation, and test sets in proportions of 70%, 15%, and 15%, respectively. The studies were conducted for 2, 5, and 20 classes of disease entities. The convolutional network with entropy features obtained the best classification result. The convolutional network without entropy-based features obtained a slightly less successful result, but had the highest computational efficiency, due to the significantly lower number of neurons.

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Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram

Cited by 73 publications

References 22 publications

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

Artificial intelligence for detecting electrolyte imbalance using electrocardiography

How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management

ECG Signal Classification Using Deep Learning Techniques Based on the PTB-XL Dataset

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