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Comprehensive electrocardiographic diagnosis based on deep learning.
Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2020-01-20 , DOI: 10.1016/j.artmed.2019.101789
Oh Shu Lih , V Jahmunah , Tan Ru San , Edward J Ciaccio , Toshitaka Yamakawa , Masayuki Tanabe , Makiko Kobayashi , Oliver Faust , U Rajendra Acharya

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can be useful to detect established MI, and may also be helpful for early diagnosis of CAD. For the latter especially, the ECG perturbations can be subtle and potentially misclassified during manual interpretation and/or when analyzed by traditional algorithms found in ECG instrumentation. For automated diagnostic systems (ADS), deep learning techniques are favored over conventional machine learning techniques, due to the automatic feature extraction and selection processes involved. This paper highlights various deep learning algorithms exploited for the classification of ECG signals into CAD, MI, and CHF conditions. The Convolutional Neural Network (CNN), followed by combined CNN and Long Short-Term Memory (LSTM) models, appear to be the most useful architectures for classification. A 16-layer LSTM model was developed in our study and validated using 10-fold cross-validation. A classification accuracy of 98.5% was achieved. Our proposed model has the potential to be a useful diagnostic tool in hospitals for the classification of abnormal ECG signals.



中文翻译:

基于深度学习的综合心电图诊断。

心血管疾病(CVD)是全球范围内主要的死亡原因,而冠状动脉疾病(CAD)是主要的原因。如果未诊断和不进行治疗,早期CAD可能会进展,导致心肌梗塞(MI),可能诱发不可逆的心肌损伤,导致心腔重塑和最终充血性心力衰竭(CHF)。心电图(ECG)信号可用于检测已建立的MI,也可能有助于CAD的早期诊断。特别是对于后者,在手动解释期间和/或通过ECG仪器中发现的传统算法进行分析时,ECG扰动可能很细微,并且可能会误分类。对于自动诊断系统(ADS),深度学习技术优于传统的机器学习技术,由于涉及自动特征提取和选择过程。本文重点介绍了各种用于将ECG信号分类为CAD,MI和CHF条件的深度学习算法。卷积神经网络(CNN),然后是CNN和长短期记忆(LSTM)组合模型,似乎是分类中最有用的架构。在我们的研究中开发了一个16层LSTM模型,并使用10倍交叉验证对其进行了验证。实现了98.5%的分类精度。我们提出的模型有可能成为医院中异常心电信号分类的有用诊断工具。其次是CNN和长期短期记忆(LSTM)组合模型,这似乎是最有用的分类架构。在我们的研究中开发了一个16层LSTM模型,并使用10倍交叉验证对其进行了验证。实现了98.5%的分类精度。我们提出的模型有可能成为医院中异常心电信号分类的有用诊断工具。其次是CNN和长期短期记忆(LSTM)组合模型,这似乎是最有用的分类架构。在我们的研究中开发了一个16层LSTM模型,并使用10倍交叉验证对其进行了验证。实现了98.5%的分类精度。我们提出的模型有可能成为医院中异常心电信号分类的有用诊断工具。

更新日期:2020-01-20
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