Kim Namho, Seo Wonju, Kim Ju-Ho, Choi So Yoon, Park Sung-Min
Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea.
School of Computer Science, University of Seoul, Seoul, Republic of Korea.
Comput Methods Programs Biomed. 2023 Apr;231:107375. doi: 10.1016/j.cmpb.2023.107375. Epub 2023 Jan 25.
Automated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs.
Inspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject-oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while following a benchmark scheme. By adopting various mother wavelets, multiple WavelNet-based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN- and SincNet-based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro+ environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation.
The proposed WavelNet-based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spectral analysis while preserving temporal ECG information compared with vanilla CNN- and SincNet-based models. In particular, a Symlet 4 wavelet-adopting WavelNet-based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat classifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies.
Our proposed WavelNet-based arrhythmia classification model achieved remarkable performance based on a reasonable decision-making process, in comparison with other models. As its noteworthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases.
从心电图（ECG）中自动检测心律失常，可为医疗专业人员为心血管疾病患者提供有效治疗提供极大帮助。近年来，基于卷积神经网络（CNN）的心律失常分类模型已被引入，但其决策过程仍不明确，且其性能不可重复。本文基于一种名为WaveletNet的新型CNN架构，提出了一种准确、可解释且可重复的端到端心律失常分类模型，该架构对于处理ECG具有可解释性且是最优的。
受能够进行基于带通滤波的频谱分析的SincNet启发，设计了WaveletNet以实现基于小波变换的频谱分析。使用从麻省理工学院 - 贝斯以色列女执事医疗中心心律失常数据库生成的面向受试者的五类ECG心律失常数据集，并遵循基准方案对WaveletNet进行训练。通过采用各种母小波，实现了多个基于WaveletNet的心律失常分类模型。为了研究我们基于小波变换的方法是否优于原始的端到端和基于带通滤波的方法，将我们提出的模型与基于香草CNN和SincNet的模型进行了比较。在Google Colab Pro +环境中，模型实现和评估过程重复了十次。此外，将我们最成功的模型与最先进的心律失常分类模型进行比较以进行性能评估。
与基于香草CNN和SincNet的模型相比，所提出的基于WaveletNet的模型在对非异位、室上性异位和室性异位搏动进行分类时表现出优异的性能，因为它们能够在保留ECG时间信息的同时进行自适应频谱分析。特别是，采用Symlet 4小波的基于WaveletNet的模型取得了最佳性能，总体准确率接近90%，并且在对每个心律失常类别进行分类时具有最高的灵敏度水平：非异位、室上性异位和室性异位搏动分类的灵敏度分别为91.4%、49.3%和91.4%。这些结果与最先进的模型相当。此外，结果是可重复的，这使我们的研究与以前的研究有所不同。
与其他模型相比，我们提出的基于WaveletNet的心律失常分类模型基于合理的决策过程取得了显著的性能。由于其值得注意的性能在临床上是合理且可重复的，我们提出的模型可为为心血管疾病患者实施现实世界的精准医疗系统做出贡献。
