Daphin Lilda S, Jayaparvathy R
Department of Electrical and Electronics Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
Comput Biol Med. 2025 Mar;186:109737. doi: 10.1016/j.compbiomed.2025.109737. Epub 2025 Jan 25.
Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a high mortality rate across the globe. The accurate and early prediction of various CVDs from the electrocardiogram (ECG) is vital for the prevention of deaths caused by CVD. Artificial intelligence (AI) is used to categorize and accurately predict various CVDs. Among different AI-based techniques, deep learning (DL)--based approaches are more effective in classifying various CVDs because they extract characteristics directly from the huge amounts of data needed to train the DL network. This paper proposes and compares the performance of a one-dimensional (1D), two-dimensional (2D) convolutional neural network (CNN), and a multi-branch convolutional neural network (MB-CNN) to classify various CVDs, namely, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), myocardial infarction (MI) and coronary artery disease (CAD) from spectrograms of one-dimensional (1D) ECG records. The 1D ECG records are classified using a 1D CNN is proposed which achieves a maximum performance of 97.60 %. To boost performance, the 1D ECG recordings are converted into 2D-ECG spectrograms via the continuous wavelet transform (CWT) and classified based on the proposed 2D-CNN with a maximum accuracy of 98.46 %. To further improve the classification performance, the obtained 2D- ECG spectrograms are classified using the proposed MB-CNN containing multiple branches which can capture various degrees of abstraction leading to a precise classification. The proposed approach using the MB-CNN model obtains an average test accuracy of 99.34 % for the classifications of five types of CVDs and 99.22 % for the classification of 5 classes of ECGs in the MIT-BIH database.
心血管疾病（CVD）是由心脏功能异常引起的，在全球范围内导致很高的死亡率。从心电图（ECG）准确、早期预测各种心血管疾病对于预防心血管疾病导致的死亡至关重要。人工智能（AI）被用于对各种心血管疾病进行分类和准确预测。在不同的基于AI的技术中，基于深度学习（DL）的方法在对各种心血管疾病进行分类方面更有效，因为它们直接从训练DL网络所需的大量数据中提取特征。本文提出并比较了一维（1D）、二维（2D）卷积神经网络（CNN）和多分支卷积神经网络（MB-CNN）对各种心血管疾病进行分类的性能，这些心血管疾病包括扩张型心肌病（DCM）、肥厚型心肌病（HCM）、心肌梗死（MI）和冠状动脉疾病（CAD），分类依据是一维（1D）心电图记录的频谱图。使用提出的一维卷积神经网络对一维心电图记录进行分类，其最高性能达到97.60%。为了提高性能，通过连续小波变换（CWT）将一维心电图记录转换为二维心电图频谱图，并基于提出的二维卷积神经网络进行分类，最高准确率为98.46%。为了进一步提高分类性能，使用提出的包含多个分支的多分支卷积神经网络对获得的二维心电图频谱图进行分类，该网络可以捕捉不同程度的抽象信息，从而实现精确分类。所提出的使用多分支卷积神经网络模型的方法在麻省理工学院 - 贝斯以色列女执事医疗中心（MIT-BIH）数据库中对五种类型的心血管疾病分类的平均测试准确率为99.34%，对五类心电图分类的平均测试准确率为99.22%。
