Selvam Immaculate Joy, Madhavan Moorthi, Kumarasamy Senthil Kumar
Department of Electronics and Communication Engineering, Saveetha Engineering College, Thandalam, Chennai, 602105, India.
Department of Biomedical Engineering, Saveetha Engineering College, Thandalam, Chennai, 602105, India.
Hellenic J Cardiol. 2025 Jan-Feb;81:75-84. doi: 10.1016/j.hjc.2024.08.011. Epub 2024 Aug 30.
Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features.
Precision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over the PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 s. The classification evaluation of five super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared.
The classification of various CVDs resulted in the highest accuracy of 98.51%, specificity of 98.12%, sensitivity of 97.9%, and F1-score of 97.95%. We have also achieved the minimum false positive and false negative rates of 2.07% and 1.87%, respectively, during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record.
When compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in the testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help clinicians to identify the disease earlier and carry out further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture.
心电图（ECG）一直是心血管疾病（CVD）诊断的重要工具，它直观地描绘了心脏的电活动。为了增强正常和患病心电图之间的自动分类，提取一致且定性的特征至关重要。
通过一种混合深度学习（DL）方法进行心电图分类的精度利用了卷积神经网络（CNN）架构和变分自编码器（VAE）技术。通过结合这些方法，我们旨在实现更准确、更稳健的心电图解读。该方法在PTB-XL数据集上进行训练和测试，该数据集包含来自18869名患者的21799份12导联心电图，每份心电图时长为10秒。将所提出的CNN-VAE模型对CVD的五个超级类别和23个子类别的分类评估进行了比较。
各种CVD的分类准确率最高达到98.51%，特异性为98.12%，敏感性为97.9%，F1分数为97.95%。在验证期间，我们还分别实现了最低的假阳性率和假阴性率，分别为2.07%和1.87%。结果根据个体心脏病专家给出的注释进行了验证，他们为每条记录分配了可能多个心电图诊断。
与其他深度学习方法相比，我们提出的CNN-VAE模型在测试阶段表现明显更好。本研究提出了一种将CNN-VAE相结合的新架构用于从心电图数据中进行CVD分类，这可以帮助临床医生更早地识别疾病并进行进一步治疗。由于其混合架构，CNN-VAE模型能够更好地表征输入信号。
