Department of General Surgery, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China.
School of Informatics, Xiamen University, Xiamen, Fujian, China.
Technol Health Care. 2024;32(S1):95-105. doi: 10.3233/THC-248008.
Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature.
This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases.
Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database.
The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters.
The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.
心血管疾病（CVDs）是全球主要的死亡原因，因此需要先进的诊断工具进行早期检测。心电图（ECG）因其非侵入性而成为诊断心脏异常的关键。
本研究旨在提出一种新的心电图信号分类方法，解决与各种疾病相关的心电图信号复杂性带来的挑战。
我们的方法集成了离散小波变换（DWT）进行特征提取，捕捉心血管疾病的显著特征。然后，使用 gcForest 模型进行高效分类。该方法在麻省理工学院-贝斯以色列医院心律失常数据库上进行了测试。
该方法在麻省理工学院-贝斯以色列医院心律失常数据库上取得了有前景的结果，测试准确率为 98.55%，召回率为 98.48%，精度为 98.44%，F1 得分为 98.46%。此外，该模型表现出稳健性和对超参数的低敏感性。
DWT 和 gcForest 模型的联合使用在心电图信号分类中证明是有效的，具有高精度和高可靠性。这种方法有可能改善心血管疾病的早期检测，为心脏保健做出贡献。
