Cardiovascular diseases are the leading cause of mortality globally, necessitating advancements in diagnostic techniques. This study explores the application of wavelet transformation for classifying electrocardiogram (ECG) signals to identify various cardiovascular conditions. Utilizing the MIT-BIH Arrhythmia Database, we employed both continuous and discrete wavelet transforms to decompose ECG signals into frequency sub-bands, from which we extracted eight statistical features per band. These features were then used to train and test various classifiers, including K-Nearest Neighbors and Support Vector Machines, among others. The classifiers demonstrated high efficacy, with some achieving an accuracy of up to 96% on test data, suggesting that wavelet-based feature extraction significantly enhances the prediction of cardiovascular abnormalities in ECG data. The findings advocate for further exploration of wavelet transforms in medical diagnostics to improve automation and accuracy in disease detection. Future work will focus on optimizing feature selection and classifier parameters to refine predictive performance further.

翻译：心血管疾病是全球死亡率的主要病因，亟需诊断技术的进步。本研究探索了小波变换在心电图信号分类中的应用，以识别不同类型的心血管疾病。基于MIT-BIH心律失常数据库，我们采用连续小波变换与离散小波变换将心电图信号分解为频率子带，并从每个子带中提取了八项统计特征。随后，利用这些特征对多种分类器（包括K近邻、支持向量机等）进行训练与测试。分类器展现出高有效性，其中部分分类器对测试数据的准确率高达96%，表明基于小波的特征提取能显著增强心电图数据中心血管异常预测能力。研究结果支持在医学诊断中进一步探索小波变换，以提升疾病检测的自动化与准确性。未来工作将聚焦于优化特征选择与分类器参数，以进一步改善预测性能。