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：心血管疾病是全球范围内导致死亡的主要原因，这要求诊断技术不断进步。本研究探索了小波变换在心电图（ECG）信号分类中的应用，以识别多种心血管疾病。利用MIT-BIH心律失常数据库，我们采用了连续小波变换和离散小波变换将ECG信号分解为频率子带，并从每个子带中提取了八个统计特征。这些特征随后被用于训练和测试多种分类器，包括K近邻算法和支持向量机等。分类器表现出高效能，其中一些在测试数据上达到了高达96%的准确率，这表明基于小波的特征提取显著增强了ECG数据中预测心血管异常的能力。研究结果支持在医疗诊断中进一步探索小波变换，以提高疾病检测的自动化和准确性。未来的工作将集中于优化特征选择和分类器参数，以进一步提升预测性能。