用陷波滤波+统计方法提升心电图诊断准确率
Integrating Notch Filtering and Statistical Methods for Improved Cardiac Diagnostics Using MATLAB
- 针对50/60Hz工频干扰设计陷波滤波器
- 保留P/QRS/T波关键特征，提升分类准确率
- 适合医疗信号处理与心律失常检测研究者
心电图（ECG）信号处理中，陷波滤波器对消除窄带噪声至关重要，尤其是50 Hz或60 Hz的工频干扰。这类干扰与心电图的关键特征重叠，影响后续分类任务（如心律失常检测）的准确性。通过合理设计陷波滤波器，可显著提升信号质量，同时保留P波、QRS波群和T波等核心成分，进而增强用于心电图分类的机器学习或深度学习模型性能。
原文摘要 · Abstract (English)
A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially powerline interference at 50 Hz or 60 Hz. This interference overlaps with vital ECG signal features, affecting the accuracy of downstream classification tasks (e.g., arrhythmia detection). A properly designed notch filter enhances signal quality, preserves essential ECG components (P, QRS, T waves), and improves the performance of machine learning or deep learning models used for ECG classification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。