Independent Component Analysis and Decision Trees for ECG Holter Recording De-Noising

We have developed a method focusing on ECG signal de-noising using Independent component analysis (ICA). This approach combines JADE source separation and binary decision tree for identification and subsequent ECG noise removal. In order to to test the efficiency of this method comparison to standard filtering a wavelet- based de-noising method was used. Freely data available at Physionet medical data storage were evaluated. Evaluation criteria was root mean square error (RMSE) between original ECG and filtered data contaminated with artificial noise. Proposed algorithm achieved comparable result in terms of standard noises (power line interference, base line wander, EMG), but noticeably significantly better results were achieved when uncommon noise (electrode cable movement artefact) were compared.

本研究开发了一种基于独立成分分析（Independent Component Analysis, ICA）的心电图（Electrocardiogram, ECG）信号去噪方法。该方法结合联合近似对角化（Joint Approximate Diagonalization of Eigenmatrices, JADE）源分离技术与二叉决策树，实现心电信号噪声的识别与后续去除。为对比本方法与标准滤波技术的降噪效能，本研究采用基于小波的去噪方法作为对照。本研究对可从PhysioNet医学数据存储库免费获取的数据集开展评测，评测指标为原始心电信号与受人工噪声污染的滤波后心电信号之间的均方根误差（Root Mean Square Error, RMSE）。所提出的算法在标准噪声（包括工频干扰、基线漂移、肌电信号（Electromyography, EMG）干扰）的处理上取得了与对照方法相当的效果；而针对电极电缆移动伪影这类罕见噪声时，其降噪效果则显著更优。