Zhu Yi, Shayan Amirali, Zhang Wanping, Chen Tong Lee, Jung Tzyy-Ping, Duann Jeng-Ren, Makeig Scott, Cheng Chung-Kuan
Department of Computer Science and Engineering, University of California, San Diego, CA 92093-0404, USA.
IEEE Trans Biomed Eng. 2008 Nov;55(11):2528-37. doi: 10.1109/TBME.2008.2001262.
The analysis of ECG signals is of fundamental importance for cardiac diagnosis. Conventional ECG recordings, however, use a limited number of channels (12) and each records a mixture of activities generated in different parts of the heart. Therefore, direct observation of the ECG signals collected on the body surface is likely an inefficient way to study and diagnose cardiac abnormalities. This study describes new experimental and analytical methods to capture more meaningful ECG component signals, each representing more directly a physical cardiac source. This study first describes a simply applied method for collecting high-density ECG signals. The recorded signals are then separated by independent component analysis (ICA) to obtain spatially fixed and temporally independent component activations. Results from five subjects show that P-, QRS-, and T-waves can be clearly separated from the recordings, suggesting ICA might be an effective and useful tool for high-density ECG analysis, interpretation, and diagnosis.
心电图信号分析对于心脏诊断至关重要。然而，传统的心电图记录使用有限数量的导联（12个），并且每个导联记录的是心脏不同部位产生的活动混合信号。因此，直接观察体表采集到的心电图信号可能是研究和诊断心脏异常的低效方法。本研究描述了新的实验和分析方法，以捕获更有意义的心电图成分信号，每个信号更直接地代表一个心脏物理源。本研究首先描述了一种简单应用的采集高密度心电图信号的方法。然后通过独立成分分析（ICA）对记录的信号进行分离，以获得空间固定且时间独立的成分激活。来自五名受试者的结果表明，P波、QRS波和T波可以从记录中清晰分离，这表明ICA可能是用于高密度心电图分析、解读和诊断的有效且有用的工具。
