Analyzing High-Density ECG Signals Using ICA

作者：Zhu Yi^*; Shayan Amirali; Zhang Wanping; Chen Tong Lee; Jung Tzyy Ping; Duann Jeng Ren; Makeig Scott; Cheng Chung Kuan

来源：IEEE Transactions on Biomedical Engineering, 2008, 55(11): 2528-2537.

DOI：10.1109/TBME.2008.2001262

• Blind signal separation
• ECG
• high-density surface ECG
• independent component analysis (ICA)
• noninvasive imaging

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.

• 出版日期2008-11