Analyzing high-density ECG signals using ICA.
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.
Zhu
Yi
Y
Department of Computer Science and Engineering, University of California, San Diego, CA 92093-0404, USA. y2zhu@cs.ucsd.edu
Shayan
Amirali
A
Zhang
Wanping
W
Chen
Tong Lee
TL
Jung
Tzyy-Ping
TP
Duann
Jeng-Ren
JR
Makeig
Scott
S
Cheng
Chung-Kuan
CK
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004562:Electrocardiography / Q000379:methods*; D006321:Heart / Q000502:physiology*; D006801:Humans; D025341:Principal Component Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity