1 Detecting electrocardiogram abnormalities

In this work, we apply independent component analysis (ICA) to electrocardiographic (ECG) signals for improved detection of abnormal conditions in the heart. Unsupervised ICA neural networks can demix the components of measured ECG signals. Such components may correspond to individual heart functions, either normal or abnormal. ICA neural networks have the potential to make abnormal components more apparent, even when they are masked by normal components in the original measured signals. This is particularly important for diagnosis well in advance of the actual onset of heart attack, in which abnormalities in the original measured ECG signals may be difficult to detect. This is the first known work that applies ICA to EGC signals beyond noise extraction, to the detection of abnormal heart function