PCA and ICA applied to noise reduction in multi-lead ECG

Abstract

The performance of PCA and ICA in the context of cleaning noisy ECGs in ambulatory conditions was investigated. With this aim, ECGs with artificial motion artifacts were generated by combining clean 8-channel ECGs with 8-channel noise signals at SNR values ranging from 10 down to −10 dB. For each SNR, 600 different simulated ECGs of 10-second length were selected. 8-channel PCA and ICA were applied and then inverted after selecting a subset of components. In order to evaluate the performance of PCA and ICA algorithms, the output of a beat detection algorithm was applied to both the output signal after PCA/ICA filtering and compared to the detections in the signal before filtering. Applying both PCA and ICA and retaining the optimal component subset, yielded sensitivity (Se) of 100% for all SNR values studied. In terms of Positive predictivity (+P), applying PCA, yielded to an improvement for all SNR values as compared to no cleaning (+P=95.45% vs. 83.09% for SNR=0dB; +P=56.87% vs. 48.81% for SNR=−10dB). However, ICA filtering gave a higher improvement in +P for all SNR values (+P=100.00% for SNR=0dB; +P=61.38% for SNR=−10dB). An automatic method for selecting the components was proposed. By using this method, both PCA and ICA gave an improvement as compared to no filtering over all SNR values. ICA had a better performance (SNR=−5dB, improvement in +P of 8.33% for PCA and 22.92% for ICA).