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Morphological analysis on single lead contactless ECG monitoring based on a beat-template development

Abstract:

In an attempt to implement an ECG model that does not require active participation from the individual himself, contactless sensors are playing a relevant role in recent research. However, extremely high noise limited contactless applications. In this work we propose a method to make possible the morphological analysis based on single lead raw contactless capacitive ECG records. With respect to signal processing required, segmentation based on statistical parameters to discard invalid sections was applied, including artifacts removal filter, R-peak detection based on discrete derivative method, correlation analysis to discard ectopic beats, and smoothing statistical filter, to finally depict a sound noise-less beat template. The best configuration was proven to be placing the electrodes on the back (84% beats detected). Conversely, the best correlations among QRS complexes were obtained with wrist signals (71%, compared to 63% and 62% in forearm and wrist, respectively). A significant high number of beats are required for beat-template development, as well as for morphological analysis.