Disruption-tolerant wireless sensor networking for biomedical monitoring in outdoor conditions
Proceedings of the 7th International Conference on Body Area Networks
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The analysis of the electrocardiogram (ECG) is widely used for diagnosing many cardiac diseases. Since most of the clinically useful information in the ECG is found in characteristic wave peaks and boundaries, a significantamount of research effort has been devoted to the development of accurate and robust algorithms for automatic detection of the major ECG characteristic waves (i.e., the QRS complex, P and T waves), so-called ECG wave delineation. One of the most salient ECG wave delineation algorithms is based on the wavelet transform (WT). This work is dedicated to the sensible optimization and porting of this WT-based ECG wave delineator to an actual wearable embedded sensor platform with limited processing and storage resources. The porting was successful and the implementation was extensively validated using a standard manually annotated database. Interestingly, our results show that, despite the limitations of the embedded sensor platform, careful optimization allows to achieve comparable or even better delineation results than the original offline algorithm.