Design exploration of energy-performance trade-offs for wireless sensor networks
Proceedings of the 49th Annual Design Automation Conference
Proceedings of the 7th International Conference on Body Area Networks
Synchronizing code execution on ultra-low-power embedded multi-channel signal analysis platforms
Proceedings of the Conference on Design, Automation and Test in Europe
A methodology for embedded classification of heartbeats using random projections
Proceedings of the Conference on Design, Automation and Test in Europe
Multi-core architecture design for ultra-low-power wearable health monitoring systems
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
Hi-index | 0.00 |
This work is devoted to the evaluation of multilead digital wavelet transform (DWT)-based electrocardiogram (ECG) wave delineation algorithms, which were optimized and ported to a commercial wearable sensor platform. More specifically, we investigate the use of root-mean squared (RMS)-based multilead followed by a single-lead online delineation algorithm, which is based on a state-of-the-art offline single-lead delineator. The algorithmic transformations and software optimizations necessary to enable embedded ECG delineation notwithstanding the limited processing and storage resources of the target platform are described, and the performance of the resulting implementations are analyzed in terms of delineation accuracy, execution time, and memory usage. Interestingly, RMS-based multilead delineation is shown to perform equivalently to the best single-lead delineation for the 2-lead QT database (QTDB), within a fraction of a sample duration of the Common Standards for Electrocardiography (CSE) committee tolerances. Finally, a comprehensive evaluation of the energy consumption entailed by the considered algorithms is proposed, which allows very relevant insights into the dominant energy-draining functionalities and which suggests suitable design guidelines for long-lasting wearable ECG monitoring systems.