Adaptive Electrocardiogram Feature Extraction on Distributed Embedded Systems

Tiny embedded systems have not been an ideal outfit for high performance computing due to their constrained resources-limitations in processing power, battery life, communication bandwidth, and memory constrain the applicability of existing complex medical analysis algorithms such as the electrocardiogram (ECG) analysis. Among various limitations, battery lifetime has been a major key technological constraint. In this paper, we address the issue of partitioning such a complex algorithm while the energy consumption due to wireless transmission is minimized. ECG analysis algorithms normally consist of preprocessing, pattern recognition, and classification. Considering the orientation of the ECG leads, we devise a technique to perform preprocessing and pattern recognition locally in small embedded systems attached to the leads. The features detected in the pattern recognition phase are considered for the classification. Ideally, if the features detected for each heartbeat reside in a single processing node, the transmission will be unnecessary. Otherwise, to perform classification, the features must be gathered on a local node and, thus, the communication is inevitable. We perform such a feature grouping by modeling the problem as a hypergraph and applying partitioning schemes which yield a significant power saving in wireless communications. Furthermore, we utilize dynamic reconfiguration by software module migration. This technique, with respect to partitioning, enhances the overall power saving in such systems. Moreover, it adaptively alters the system configuration in various environments and on different patients. We evaluate the effectiveness of our proposed techniques on MIT/BIH benchmarks and, on average, achieve 70 percent energy saving