Adaptive Medical Feature Extraction for Resource Constrained 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/biological analysis algorithms to such platforms. Electrocardiogram (ECG) analysis resembles such algorithm. In this paper, we address the issue of partitioning an ECG analysis algorithm while the wireless communication power consumption is minimized. Considering the orientation of the ECG leads, we devise a technique to perform preprocessing and pattern recognition locally on small embedded systems attached to the leads. The features detected in pattern recognition phase are considered for classification. Ideally, if the features detected for each heart beat 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 commu...

• Type: Conference
• Year: 2006
• Where: PERCOM
• Authors: Roozbeh Jafari, Hyduke Noshadi, Majid Sarrafzadeh, Soheil Ghiasi
• Keywords: ECG Analysis Algorithm, PERCOM 2006, Significant Power Saving, Wireless Communication Power, Wireless Networks