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Reducing energy consumption of multi-channel mobile medical monitoring algorithms

Advances in integrated circuit technologies have enabled engineers to embed computation and communication resources in small form factors. Small wireless sensors have enabled a variety of ambulatory medical detection applications where individuals are instrumented in order to detect events outside a clinical setting. To make such devices suitable for long-term use, small batteries must be used. Thus, reducing overall energy consumption is essential.
 In these applications, reducing the amount of data sampled or processed can reduce energy consumption. One way to do this is to use a low-cost screening detector that quickly rules out segments of the sampled data that are unlikely to contain an event. Depending on the detection ability and relative power consumption of the screening detector, the combination of the original detector and screening detector can decrease energy consumption dramatically while maintaining low false positives and false negatives.
 In this paper, we describe a systematic method that uses machine learning to construct a screening detector for multi-feature detection algorithms. We evaluate this technique on real electroencephalography (EEG) data obtained from patients with epileptic seizures. Our results suggest that for most patients our technique can be used to construct a detector that reduces computation time by an average of 80% relative to the original detector. Moreover, we estimate that the average reduction in energy consumption would be 71% overall and 77% during non-seizure periods. This corresponds to a 4x increase in battery lifetime.