Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Power management techniques for mobile communication
MobiCom '98 Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking
Energy-aware adaptation for mobile applications
Proceedings of the seventeenth ACM symposium on Operating systems principles
Wake on wireless: an event driven energy saving strategy for battery operated devices
Proceedings of the 8th annual international conference on Mobile computing and networking
The Case for Higher-Level Power Management
HOTOS '99 Proceedings of the The Seventh Workshop on Hot Topics in Operating Systems
An introduction to variable and feature selection
The Journal of Machine Learning Research
CoolSpots: reducing the power consumption of wireless mobile devices with multiple radio interfaces
Proceedings of the 4th international conference on Mobile systems, applications and services
Operating System Modifications for Task-Based Speed and Voltage
Proceedings of the 1st international conference on Mobile systems, applications and services
Wireless wakeups revisited: energy management for voip over wi-fi smartphones
Proceedings of the 5th international conference on Mobile systems, applications and services
A framework for the automated generation of power-efficient classifiers for embedded sensor nodes
Proceedings of the 5th international conference on Embedded networked sensor systems
HARMONI: Context-aware Filtering of Sensor Data for Continuous Remote Health Monitoring
PERCOM '08 Proceedings of the 2008 Sixth Annual IEEE International Conference on Pervasive Computing and Communications
Sensor selection for energy-efficient ambulatory medical monitoring
Proceedings of the 7th international conference on Mobile systems, applications, and services
Journal of Signal Processing Systems
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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.