A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
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
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
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
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
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Design of a wireless sensor network platform for detecting rare, random, and ephemeral events
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Operating System Modifications for Task-Based Speed and Voltage
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Wireless wakeups revisited: energy management for voip over wi-fi smartphones
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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
Reducing energy consumption of multi-channel mobile medical monitoring algorithms
Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments
Journal of Artificial Intelligence Research
Cooperative transit tracking using smart-phones
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EasiCPRS: design and implementation of a portable Chinese pulse-wave retrieval system
Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
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Epilepsy affects over three million Americans of all ages. Despite recent advances, more than 20% of individuals with epilepsy never achieve adequate control of their seizures. The use of a small, portable, non-invasive seizure monitor could benefit these individuals tremendously. However, in order for such a device to be suitable for long-term wear, it must be both comfortable and lightweight. Typical state-of-the-art non-invasive seizure onset detection algorithms require 21 scalp electrodes to be placed on the head. These electrodes are used to generate 18 data streams, called channels. The large number of electrodes is inconvenient for the patient and processing 18 channels can consume a considerable amount of energy, a problem for a battery-powered device. In this paper, we describe an automated way to construct detectors that use fewer channels, and thus fewer electrodes. Starting from an existing technique for constructing 18 channel patient-specific detectors, we use machine learning to automatically construct reduced channel detectors. We evaluate our algorithm on data from 16 patients used in an earlier study. On average, our algorithm reduced the number of channels from 18 to 4.6 while decreasing the mean fraction of seizure onsets detected from 99% to 97%. For 12 out of the 16 patients, there was no degradation in the detection rate. While the average detection latency increased from 7.8 s to 11.2 s, the average rate of false alarms per hour decreased from 0.35 to 0.19. We also describe a prototype implementation of a single channel EEG monitoring device built using off-the-shelf components, and use this implementation to derive an energy consumption model. Using fewer channels reduced the average energy consumption by 69%, which amounts to a 3.3x increase in battery lifetime. Finally, we show how additional energy savings can be realized by using a low-power screening detector to rule out segments of data that are obviously not seizures. Though this technique does not reduce the number of electrodes needed, it does reduce the energy consumption by an additional 16%.