Reducing the computational demands of medical monitoring classifiers by examining less data

[摘要] (cont.) Using this method, we automatically generated detectors that used roughly 60% fewer channels than the original detector. The reduced channel detectors missed seven seizures out of 143 total seizures while the original detector missed four. The median detection latency increased slightly from 6.0 to 7.0 seconds, while the average false alarms per hour increased from 0.07 to 0.11. Finally, we investigated the impact of approaches that combine duty cycling with sensor selection on the energy consumption and detection performance of the seizure onset detection algorithm. In one approach, where we combined two reduced channel detectors to form a single detector, we reduced energy consumption by an additional 20% over the reduced channel detectors.

[发布机构] Massachusetts Institute of Technology