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

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 113-118).Instrumenting patients with small, wearable sensors will enable physicians to continuously monitor patients outside the hospital. These devices can be used for real-time classification of the data they collect. For practical purposes, such devices must be comfortable and thus be powered by small batteries. Since classification algorithms often perform energy-intensive signal analysis, power management techniques are needed to achieve reasonable battery lifetimes. In this thesis, we describe software-based methods that reduce the computation, and thus, energy consumption of real-time medical monitoring algorithms by examining less data. Though discarding data can degrade classification performance, we show that the degradation can be small. We describe and evaluate data reduction methods based on duty cycling, sensor selection, and combinations of the two. Random duty cycling was applied to an online algorithm that performs risk assessment of patients with a recent acute coronary syndrome (ACS). We modified an existing algorithm that estimates the risk of cardiovascular death following ACS. By randomly discarding roughly 40% of the data, we reduced energy consumption by 40%. The percentage of patients who had a change in their risk classification was 3%. A sensor selection method was used to modify an existing machine learning based algorithm for constructing multi-channel, patient-specific, delay-sensitive seizure onset detectors.(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.by Eugene Inghaw Shih.Ph.D

Sensor selection for energy-efficient ambulatory medical monitoring

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%

An energy-efficient radio for wireless microsensor networks

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 139-142).by Eugene Inghaw Shih.S.M