STTR Phase I: Real-time Automatic Analysis of Electroencephalograms in an Intensive Care Environment Using Deep Learning

Period of Performance: 07/01/2016 - 06/30/2017

$224K

Phase 1 STTR

Recipient Firm

BioSignal Analytics, Inc
3711 Market St Ste 800
Philadelphia, PA 19104
Firm POC, Principal Investigator

Research Institution

Temple University
Dept. of Computer & Informatio 1805 N. Broad St
Philadelphia, PA 19122
Institution POC

Abstract

The broader impact / commercial potential of this Small Business Technology Transfer Phase I project is enabling real-time seizure detection in intensive care units ? especially units at hospitals without 24/7 neurologist coverage to interpret scans in a timely manner. High performance real-time detection of critical EEG events in an ICU setting will increase the use of brain monitoring in critical care, thereby improving patient outcomes, increasing the efficiency of healthcare and decreasing the cognitive burden placed on caregivers. Current approaches to automatic detection suffer from unacceptably high false alarm rates that overwhelm care providers, and are of limited use in this environment. A reliable service would expand access to quality care for 877,500 neurologically compromised critical care patients in 4,000+ community hospitals in the United States. The market opportunity for real-time seizure detection in the ICU is approximately $80M per year. The proposed project will develop an assistive technology for EEG analysis to support clinicians in evaluating EEG signals for medically important events in an ICU environment. Analysis of EEG signals requires a highly trained neurologist, and is time consuming and expensive since identifying rare clinical events requires analysis of long data streams. Most community hospitals do not have 24/7 access to trained neurologists and can not provide continuous EEG monitoring to detect non-convulsive seizures in neurologically compromised patients. Reliable automatic detection improves patient access to long-term brain monitoring by auto-scanning EEG signals and flagging sections of the signal that need further review by a clinician. The tool reduces the amount of data needing manual review by two orders of magnitude, offering substantial productivity gains in a clinical setting. The project will leverage an innovative approach for integrating hidden Markov models, deep learning and active learning to allow the rapid development of a high performance machine learning system from minimal amounts of manually annotated data. The resulting automatic analysis will achieve 95% detection accuracy for seizures with a false alarm rate of 1 per 8-hour period.