A two-stage algorithm to detect electrographically focal seizures using a wearable single-channel EEG sensor.

Abstract

This paper presents a two-stage machine learning model for electrographic seizure detection using wearable single-channel scalp electroencephalogram (EEG) sensors. The algorithm first detects seizure in short, nonoverlapping segments. The binary decisions made by Stage-I as ictals are fed to Stage-II with the goal of reducing the false alert rate (FAR). A post-processing framework is applied to the segment-level binary results to create event-level decisions. The performance of the two-stage system for detecting electrographically focal seizures was evaluated on EEGs recorded in a multi-center study. The two-stage algorithm exhibited increased sensitivity and reduced FAR when compared to singlestage models. For example, a two-stage model employing a balanced bagging classifier for Stage-I and a gradient boosting classifier for Stage-II improved the sensitivity of seizure detection from 61 $\boldsymbol{\pm }$ 5.9% to 75 $\boldsymbol{\pm }$ 6.6% while reducing the FAR from 3.3 $\boldsymbol{\pm }$ 0.3/hr to 2.4 $\boldsymbol{\pm }$ 0.3/hr. The two-stage algorithm of this paper exhibited statistically significant performance improvement in detecting electrographically focal seizures over single-stage approaches. In addition, adding memory at the input of Stage-I and incorporating an iterative learning algorithm in Stage-I statistically significantly improved the performance of the first stage. The performance of the two-stage method for single-channel seizure detection suggests its potential to enhance support systems used by epileptologists for post-hoc reviews. This system may represent the beginning of the roadmap for long-duration seizure monitoring using wearable single-channel EEG sensors during activities of daily life.