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A two-stage algorithm to detect electrographic seizures using a wearable single-channel EEG sensor

Year: 2025

Type: preprint

Abstract: This paper presents a two-stage machine learning model for electrographic seizure detection using wearable single-channel scalp electroencephalogram (EEG) sensors. Methods: 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. Results: 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 ± 5.9% to 75 ± 6.6% while reducing the FAR from 3.3 ± 0.3/hr to 2.4 ± 0.3/hr. Conclusion: 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. Significance: 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 longduration seizure monitoring using wearable single-channel EEG sensors during activities of daily life.

DOI: 10.36227/techrxiv.176101848.82595530/v1

Authors: Shini Renjith, Karthik Gopalakrishnan, Tobias Loddenkemper, Daniel Friedman, Mark Spitz, Mitchell A Frankel, Mark J. Lehmkuhle, V John Mathews

Institution: Oregon State University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Neurological Disorders and Stroke

Award: Automated Seizure Detection for Home Seizure Monitoring with Epilog Sensors

OpenAlex ID: W4415388298

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