Journal article
Automated Seizure Detection Based on State-Space Model Identification
Sensors (Basel, Switzerland), v 24(6), p1902
16 Mar 2024
PMID: 38544166
Abstract
In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.
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Details
- Title
- Automated Seizure Detection Based on State-Space Model Identification
- Creators
- Zhuo Wang - Drexel UniversityMichael R Sperling - Thomas Jefferson UniversityDale Wyeth - Jefferson Hospital for NeuroscienceAllon Guez - Drexel University
- Publication Details
- Sensors (Basel, Switzerland), v 24(6), p1902
- Publisher
- MDPI
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:001192511400001
- Scopus ID
- 2-s2.0-85189026518
- Other Identifier
- 991021864466504721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation