Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor data.
Comput Biol Med
; 130: 104232, 2021 03.
Article
in En
| MEDLINE
| ID: mdl-33516072
This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video-electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Quality of Life
/
Epilepsy
Type of study:
Diagnostic_studies
/
Prognostic_studies
Aspects:
Patient_preference
Limits:
Adult
/
Female
/
Humans
/
Male
Language:
En
Journal:
Comput Biol Med
Year:
2021
Document type:
Article
Affiliation country:
United States
Country of publication:
United States