Seizure Detection: A Low Computational Effective Approach without Classification Methods

Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpreta...

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Vydáno v:Sensors (Basel, Switzerland) Ročník 22; číslo 21; s. 8444
Hlavní autoři: Sreenivasan, Neethu, Gargiulo, Gaetano D., Gunawardana, Upul, Naik, Ganesh, Nikpour, Armin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2022
MDPI
Témata:
Algorithms
Analysis
Biomarkers
Brain damage
Classification
Convulsions & seizures
Datasets
Detectors
Discriminant analysis
EEG
Electroencephalography
Epilepsy
feature identification
Innovations
low computation method
Machine learning
Nervous system diseases
Patients
Pediatrics
Physiology
seizure detection
Seizures (Medicine)
Wavelet transforms
Australia
ISSN:1424-8220, 1424-8220
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Shrnutí:Epilepsy is a severe neurological disorder that is usually diagnosed by using an electroencephalogram (EEG). However, EEG signals are complex, nonlinear, and dynamic, thus generating large amounts of data polluted by many artefacts, lowering the signal-to-noise ratio, and hampering expert interpretation. The traditional seizure-detection method of professional review of long-term EEG signals is an expensive, time-consuming, and challenging task. To reduce the complexity and cost of the task, researchers have developed several seizure-detection approaches, primarily focusing on classification systems and spectral feature extraction. While these methods can achieve high/optimal performances, the system may require retraining and following up with the feature extraction for each new patient, thus making it impractical for real-world applications. Herein, we present a straightforward manual/automated detection system based on the simple seizure feature amplification analysis to minimize these practical difficulties. Our algorithm (a simplified version is available as additional material), borrowing from the telecommunication discipline, treats the seizure as the carrier of information and tunes filters to this specific bandwidth, yielding a viable, computationally inexpensive solution. Manual tests gave 93% sensitivity and 96% specificity at a false detection rate of 0.04/h. Automated analyses showed 88% and 97% sensitivity and specificity, respectively. Moreover, our proposed method can accurately detect seizure locations within the brain. In summary, the proposed method has excellent potential, does not require training on new patient data, and can aid in the localization of seizure focus/origin.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22218444