A patient-independent classification system for onset detection of seizures
-
Abdul Quaiyum Ansari
and
Manjari Tripathi
Abstract
Seizures are the most common brain dysfunction. Electroencephalography (EEG) is required for their detection and treatment initially. Studies show that if seizures are detected at their early stage, instant and effective treatment can be given to the patients. In this paper, an automated system for seizure onset detection is proposed. As the power spectrum of normal person’s EEG and EEG of someone with epilepsy is plotted, powers present at different frequencies are found to be different for both. The proposed algorithm utilizes this frequency discrimination property of EEG with some statistical features to detect the seizure onset using simple linear classifier. The tests conducted on EEG data of 30 patients, obtained from the two different datasets, show the presence of all 183 seizures with mean latency of 0.9 s and 1.02 false detections per hour. The main contribution of this study is the use of simple features and classifier in the field of seizures onset detection that reduces the computational complexity of the algorithm. Also, the classifier used is patient independent. This patient independency in the classification system would be helpful in the implementation of the proposed algorithm to develop an online detection system.
Research funding: Authors state no funding involved.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Informed consent: Informed consent was obtained from all individuals included in this study.
Ethical approval: In this study, two sets of scalp EEG database have been used. One of the datasets was collected at the All India Institute of Medical Sciences (AIIMS), New Delhi, India, after taking an approval from Ethics committee of AIIMS.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
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- A patient-independent classification system for onset detection of seizures
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Articles in the same Issue
- Frontmatter
- Review
- Surrogate based continuous noninvasive blood pressure measurement
- Research Articles
- Smart automated heart health monitoring using photoplethysmography signal classification
- In vivo evaluation of two adaptive Starling-like control algorithms for left ventricular assist devices
- A patient-independent classification system for onset detection of seizures
- Prediction of salivary cortisol level by electroencephalography features
- Confocal laser microscopy without fluorescent dye in minimal-invasive thoracic surgery: an ex-vivo pilot study in lung cancer
- Spinal cord segmentation and injury detection using a Crow Search-Rider optimization algorithm
- Experimental and numerical investigations of fracture and fatigue behaviour of implant-supported bars with distal extension made of three different materials
- Compression and tension behavior of the prosthetic foam materials polyurethane, EVA, Pelite™ and a combination of polyurethane and EVA: a preliminary study
- Evaluation of a novel stair-climbing transportation aid for emergency medical services
