Expectation-Maximization Extreme Machine Learning Classifier for Epileptic Seizure Detection

Jaffino, G.; Jose, J. Prabin; Kumar, K. Sangeet

doi:10.1109/fabs52071.2021.9702627

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…Automatic detection of nonconvulsive status epilepticus (NCSE) can be implemented, which presents a major challenge for physicians in detection. [5] Early detection of focal seizure is detected using microelectrode. The microelectrode records the Intra-cortical neural signals.…”

Section: Literature Surveymentioning

confidence: 99%

Alert System for Epileptic Seizure

Elma¹,

Nithiyashree²,

Savitha³

et al. 2022

Preprint

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Covid 19 has given way to a disruptive and transformative digital automation in health sector worldwide with multi fold investments for upgradation. Benefits could extend across health management with more innovations. Epilepsy, the fourth most common chronic neurological disorder, characterized by recurrent involuntary seizures affects everyone. Worldwide, 70 million people are affected, including 12 million in India, about one-sixth of the global burden. Epilepsy is unpredictable with violent seizures leading to sudden fluctuations in heart rate and involuntary muscle movements leading to death. Therefore, prompt medical attention is essential. Collectively, epilepsy has an effect on the quality of life, career development, and social behaviour, due to an imbalance of the patient’s nervous system. Pre-Covid epilepsy management is largely traditional with little advancement in smartwatches, apps like Apple smartwatches, Android apps, and more. expensive. Covid's new normal has led many to develop cost-effective digital health technology using existing smartphones with smartwatches built in. Epilepsy management systems can also be made affordable. We propose to take advantage of current advancements in smartwatches and smartphone apps by integrating them and adding new features such as video camera activation, photos and automation in sending alerts to, friends and care takers.

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Section: Literature Surveymentioning

confidence: 99%