Intelligent Epileptic Seizure detection methods using Biosignals
Seizure detection based on EEG, ECG and MEMS Accelerometer signals using Supervised and Unsupervised learning methods
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There is a worldwide concern about the mounting number of people with epilepsy. Globally around 50 million people are affected by seizure which is epileptic in nature. The epileptic seizure can be detected using EEG signals. For proper diagnosis of epileptic seizure, long hours of EEG signals have to be acquired from the affected persons and have to be reviewed by the doctor which proves to be strenuous. The heart rate variability aids in indicating the occurrence of a seizure. A seizure can also be detected using MEMS sensor which captures the most common external manifestations such as twitc...
There is a worldwide concern about the mounting number of people with epilepsy. Globally around 50 million people are affected by seizure which is epileptic in nature. The epileptic seizure can be detected using EEG signals. For proper diagnosis of epileptic seizure, long hours of EEG signals have to be acquired from the affected persons and have to be reviewed by the doctor which proves to be strenuous. The heart rate variability aids in indicating the occurrence of a seizure. A seizure can also be detected using MEMS sensor which captures the most common external manifestations such as twitching of muscles and convulsions. In this book, EEG signals, heart rate variability and MEMS accelerometer signals are analyzed for the purpose of seizure detection.
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