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Epilepsy Seizure Detection
Epilepsy Seizure Detection
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80,39 €
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Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. It is characterized by recurrent, involuntary, paroxysmal seizure activity. Epilepsy is the result of a sudden disturbance of brain function, characterized by asynchronous firing in cerebral neurons. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence…
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Epilepsy Seizure Detection (e-book) (used book) | bookbook.eu

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Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. It is characterized by recurrent, involuntary, paroxysmal seizure activity. Epilepsy is the result of a sudden disturbance of brain function, characterized by asynchronous firing in cerebral neurons. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence physical injury may occur. Proper health care must be provided to the patients and this can be achieved only if the seizures are detected correctly in time. In this work, a system is designed using wavelet decomposition method and different training algorithms to train the network for classification of the EEG signals.

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Epilepsy is a perilous neurological disease covering about 4-5% of total population of the world. Its main characteristics are seizures which occur due to certain disturbance in brain function. It is characterized by recurrent, involuntary, paroxysmal seizure activity. Epilepsy is the result of a sudden disturbance of brain function, characterized by asynchronous firing in cerebral neurons. During epileptic seizures the patient is unaware of their physical as well as mental condition and hence physical injury may occur. Proper health care must be provided to the patients and this can be achieved only if the seizures are detected correctly in time. In this work, a system is designed using wavelet decomposition method and different training algorithms to train the network for classification of the EEG signals.

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