Early detection of epilepsy using EEG signals

Kumar, Saurabh; Ajitha, L.

doi:10.1109/iccicct.2014.6993198

Cited by 13 publications

(3 citation statements)

References 11 publications

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“…However higher order, that is third-order statistics is also famous due to more complete characterisation of the signal. Higher-order spectra are also having the ability to detect non-linearity in the signals [17][18][19]. Signal entropy is also a measure of signal randomness, like noise and periodic signal having high and low entropies, respectively.…”

Section: Motivationmentioning

confidence: 99%

Scaled correlation analysis of electroencephalography: a new measure of signal influence

Ahirwal

Kumar

Singh

et al. 2016

IET Science, Measurement & Technology

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Section: Motivationmentioning

confidence: 99%

Scaled correlation analysis of electroencephalography: a new measure of signal influence

Ahirwal

Kumar

Singh

et al. 2016

IET Science, Measurement & Technology

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“…It measures the electrical potential detected by each electrode. EEG can be used in monitoring the brain during anesthesia [ 28 ], surgical procedures [ 29 ], and investigations of brain disorders (psychoses [ 30 ], meningoencephalitis [ 31 ], Parkinson [ 9 ], Alzheimer [ 32 , 33 , 34 , 35 , 36 , 37 ], dementia [ 38 ], epilepsy [ 39 , 40 , 41 , 42 ], central motor neuron syndrome [ 43 ], cerebral palsy [ 44 , 45 , 46 ], and muscular dystrophy [ 47 ]). Mainly, EEG systems are used to diagnose and monitor patients with neuropathology, especially in diagnosis of epilepsy and in studying the seizures, as well as the monitoring of treatment and evolution.…”

Section: Introductionmentioning

confidence: 99%

“…Manifestations consist of involuntary movements of different body segments and abnormal neuro-vegetative sensations in the body. EEG analysis can be used to diagnose and monitor the patient in various stages of the disease (focal or generalized seizures, sleep) [ 38 , 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks

Aileni

Pasca

Florescu

2020

Sensors

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Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.

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