Classification of EEG signals to detect epilepsy problems

Al-Omar, Sally; Kamali, Walid; Khalil, Mohamad Ali; Daher, Ahmad

doi:10.1109/icabme.2013.6648833

Cited by 6 publications

(2 citation statements)

References 10 publications

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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%

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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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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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Epilepsy analysis using open source EDF tools for information science and data analytics

Gurumoorthy¹,

Muppalaneni

Sekhar³

et al. 2019

Int J Communication

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SummaryElectroencephalogram (EEG) is the signals that measure the electrical variances of brain using metal electrodes. We observe the EEG signals by using European Data Format (EDF) BROWSER and EEG STUDIO. By using EDF BROWSER, we can get the mean and frequency from the filtered output signal using band‐pass filter. Using EDF BROWSER, we can also perform Root Mean Square (RMS) and signal samples. Using EEG STUDIO, we can analyze the average frequency and standard deviation. Epileptic seizure prediction and detection are done by spike detection, frequency domain analysis, and nonlinear methods. EEG signal contains different artifacts like electrooculography (EOG), EKG, and electrocardiogram (ECG). ECG signals are produced by heart. EOG signals are produced by eyes. EMG signals are produced by muscle coordination.

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