May 2024
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Publications (4)
November 2022
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10 Reads
Epileptic seizure is detected by reading the electroencephalogram (EEG) signals which are obtained from the electrical activities of the brain which are containing information about the brain. Epileptic seizure is known as the abrupt abnormal activity of a bunch of neurons which results in an electric surge in the brain. India is also one of the countries on the globe which is having about 10 million people suffering from a seizure. In this paper, the combination of discrete wavelet transform along with power spectral density is proposed for the classification and feature extraction process to detect epileptic seizures. To achieve high accuracy of seizure detection rate and explore relevant knowledge from the EEG processed dataset, deep learning has been used. The result shows that the detection of epileptic seizures using the proposed method gives an accuracy of 90.1%. This system would be useful for clinical analysis of epileptic seizures, and appropriate action would be taken against epileptic seizures. KeywordsEEGDWTEpilepsySeizure
March 2022
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180 Reads
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15 Citations
Journal of Healthcare Engineering
One of the most common neurological disorders is epilepsy, which disturbs the nerve cell activity in the brain, causing seizures. Electroencephalography (EEG) signals are used to detect epilepsy and are considered standard techniques to diagnose epilepsy conditions. EEG monitors and records the brain activity of epilepsy patients, and these recordings are used in the diagnosis of epilepsy. However, extracting the information from the EEG recordings manually for detecting epileptic seizures is a difficult cumbersome, error-prone, and labor-intensive task. These negative attributes of the manual process increase the demand for implementing an automated model for the seizure detection process, which can classify seizure and nonseizures from EEG signals to help in the timely identification of epilepsy. Recently, deep learning (DL) and machine learning (ML) techniques have been used in the automatic detection of epileptic seizures because of their superior classification abilities. ML and DL algorithms can accurately classify different seizure conditions from large-scale EEG data and provide appropriate results for neurologists. This work presents a feature extraction-based convolutional neural network (CNN) to sense and classify different types of epileptic seizures from EEG signals. Different features are analyzed to classify seizures via EEG signals. Simulation analysis was managed to investigate the classification performance of the hybrid CNN-RNN model in terms of different achievement metrics such as accuracy, precision, recall, f1 score, and false-positive rate. The results validate the efficacy of the CNN-RNN model for seizure detection.
January 2021
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59 Reads
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4 Citations
Electroencephalography (EEG), one of the most effective method for analyzing the electrical activities of brain. It is used widely in diagnosis of brain-related diseases. There are many disorders of brain which are discussed in this paper. The datasets and the number of subjects which were included in these were also reviewed. Review of various research papers and articles published in refereed peer-reviewed international journals in the domain of neurological science is also taken into consideration. A review has been developed that emphasized on the neurological disorders and role of electroencephalography in their detection. Role of electroencephalography is also studied for diagnosing the disorders of brain.
Citations (2)
... In recent years, the advancement of deep learning technology has showcased strong generalization and adaptive learning capabilities in the realm of epilepsy research, thereby becoming the leading method in the field of seizure detection and classification. Most studies typically utilize conventional deep learning models, such as recurrent neural networks (RNNs) (Tsiouris et al. and Usman et al. [10,11]) and convolutional neural networks (CNNs) (Saab et al.; Ahmedt-Aristizabal et al. and Dhar et al. [8,9,19]). However, these methods have certain limitations. ...
- Citing Article
- Full-text available
March 2022
Journal of Healthcare Engineering
... In patients with head injury who have fallen into a coma, EEG is of paramount importance in the assessment of brain responsiveness and the quantification of damage [9]. It may be used in the determination of early abnormal loci within the brain, loci which may be reflecting the onset of ailments such as epilepsy, dementia, and Alzheimer's [10,11]. ...
- Citing Chapter
January 2021