Classification of Epileptic Seizure Using Machine Learning and Deep Learning Based on Electroencephalography (EEG)

Tawfik, Mohammed; Mahyoub, Ezzaldden; Ahmed, Zeyad A. T.; Al-Zidi, Nasser M.; Nimbhore, Sunil

doi:10.1007/978-981-19-2130-8_15

Cited by 3 publications

(1 citation statement)

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“…This outcome underscores the efficacy of the neural network in accurately discerning Parkinson's disease cases and highlights its potential as a reliable tool for healthcare professionals in the diagnostic process. The findings contribute valuable insights into refining and optimizing classification techniques, paving the way for enhanced diagnostic precision and ultimately improving patient outcomes in the context of Parkinson's disease identification 21 . Author implemented a successful approach in Parkinson's disease identification by utilizing a deep belief network (DBN).…”

Section: Related Workmentioning

confidence: 96%

Detection of Parkinson disease using multiclass machine learning approach

Srinivasan,

Ramadass,

Mathivanan

et al. 2024

Sci Rep

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Parkinson’s Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80–20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson’s Disease.

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Section: Related Workmentioning

confidence: 96%