Key result
EEG machine learning algorithms achieve up to ~97% accuracy in detecting Parkinson's disease.
Why the study?
Parkinson's Disease is a prominent cause of demise in adults over 60 and relies on clinical evaluation, motivating the use of EEG signal analysis with machine learning and deep learning for early-stage discovery.
Can machine learning and deep learning techniques applied to EEG signals accurately detect Parkinson's Disease?
Can machine learning and deep learning techniques applied to EEG signals accurately detect Parkinson's Disease?
Machine learning and deep learning algorithms applied to 12-channel EEG signals can detect Parkinson's Disease with 86-97% accuracy, offering a potential cost-effective diagnostic tool.
Supports hypothesis generation for EEG-ML Parkinson's detection; leaves open prospective validation before clinical adoption.
Parkinson's Disease (PD) is the prominent reasons of demise in adults over the age of 60. The disease is centered on clinical evaluation. The leading objective of the proposed work is to use Electroencephalogram (EEG) signal analysis for the discovery of Parkinson's Disease at an early stage with the help of Machine learning and Deep learning techniques. The features were extracted by time-frequency representation from the Mel Spectrogram for each of the 32 EEG channels. With experimental analysis, only 12 channels are used, which covers the significant information for PD identification, so to reduce the computational cost, this data is passed into Machine learning and Deep learning algorithms for example support vector machines (SVM), K-Nearest Neighbors (KNN), Random forests, and Convolutional Neural networks (CNN) to determine the Accuracy of the model. A decent accuracy of classification was received, which was in the range of 86-97%. Thus, we propose a cost-effective and risk-free method of detecting Parkinson's Disease with significant accuracy.
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Nayana et al. (2023) studied Parkinson's Disease. EEG signal analysis with Machine learning and Deep learning techniques was evaluated on Accuracy of the model. EEG signal analysis using machine learning and deep learning algorithms achieved a classification accuracy of 86-97% for detecting Parkinson's Disease.
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