Leveraging EEG Signal Analysis and Machine Learning for Early Detection of Parkinson’s Disease

Early diagnosis of Parkinson’s Disease (PD) through proper intervention proves essential because this disorder advances as a progressive neurodegenerative condition. Significant research has used EEG signal analysis with machine learning to find promising solutions although these approaches tend to experience major obstacles including incomplete results and hard implementation of real-time application and excessive incorrect diagnoses. There are current systems which demand difficult system installation procedures and lack straightforward interfaces that prevent their application in medical settings. This paper develops an improved analytical method which applies pre-trained deep learning models to EEG signals for medical use in order to achieve better accuracy and scalability in hospital settings. A user-friendly Streamlit application interfaces with the real-time prediction system through the coupling of the model. The system performs noise reduction by using robust scaling preprocessing before running data through a deep neural network which identifies results in “Parkinson’s” or “Healthy” classes exceeding a 0.8 confidence score threshold to prevent errors. The system demonstrates excellent performance by correctly classifying 96.7% of Parkinson's samples through detection of 44 valid samples while missing one but passing four potential false readings. Through this method the diagnostic process obtains substantial benefits by providing efficient and scalable techniques for non-invasive Parkinson’s Disease early detection.