An FPGA Prototype for Parkinson’s Disease Detection Using Machine Learning on Voice Signal
Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-inv...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11008599/ |
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| Summary: | Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms — Chi-squared (<inline-formula> <tex-math notation="LaTeX">$\chi ^{2}$ </tex-math></inline-formula>), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) — and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. The Quadratic SVM, optimized for minimal power consumption, is implemented on a Nexys A7 FPGA. This setup achieves a dynamic power consumption of just 23 mW and delivers a performance acceleration of <inline-formula> <tex-math notation="LaTeX">$155 \times $ </tex-math></inline-formula> compared to equivalent computations on a 6th-generation Intel i5 processor. By combining a high-accuracy machine learning model with an energy-efficient FPGA implementation, our approach offers a powerful and portable solution for real-time PD detection. |
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| ISSN: | 2169-3536 |