Parkinson’s Disease Classification Framework Using Vocal Dynamics in Connected Speech
Abstract
:1. Introduction
- Efforts to establish an analytical framework that can be automated to classify between PD and HC using vocalic dynamics.
- Providing evidence as to the robustness of the framework for the language being spoken.
- Evaluation of PSFs for PD classification.
- Providing evidence for the shortcomings of using MFCCs for PD classification due to their inherent nature of embedding patient identifiable information.
2. Dataset Description
2.1. Database 1
2.2. Database 2
3. Materials and Methods
3.1. Methodology Block Description
3.1.1. Preprocessing: Block Processing and Pitch Synchronous Segmentation
3.1.2. Feature Extraction: MFCCs and PSFs
3.1.3. Feature Preparation
3.1.4. Validation Split: Hold-Out
3.1.5. Classifier Training
3.2. Experimental Design
3.2.1. Importance of Block Size in Conventional Block Processing
3.2.2. Identification of Optimal Choice for Segmentation Method
3.2.3. Identification of Optimal Choice for Feature Types
3.2.4. Evaluation of Classifiers
3.2.5. Testing Using Different Databases
4. Results and Discussion
- True Positives (TP): Number of PD samples predicted as PD.
- True Negatives (TN): Number of HC samples predicted as HC.
- False Positives (FP): Number of HC samples predicted as PD.
- False Negatives (FN): Number of PD samples predicted as HC.
- Accuracy: Proportion of test samples correctly predicted.
- Precision (P): Proportion of PD predictions that were correct.
- Recall (R): Proportion of all PD samples correctly predicted.
- F1-Score: Harmonic mean of precision and recall.
- Matthews Correlation Coefficient (MCC): An improvement over F1-Score as it includes the TN in its computation.
- ROC-AUC: Area under Receiver Operating Characteristic (ROC) curve.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Name | Size |
---|---|
Pitch Period | 1 |
LOC—Length of Curve | 1 |
Quarter Segment Energy | 4 |
Total Energy | 1 |
Correlation Canceller Efficiency | 1 |
Correlation Canceller MSE | 1 |
Peak Frequency (Hz) | 1 |
Quarter Band Magnitude | 4 |
Spectral Factor | 1 |
Classifier No. | Classifier Name | Overfit Factor with Original Label | |||
---|---|---|---|---|---|
Male | Female | ||||
MFCC | PSF | MFCC | PSF | ||
1 | Medium KNN | −0.003 | 0.021 | 0.023 | 0.022 |
2 | Coarse KNN | −0.012 | 0.017 | 0.014 | 0.003 |
3 | Cosine KNN | 0.005 | 0.015 | 0.016 | 0.021 |
4 | Linear SVM | −0.001 | −0.005 | 0.009 | 0 |
5 | Coarse Tree | 0.012 | 0.037 | 0.017 | −0.001 |
6 | Coarse Gaussian SVM | −0.002 | 0.006 | 0.013 | 0.001 |
7 | Medium Tree | 0.016 | 0.031 | 0.015 | 0.003 |
8 | Ensemble Boosted Tree | 0.018 | 0.026 | 0.025 | 0.004 |
9 | RUS Boosted Tree | 0.016 | 0.031 | 0,015 | 0.003 |
10 | Logistic Regression | 0 | −0.012 | 0.007 | −0.001 |
11 | Fine Tree | 0.039 | 0.03 | 0.061 | 0.008 |
12 | Medium Gaussian SVM | 0.003 | 0.006 | 0.02 | 0.001 |
13 | Fine KNN | 0.035 | 0.057 | 0.109 | 0.111 |
14 | Weighted KNN | 0.026 | 0.055 | 0.099 | 0.1 |
15 | Ensemble Bagged Trees | 0.046 | 0.051 | 0.1 | 0.051 |
16 | Ensemble Subspace KNN | 0.026 | 0.139 | 0.089 | 0.13 |
17 | Fine Gaussian SVM | 0.173 | 0.017 | 0.137 | 0.008 |
Classifier No. | Classifier Name | Median Test Accuracy | |||
---|---|---|---|---|---|
Male | Female | ||||
MFCC | PSF | MFCC | PSF | ||
1 | Medium KNN | 90.18 | 75.77 | 93.73 | 78.725 |
2 | Coarse KNN | 85.33 | 72.17 | 89.11 | 74.855 |
3 | Cosine KNN | 89.46 | 75.965 | 93.14 | 79.31 |
4 | Linear SVM | 67.69 | 58.8 | 80.44 | 53.65 |
5 | Coarse Tree | 66.52 | 59.52 | 77.01 | 69.605 |
6 | Coarse Gaussian SVM | 75.12 | 61.715 | 83.92 | 64.525 |
7 | Medium Tree | 68.83 | 63.09 | 76.81 | 72.405 |
8 | Ensemble Boosted Tree | 74.04 | 67.96 | 82.76 | 75.415 |
9 | RUS Boosted Tree | 68.83 | 63.09 | 76.81 | 72.35 |
10 | Logistic Regression | 67.62 | 59.335 | 79.92 | 59.38 |
11 | Fine Tree | 72.18 | 66.965 | 80.62 | 75.93 |
12 | Medium Gaussian SVM | 86.63 | 70.21 | 90.78 | 75.865 |
13 | Fine KNN | 91.27 | 75.32 | 93.91 | 76.43 |
14 | Weighted KNN | 91.97 | 77.4 | 94.29 | 78.66 |
15 | Ensemble Bagged Trees | 88.67 | 80.98 | 92.39 | 85.26 |
16 | Ensemble Subspace KNN | 90.62 | 66.125 | 94.08 | 73.915 |
17 | Fine Gaussian SVM | 92.14 | 78.415 | 95.86 | 80.62 |
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Appakaya, S.B.; Pratihar, R.; Sankar, R. Parkinson’s Disease Classification Framework Using Vocal Dynamics in Connected Speech. Algorithms 2023, 16, 509. https://doi.org/10.3390/a16110509
Appakaya SB, Pratihar R, Sankar R. Parkinson’s Disease Classification Framework Using Vocal Dynamics in Connected Speech. Algorithms. 2023; 16(11):509. https://doi.org/10.3390/a16110509
Chicago/Turabian StyleAppakaya, Sai Bharadwaj, Ruchira Pratihar, and Ravi Sankar. 2023. "Parkinson’s Disease Classification Framework Using Vocal Dynamics in Connected Speech" Algorithms 16, no. 11: 509. https://doi.org/10.3390/a16110509