# Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials

## 3. Methods

#### 3.1. Feature Computing and Selection

#### 3.2. Pattern Analysis with Semi-Supervised Competitive Learning

#### 3.2.1. Competitive Selection of Initial Prototype Seeds

#### 3.2.2. K-Means Optimization Algorithm

#### 3.2.3. Nearest Neighbor Classification

#### 3.3. Benchmark Classifiers for Comparison

#### 3.4. Classification Performance Evaluation Metrics

#### 3.4.1. Accuracy

#### 3.4.2. Recall

#### 3.4.3. Specificity

#### 3.4.4. Precision

#### 3.4.5. F-Score

#### 3.4.6. Matthews Correlation Coefficient

#### 3.4.7. Area under ROC Curve

#### 3.4.8. Kappa Coefficient

## 4. Results and Discussions

#### 4.1. Feature Analysis Results

#### 4.2. Classification Results and Discussions

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

APQ | Amplitude Perturbation Quotient |

AUC | Area Under the receiver operating characteristic Curve |

DFA | Detrended Fluctuation Analysis |

KNN | K-Nearest Neighbor |

GNE | Glottal-to-Noise excitation Ratio |

HC | Healthy controls |

HNR | Harmonic-to-Noise Ratio |

MCC | Matthews Correlation Coefficient |

MFCC | Mel-Frequency Cepstral Coefficient |

PCA | Principal Component Analysis |

PD | Parkinson’s Disease |

PPE | Pitch Period Entropy |

ROC | Receiver Operating Characteristic curve |

RPDE | Recurrence Period Density Entropy |

SE | Standard Error |

SSCL | Semi-Supervised Competitive Learning algorithm |

SVM | Support Vector Machine |

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**Figure 1.**Flowchart of the voice detection procedures that contain vocal parameter analysis, dimensionality reduction, feature selection using the Mann–Whitney–Wilcoxon hypothesis test, pattern analysis based on semi-supervised competitive learning, and classification result evaluation.

**Figure 2.**Pearson correlation coefficient results of the families of (

**a**) Jitter, (

**b**) Shimmer, (

**c**) HNR, (

**d**) Nonlinear, (

**e**) MFCC, and (

**f**) Frequency Delta vocal parameters.

**Figure 3.**ROC curves generated by the SVM with radial basis functions, KNN, and SSCL methods, respectively. The AUC values estimated by the SVM, KNN, and SSCL methods were 0.868 ± 0.043, 0.855 ± 0.029, and 0.939 ± 0.018, respectively.

Subject Groups | HC Group | PD Group | ||
---|---|---|---|---|

Gender | Male | Female | Male | Female |

n (%) | 22 (55%) | 18 (45%) | 27 (67.5%) | 13 (32.5%) |

Age (years old) | 66.38 ± 8.38 | 69.58 ± 7.82 |

**Table 2.**Description of acoustic parameter families derived from the voice records in HC and PD subject groups.

Parameter Family | Abbreviation | Parameter Description |
---|---|---|

Jitter | Jitter-Rel | Relative jitter |

Jitter-Abs | Absolute jitter | |

Jitter-RAP | Relative average perturbation | |

Jitter-PPQ | Pitch perturbation quotient | |

Shimmer | Shim-Loc | Local shimmer |

Shim-dB | Shimmer in dB | |

Shim-APQ3 | 3-point amplitude perturbation quotient | |

Shim-APQ5 | 5-point amplitude perturbation quotient | |

Shim-APQ11 | 11-point amplitude perturbation quotient | |

Harmonic-to-noise | HNR05 | Harmonic-to-noise ratio in 0–500 Hz |

HNR15 | Harmonic-to-noise ratio in 0–1500 Hz | |

HNR25 | Harmonic-to-noise ratio in 0–2500 Hz | |

HNR35 | Harmonic-to-noise ratio in 0–3500 Hz | |

HNR38 | Harmonic-to-noise ratio in 0–3800 Hz | |

Nonlinear | RPDE | Recurrence period density entropy |

DFA | Detrended fluctuation analysis | |

PPE | Pitch period entropy | |

GNE | Glottal-to-noise excitation ratio | |

Frequency | MFCC 0 to 12 | Mel-frequency cepstral coefficient-based spectral measures of order 0–12 |

Delta 0 to 12 | The derivatives of mel-frequency cepstral coefficient measures of order 0–12 |

**Table 3.**Mann–Whitney–Wilcoxon hypothesis test results of the vocal features derived from the PCA approach. The p-value < 0.05 indicates the significant difference, marked with *. Null hypothesis: Data samples from two subject groups are not significantly different in statistical sense; 1: rejects the null hypothesis, with the corresponding p-value marked with stars, 0: accepts the null hypothesis.

Vocal Features | Null Hypothesis | p-Value |
---|---|---|

Jitter-PCA | 1 | 0.0036 * |

Shimmer-PCA | 1 | 0.0007 * |

HNR-PCA | 1 | 0.0001 * |

Nonlinear-RPDE | 0 | 0.1779 |

Nonlinear-DFA | 0 | 0.3233 |

Nonlinear-PPE | 1 | 0.0476 * |

Nonlinear-GNE | 1 | 0.0001 * |

Frequency-MFCC-PCA1 | 1 | 0.0001 * |

Frequency-MFCC-PCA2 | 0 | 0.2305 |

Frequency-MFCC-PCA3 | 0 | 0.2926 |

Frequency-MFCC-PCA4 | 0 | 0.4885 |

Frequency-MFCC-PCA5 | 0 | 0.2856 |

Frequency-MFCC-PCA6 | 0 | 0.2952 |

Frequency-Delta-PCA1 | 1 | 0.0001 * |

Frequency-Delta-PCA2 | 0 | 0.1530 |

Frequency-Delta-PCA3 | 0 | 0.0579 |

Frequency-Delta-PCA4 | 0 | 0.1624 |

Frequency-Delta-PCA5 | 1 | 0.0369 * |

**Table 4.**Classification results of vocal patterns of HC subjects and PD patients. N/A: Not applicable.

ClassificationMetrics | Methods | ||||
---|---|---|---|---|---|

Bayesian Expert System [36] | Two-Stage Method [17] | KNN ($\mathit{K}=7$) | SVM | SSCL | |

Accuracy ± SD | 0.752 ± 0.086 | 0.779 ± 0.08 | 0.806 ± 0.031 | 0.825 ± 0.03 | 0.838 ± 0.029 |

Recall ± SD | 0.718 ± 0.132 | 0.765 ± 0.135 | 0.812 ± 0.044 | 0.8 ± 0.045 | 0.825 ± 0.042 |

Specificity ± SD | 0.786 ± 0.135 | 0.792 ± 0.15 | 0.8 ± 0.045 | 0.85 ± 0.04 | 0.85 ± 0.04 |

Precision ± SD | 0.785 ± 0.118 | 0.806 ± 0.115 | 0.802 ± 0.044 | 0.842 ± 0.042 | 0.846 ± 0.041 |

F-score ± SD | 0.75 ± 0.024 | 0.785 ± 0.022 | 0.807 ± 0.012 | 0.821 ± 0.011 | 0.835 ± 0.011 |

MCC ± SD | 0.505 ± 0.096 | 0.557 ± 0.089 | 0.613 ± 0.049 | 0.651 ± 0.046 | 0.675 ± 0.043 |

AUC ± SD | N/A | 0.879 ± 0.067 | 0.855 ± 0.029 | 0.868 ± 0.043 | 0.939 ± 0.018 |

Kappa ± SD | N/A | N/A | 0.613 ± 0.062 | 0.65 ± 0.06 | 0.675 ± 0.058 |

**Table 5.**Summary of the misclassified voice records in percentage and their corresponding subject group and gender information.

Subject Group | KNN | SVM | SSCL | |||
---|---|---|---|---|---|---|

Male | Female | Male | Female | Male | Female | |

HC | 25.8% | 25.8% | 25% | 17.9% | 30.8% | 15.4% |

PD | 38.7% | 9.7% | 46.4% | 10.7% | 53.8% | 0% |

Total | 64.5% | 35.5% | 71.4% | 28.6% | 84.6% | 15.4% |

100% | 100% | 100% |

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**MDPI and ACS Style**

Bao, G.; Lin, M.; Sang, X.; Hou, Y.; Liu, Y.; Wu, Y.
Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm. *Biosensors* **2022**, *12*, 502.
https://doi.org/10.3390/bios12070502

**AMA Style**

Bao G, Lin M, Sang X, Hou Y, Liu Y, Wu Y.
Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm. *Biosensors*. 2022; 12(7):502.
https://doi.org/10.3390/bios12070502

**Chicago/Turabian Style**

Bao, Guidong, Mengchen Lin, Xiaoqian Sang, Yangcan Hou, Yixuan Liu, and Yunfeng Wu.
2022. "Classification of Dysphonic Voices in Parkinson’s Disease with Semi-Supervised Competitive Learning Algorithm" *Biosensors* 12, no. 7: 502.
https://doi.org/10.3390/bios12070502