Research Article
A Multiple-Classifier Framework for Parkinson’s Disease Detection Based on Various Vocal Tests
Table 1
Time-frequency based features presented in Parkinson speech dataset with multiple types of sound recordings [
13].
| Feature number | Feature name | Group |
| 1 | Jitter (local) | Frequency parameter | 2 | Jitter (local, absolute) | 3 | Jitter (rap) | 4 | Jitter (ppq5) | 5 | Jitter (ddp) |
| 6 | Number of pulses | Pulse parameters | 7 | Number of periods | 8 | Mean period | 9 | Standard deviation of period |
| 10 | Shimmer (local) | Amplitude parameters | 11 | Shimmer (local, dB) | 12 | Shimmer (apq3) | 13 | Shimmer (apq5) | 14 | Shimmer (apq11) | 15 | Shimmer (dda) |
| 16 | Fraction of locally unvoiced frames | Voicing parameters | 17 | Number of voice breaks | 18 | Degree of voice breaks |
| 19 | Median pitch | Pitch parameters | 20 | Mean pitch | 21 | Standard deviation | 22 | Minimum pitch | 23 | Maximum pitch |
| 24 | Autocorrelation | Harmonicity parameters | 25 | Noise-to-harmonic | 26 | Harmonic-to-noise |
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