Table 13 Summary of PD detection studies that used the same publicly available PD datasets.

From: Detection of Parkinson’s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques

References

FE methods

Classifier(s)

Dataset

Classification

Accuracy (%)

27

CNN + LSTM

UNM

Off-PD versus On-PD

99.2

29

Gabor transformation + 2D-CNN

SanDiego

Off-PD vs. HC

On-PD vs. HC

Off-PD vs. on-PD

99.44

98.84

92.60

30

Smoothed pseudo-Wigner Ville distribution + CNN

SanDiego

Off-PD vs. HC

On-PD vs. HC

99.84

100.0

33

PSD

Hyperplanes

UNM

Off-PD versus HC

85.40

34

WT + statistical measures

SVM

SanDiego

Off-PD versus HC

On-PD versus HC

96.13

97.65

35

CSP + LogEn

KNN

UNM

(close/open)

Off-PD versus HC

On-PD versus HC

Off-PD versus on-PD

98.81/99.01

98.77/98.85

98.73/98.97

SVM

KNN

KNN

SanDiego

Off-PD versus HC

On-PD versus HC

Off-PD versus on-PD

99.41

95.76

97.52

Present study

DWT + TShEn

DWT + ThEn

DWT + TShEn

SVM

KNN

SVM

UNM (close/open)

Off-PD versus HC

On-PD versus HC

Off-PD versus on-PD

99.51/98.62

99.52/99.20

99.39/98.79

DWT + TShEn

DWT + TShEn

DWT + ThEn

DWT + SuEn

DWT + TShEn

KNN

SanDiego

On-PD versus HC

Off-PD versus on-PD

Off-PD versus HC

Off-PD versus HC

Off-PD versus HC

94.21

98.84

99.72

99.66

99.89