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Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges

Sheerin Zadoo
Sheerin Zadoo
, 
Yashwant Singh
Yashwant Singh
 et 
Pradeep Kumar Singh
Pradeep Kumar Singh
   | 12 mars 2024
International Journal on Smart Sensing and Intelligent Systems's Cover Image
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Article Category: Article
Publié en ligne: 12 mars 2024
Pages: -
Reçu: 31 oct. 2023
DOI: https://doi.org/10.2478/ijssis-2024-0008
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© 2024 Sheerin Zadoo et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1:

Parkinson's disease symptoms [4, 33, 34].
Parkinson's disease symptoms [4, 33, 34].

Figure 2:

Roadmap of the proposed review.
Roadmap of the proposed review.

Figure 3:

AI-based PsD detection techniques.
AI-based PsD detection techniques.

Figure 4:

Comparison of the highest accuracies achieved in various modalities in PsD detection.
Comparison of the highest accuracies achieved in various modalities in PsD detection.

Figure 5:

Process flow diagram of PsD detection using EI.
Process flow diagram of PsD detection using EI.

Figure 6:

Taxonomy for detection of Parkinson's disease according to different modalities [42, 48].
Taxonomy for detection of Parkinson's disease according to different modalities [42, 48].

Comparative analysis of techniques used to detect PsD using MRI images.

Author (s) Year Purpose Algorithm Dataset Output Merits Demerits
Sangeetha et al. [62] 2023 PsD Diagnosis CNN

PPMI dataset

PsD: 229

HC: 229

 		Accuracy = 96% Preprocessing done and various performance metrics considered -
Cui et al. [61] 2022 Classification of PsD and HC Resnet18 + Support Vector Machine PPMI dataset Accuracy = 98.66% Feature fusion improves classification performance -
Monte-Rubio et al. [63] 2022 Parameters from multiple sites to harmonize MRI clinical data for classification of PsD Weighted HARMonization Parameters (WHARMPA)

PPMI dataset

PsD: 216

HC: 87

 		Balanced accuracy = 78.60%; AUC = 0.90 WHARMPA encodes global site-effects quantitatively with simple implementation Unbalanced dataset used,
Hathaliya et al. [64] 2021 To classify PsD and HC patients Stacked ML model PPMI dataset Accuracy = 92.5%, Precision = 98%, F1_score = 98%, Recall = 97% Detects PsD and measures the disease progression in PsD patients -
Vyas et al. [5] 2021 Detection of PsD using 3D CNN and compare with the 2D CNN 3D and 2D CNN PPMI database Accuracy = 88.9% Voxel-based morphometry usage is very accurate Manual feature extraction
Sivaranjini and Sujatha [57] 2021 Detection of PsD Transfer learning (TL), AlexNet pre-trained model PPMI MRI images Accuracy = 88.9% Improved efficiency of the model using transfer learning Only one slice of brain image is used
Mangesius et al. [56] 2020 Classification of PsD, MSA and PSP Decision Tree algorithm - Accuracy of classification PsD = 83.7% Distinguishes PsD, MSA, and PSP with very high accuracy Does not classify HC and PsD
Chakraborty et al. [60] 2020 Detecting PsD 3D CNN model MRI scans of 406 subjects from PPMI database Accuracy = 95.29%, Average recall = 0.943, Precision = 0.927, F1-score = 0.936 The maximum focus was on the substantia nigra region to predict PsD as it is the most-affected part of brain Specific subcortical structures should be considered

Comparative analysis of PsD public datasets.

Dataset Name References Instances and subjects Attributes Datatype Merit Demerit Used Device /Sensor
Parkinson's Disease dataset [132, 133, 134] I-197, PsD:23, HC:8 23 Speech signal No missing values Fewer instances, unbalanced and only suitable for binary classification Recorder
PhysioNet/Vertical Ground Reaction (VGRF) [55, 135, 136, 137, 138] PsD:93, HC:72 - Multi-channel recordings from force sensors Disease severity is also monitored - 8 sensors under each foot
PPMI database [5, 108, 139, 140] PsD:600, HC:400 - MRI Images, DaTscan, PET, etc. Most widely used database of PsD datasets Imbalanced dataset Tesla scanner
Parkinson's telemonitoring/Oxford PsD Telemonitoring Dataset [141, 142] I-5875, PsD: 42 26 Voice recordings No missing values Suitable only for binary classification Telemonitoring device
Daphnet Freezing of Gait Data Set [134, 143, 144] I-237, PsD:10 9 Gait monitoring data Realistic activity monitoring Very few subjects in dataset Acceleration sensors at the hip and leg
Parkinson Dataset with replicated acoustic features dataset [141, 145, 146] I-240, PsD:40, HC:40 46 Voice recording replications No missing values ML cannot be applied Recorder
Parkinson Speech Dataset with Multiple Types of Sound Recordings Dataset [147, 148] I-1040, PsD:20, HC:20 26 Sound recordings Useful for classification as well as regression Fewer subjects -
Parkinson's Disease Classification Data Set [149, 150, 151, 152] I-756, PsD:188, HC:64 754 Speech recordings Features extracted through many speech signal processing algorithms Imbalanced dataset Microphone
Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset [153, 154] I:77, PsD:62, HC:15 7 Handwriting-Spiral drawings Three different kinds of tests to collect data Fewer subjects, and imbalanced dataset Wacom Cintiq 12WX graphics tablet
NewHandPsD dataset [155, 156] PsD:31, HC:35 - Handwriting Balanced dataset Fewer subjects Smart pen (BiSP)
PC-GITA Spanish dataset [157, 158] PsD:50, HC:50 - Voice samples A well-balanced corpus with respect to age and gender Recorded under optimal recording conditions Fast Track C400 sound card and a Professional microphone
NTUA Parkinson Dataset [158, 159] I:44007, PsD:55, HC:23 - MRI and DaTscans Sufficiently large dataset Imbalanced dataset -
Neurovoz [160, 161, 162] PsD:47, HC:32 - Spanish speech samples No Noise Fewer subjects AKG C420 headset microphone coupled with a preamplifier
OpenfMRI [163, 164] 37 studies with 1411 subjects - MRI and EEG scans NIFTI format - -
PsD-BioStampRC21 dataset [165, 166] PsD:17, HC:17 - wearable sensor accelerometry data 24-hour monitoring data Fewer subjects MC 10 BioStamp RC sensors
Italian Parkinson's Voice and Speech [167, 168] PsD: 28, HC: 37 - Voice and Speech data - Fewer subjects -
Physio Bank [94, 169, 170] PsD: 93, HC:73 - Database of Gait Multichannel recordings, demographic information, and disease severity - Ultraflex Computer Dyno Graphy, Infotronic Inc. sensors
Neurocon [171, 172] I: 303, PsD: 27, HC:16 - T1 and resting-state MRI dataset Includes both T1 and resting-state scans Fewer subjects of only initial stage PsD 1.5-Tesla Siemens Avanto MRI scanner

A relative contrast of the proposed survey with the PsD detection state-of-the-art surveys.

Author Year Conclusion 1 2 3 4 5 6 7 8 9 10 11 12 13
Proposed Survey 2023 Examines PsD patients’ datasets and PsD diagnosis techniques on various modalities while addressing the open research challenges too ✓ ✓ 9 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓
Salari et al. [33] 2023 SVM is proposed to be a bridge between psychological research and the clinical facts ✓ ✓ 5 ✓ ✓ ✓
Shafiq et al. [39] 2023 Flower Pollination Algorithm and Extreme Gradient Boost Algorithm pair obtained the accuracy of 93% ✓ ✓ 1 ✓ ✓ ✓ ✓
Shaban [40] 2023 ANN is the most widely used PsD detection technique and sensory signals, EEG and handwriting are the most utilized data modalities 4 ✓ ✓ ✓ ✓
Kumar et al. [41] 2023 Accelerometer is the most widely used sensor for gait analysis for PsD detection ✓ ✓ 1 ✓ ✓ ✓ ✓ ✓ ✓
Dixit et al. [42] 2023 Review of PsD diagnosis along with an extensive discussion of future scope ✓ 6 ✓ ✓ ✓ ✓ ✓ ✓
Khanna et al. [43] 2023 No existing technique is yet suitable for practical use clinically. MRI, PET and SPECT scans are the most popular neuroimaging modalities ✓ ✓ 3 ✓ ✓ ✓ ✓ ✓ ✓
Zhang [44] 2022 SVM and ANN show best accuracy on SPECT scan for PsD detection ✓ 3 ✓ ✓ ✓
Chandrabhatla et al. [45] 2022 Technological evolution in detecting PsD, in-lab to in-home ✓ 3 ✓ ✓
Giannakopoulou and Roussaki [46] 2022 ML + IoT can revolutionize PsD diagnosis, mostly used best performing AI models are MLPs, LSTMs, CNNs, other ANN and ensemble learning techniques ✓ 5 ✓ ✓ ✓ ✓ ✓ ✓ ✓
Rana et al. [47] 2022 AI has revolutionized detecting early-stage PsD ✓ 7 ✓ ✓ ✓ ✓
Tanveer et Al. [48] 2022 CNN and RNN give better performance than ML ✓ 8 ✓ ✓ ✓ ✓ ✓ ✓
A. ul Haq et al. [49] 2022 DL techniques are the most appropriate 4 ✓ ✓ ✓ ✓ ✓ ✓
Rana et al. [50] 2022 Best accuracy to diagnose PsD for voice data was achieved using L1-norm SVM and k-fold cross-validation, for handwritten patterns using bagging ensemble, and for gait analysis by SVM ✓ ✓ 3 ✓ ✓ ✓ ✓ ✓
Alzubaidi et al. [51] 2021 Best performing and widely used DL models and datasets according to various modalities are identified 9 ✓ ✓ ✓ ✓ ✓ ✓ ✓
Loh et al. [52] 2021 CNN model achieves higher accuracy in various modalities for PsD detection ✓ 8 ✓ ✓ ✓
Noor et al. [53] 2020 CNN is used mostly in the detection of PsD 1 ✓ ✓ ✓ ✓
Khachnaoui et al. [54] 2020 DL methods show better performance than ML 2 ✓ ✓ ✓ ✓
Di Biase et al. [55] 2020 Very few algorithms are accurate enough to be potentially used clinically for PsD diagnosis and monitoring symptoms 1 ✓ ✓ ✓ ✓ ✓

Comparative analysis of PsD private datasets.

Dataset/Source/Author Name References Instances and subjects Attributes Datatype Merit Demerit Used Device/Sensor
Hospital Universiti Kebangsaan Malaysia, Kuala Lumpur [124]

I: 1440

PsD: 20

HC: 20

 		- EEG signals Non-invasive detection Fewer subjects 14-channel wireless Emotiv Epoc headset
Wearable Bio mechatronics Laboratory at Western University [173]

I: 409,380

PsD: 13

 		- Video recordings while performing the trials Resting, postural, and action tremors are monitored - Wearable assistive devices
Tuncer T. [174]

I:756

PsD:188

HC:64

 		- Vowel voice recordings High classification performance even with heterogeneous dataset, less expensive Imbalanced dataset Microphone
M. Lu [114]

I:200

PsD:55

 		- Video recordings of MDS-UPDRS exams Both gait and finger tapping experiments are performed Less data -
Pacific Parkinsons Research Centre (PPRC) [175]

PsD:20

HC:21

 		- Resting EEG High Performance metrics Fewer subjects 64-channel EEG cap and a Neuroscan SynAmps2 acquisition system
Iakovakis et al. [176, 177]

PsD:18

HC:15

 		913 Keystroke dynamics information Combined discriminative potential of enriched keystroke variables Fewer subjects Touchscreen smartphone
Hospital at Sun Yat-sen University [178]

I:7000

PsD:40

HC:30

 		- EEG signals Large number of instances Fewer hyperparameters 64-electrode Geodesic Sensor Net (Electrical Geodesics Inc.)
RMIT University, Melbourne, Australia [179]

PsD:41

HC:40

 		- Voice samples all recordings were normalized Fewer number of samples Apple iPhone 6S plus

Comparative analysis of PsD detection using emotional intelligence.

Author (s) Year Purpose Algorithm Dataset Output Merits Demerits
Pegolo et al. [127] 2022 Measuring hypomimia to distinguish between PsD and HC and to classify the emotions Random Forest showed best results Frontal face videos; PsD:50 HC:17 AUC values from 94.3 to 91.6; F1 scores from 76.2 to 71.5 A stand-alone methodology for quantifying the degree of impairment Imbalanced data
Parameshwara et.al. [124] 2022 PsD detection KNN and Naïve Bayes Classifiers

EEG data;

PsD: 20

HC: 20

 		Acc = 99% Non-invasive use of emotional EEG signals Inadequate number of PsD patients with mild-to-moderate severity, relation in EEG and motor symptoms is unknown
Dar et al. [20] 2022 EEG based emotional classification ELM (extreme learning machine) PsD dataset, AMIGOS, and SEED-IV datasets Acc = 83.2% Implemented on cross-subject and cross-dataset, robust to real-time applications Datasets with fewer number of subjects
Anusri et al. [126] 2021 Early detection of PsD using facial emotional intelligence Alexnet and VGG16

PPMI dataset

PsD:188

HC:50

 Acc = 96.5% Early diagnosis justified using different performance metrics Imbalanced dataset
Sechidis et al. [128] 2021 Assessment of PsD speech characteristics Machine learning Speech dataset NA Main focus is on happy and sad emotion only No dataset of PsD and HC exists thta has labeled emotions
Justyna and Burget [22] 2020 Analysis of Emotional changes during quick pronunciation exercise XG Boost

Video recording of speech exercise

PsD:70

HC:45

 Acc = 69%; sadness and surprise are negatively correlated with PsD Seven emotion classes are considered Low accuracy
Yuvaraj et al. [125] 2014 To discriminate PsD patients and HC while emotional information processing Discriminant analysis

EEG scan;

PsD: 20

HC: 30

 		Acc = 95% Distributed spectral powers in EEG frequency bands supply information about emotion processing in PsD patients Fewer number of subjects
Zhao et al. [129] 2014 Classification of emotions through speech in PsD patients Naïve Bayes, Random Forest, and SVM Recorded speaking short statements Acc = 3.33% First automatic classification of emotions in the voice of PsD patients Low Accuracy

List of acronyms.

Acronym Meaning Acronym Meaning
PsD Parkinson's Disease FER Facial emotion recognition
HC Healthy Controls MRI Magnetic Resonance Imaging
PPMI Parkinson's Progression Markers Initiative TNR True negative rate
TPR True positive rate SVM Support Vector Machine
PET Positron emission tomography MSA Multiple system atrophy
AI Artificial Intelligence DL Deep Learning
ML Machine Learning DICOM Digital Imaging and Communications in Medicine
SPECT Single-photon emission computerized tomography EMG Electromyogram
CNN Convolutional Neural Network CRNN Convolutional Recurrent Neural Network
SWEDD Scans Without Evidence of Dopaminergic Deficit KNN K-nearest neighbors
ECG Electrocardiogram PSP Progressive supranuclear palsy
LC Locus coeruleus LDA Linear discriminant analysis
SNpc Substantia nigra pars compacta DaTscan Dopamine Transporter Scan
MFCC Mel-Frequency Cepstral Coefficients OCSA Optimized crow search algorithm
OCT Optical coherence tomography CCSA Chaotic crow search algorithm
EEG Electroencephalogram RBD REM sleep behavior disorder
PCA Principal component analysis MDS Movement Disorder Society
ROI Region of interest RGNN Regularized graph neural network
ANOVA Analysis of variance FAWT Flexible analytic wavelet transform
RF Random forests Emotion DL Emotion-aware distribution learning
RBF Radial basis functions LIWC Linguistic Inquiry and Word Count
RQA Recurrence quantification analysis MVLL Maximum vertical line length
MDLL Maximum diagonal line length FMI Face mobility index
STRNN Spatial–temporal recurrent neural network LDA Latent Dirichlet Allocation
FACS Facial Action Coding System VGRF Vertical ground reaction force
AUC Area under the ROC Curve EI Emotional intelligence
MEIS Multi-factor emotional intelligence scale FNN Fuzzy neural network
ANN Artificial Neural network GCNN Graph Convolutional Neural Network
FL Federated learning UPDRS Unified Parkinson's disease rating scale
FDG Fluorodeoxyglucose MCI Mild cognitive impairment
DN Dopaminergic Neurons DT Dopamine transporter
DBN Deep belief network LSTM Long short-term model
TL Transfer learning SVR Support vector regression
Node DAT Node-wise domain adversarial training GSR Galvanic skin response
DNA Deoxyribonucleic acid RBC Red blood cell
WHARMPA Weighted HARMonization Parameters RNA Ribonucleic acid
FoG Freezing of Gait CC Correlation coefficients
MCC Merkel cell carcinoma RMSE Root mean square error

Comparative analysis of techniques used to detect PsD using SPECT images.

Author (s) Year Purpose Algorithm Dataset Output Merits Demerits
Hathaliya et al. [73] 2022 PsD classification and monitoring of the DT (dopamine transporter) level inside the brain CNN-based scheme 58692, 11738, and 7826 SPECT images from PPMI for training, validation, and testing, respectively Accuracy = 88% Accurate diagnosis of PsD and its progression -
Adams et al. [32] 2021 DL algorithm to accurately predict motor-based symptoms CNN model 252 subjects DAT SPECT images Average absolute error = 6.0 ± 4.8 Enhanced diagnosis of UPDRS_III score with longitudinal data Features related to PsD are not identified
Magesh et al. [74] 2020 ML-based early detection of PsD CNN and VGG16-based TL scheme 642 DaTscan SPECT images Accuracy = 95.2% Quick diagnosis for PsD Dearth of conclusive diagnostic tests for PsD
Pereira and Ferreira [75] 2020 To classify PsD, SWEDD, and HC CNN models SPECT and MRI images Accuracy = 65.7% Classifies PsD, SWEDD, and HC subjects Lower accuracy
Wenzel et al. [76] 2019 Robust classification algorithm to diagnose the dopamine transporter CNN and ImageNet-based TL, semi-quantitative SBR analysis 645 subjects Accuracy = 97% Accurate diagnosis of PsD patient with DT analysis Trained model with few samples
Ortiz et al. [77] 2019 To classify HC and PsD using isosurface-based feature extraction CNN model 269 DaTscan images Accuracy = 95% Low complexity of the input data Increases overall system complexity

Comparative analysis of techniques used to detect Parkinson's disease progression detection.

Author (s) Year Purpose Algorithm Dataset Output Merits Demerits
Kleinholdermann et al. [112] 2021 To predict UPDRS scores Random Forest regression model outnumbered Surface electromyography (sEMG) electrodes data; PsD: 45 Correlation between true and estimated UPDRS values = 0.739 Support the usage of wearables to detect PsD Other performance metrics not considered
Hssayeni et al. [113] 2021 Severity level estimation (UPDRS-III scores) Dual-channel LSTM and TL Ensemble Angular velocity data from inertial sensors; PsD:24 1D-CNN-LSTM used for raw signals and 2D-CNN-LSTM used for time-frequency data with r = 0.79, MAE = 5.95 Unobtrusive analysis at home Fewer subjects
Lu et al. [114] 2021 Detection and severity estimation for PsD patients OF-DDNet Video recordings of MDS-UPDRS exams; PsD: 55 AUC = 0.69, F1-score = 0.47, prec. = 0.47, and balanced acc. = 48% Handles model uncertainty, label noise and small dataset and imbalance issues -
Raza et al. [110] 2021 Progression of PsD XGBoost Speech recordings; I:5875; PsD:42 MAE = 5.09 ± 0.16 Enables continuous monitoring and efficient communication Uses only voice recordings
Rupprechter et al. [89] 2021 Severity estimation (UPDRS scores) Ordinal Random Forest classifier 729 videos of gait assessments Balanced accuracy = 50%, Binary sensitivity = 73%; Binary specificity = 68% Provides interpretability 3D pose estimation not used
Abujrida et al. [115] 2020 Detection and severity estimation in walking balance Wavelet (DWT) domain + fine tree/Boosted trees/RF/bagged trees/weighted LR/KNN/LDA, cubic SVM Signals from gyroscopes, accelerometers, demographics, and lifestyle data PsD:152, HC:304

Bagged trees

Acc = 95%, Prec = 95%

AUROC = 0.92

RF:

Acc = 93%, Prec = 92%

AUROC = 0.97;

 		Large no. of participants, and amalgam of frequency, time, and statistical features with sway area and lifestyle parameters too Lack of signal segmentation strategies
Mehrbakhsh Nilashi et al. [111] 2020 Remote tracking of PsD progression DBN with SVR Parkinson's telemonitoring dataset; I: 5875 Average output for testing and training sets are 0.521 and 0.537 respectively for RMSE in detecting motor-UPDRS Supervised and unsupervised learning techniques used Feature selection techniques are not used
Li et. al. [116] 2019 Diagnosis and severity (H&Y scores) estimation Random Forest regression Acceleration and angular velocity signals; PsD:13 HC:12 MAE = 0.166, r = 0.97 Records user's motion information unobtrusively -
Buongiorno et al. [117] 2019 Detection and severity rating of PsD ANN and SVM

Postural and kinematics parameters from MS Kinect v2 sensor; PsD:16

HC:14

 		ANN: Acc. = 95%, SVM: Acc. = 81%, Spec = 78% Low-cost and easy and fast setup Only mild vs. moderate severity is measured
Yoon et al. [118] 2019 PsD Severity estimation (UPDRS 0–176 scores) TL Phonation and speech recordings; PsD:42 MAE = 2–3 Approximately Recording done at home, and better accuracy -
Grammatikopoulou et al. [119] 2019 PsD Severity estimation Parallel two LSTMs trained with raw joint-coordinates and the combined line distances Skeletal features of MS Kinect v2 RGB videos; PsD (advanced stage): 12 and PsD (initial stage):6 Acc. = 77.7% - Very few subjects, only 2 stages
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