Context

Try finding the reasons for Parkinsons disease and predict who might have it next!

The proposed multi-modal Parkinson’s disease detection model integrates diverse data types to enhance the accuracy and robustness of diagnosis by leveraging unique features from multiple modalities. The methodology can be described as follows:

1. Speech Analysis

• Dataset: MDVR-KCL Dataset (https://data.niaid.nih.gov/resources?id=zenodo_2867215)
• The speech data will be preprocessed to extract relevant features such as pitch, jitter, shimmer, and Mel-frequency cepstral coefficients (MFCCs). These features, indicative of vocal impairments associated with Parkinson’s disease, will be fed into a deep learning-based audio classification model for feature extraction and classification.

1. Handwriting Analysis

• Dataset: Parkinson’s Drawings Dataset (https://www.kaggle.com/datasets/kmader/parkinsons-drawings)
• The handwriting data will undergo preprocessing, including resizing and normalization. Features such as tremors, stroke consistency, and pressure patterns will be extracted using computer vision techniques. Deep learning models like Convolutional Neural Networks (CNNs) will then classify the handwriting data based on Parkinson’s-specific anomalies.

1. DAT Scan Imaging

• Dataset: NTUA Parkinson Dataset (https://www.kaggle.com/datasets/irfansheriff/parkinsons-brain-mri-dataset)
• DAT scan images will be processed to detect dopamine transporter activity levels. Image preprocessing techniques such as denoising, normalization, and segmentation will be applied. A pre-trained CNN model, fine-tuned for this task, will classify the scans to identify patterns associated with Parkinson’s disease.

1. MRI Imaging

• Dataset: NTUA Parkinson Dataset (https://www.kaggle.com/datasets/rishikjha/parkinsons-disease-dat-and-mri-scans)
• MRI images will be analyzed to detect structural changes in brain regions commonly associated with Parkinson’s disease. A combination of deep learning models, such as 3D CNNs or Vision Transformers (ViTs), will be used to extract spatial features and classify the MRI data effectively.

1. EEG Analysis

• Dataset: EEG Motor Movement/Imagery Dataset (https://www.kaggle.com/datasets/bjoernjostein/ptb-diagnostic-ecg-database)
• EEG signals will be preprocessed using techniques like bandpass filtering and artifact removal to enhance signal quality. Features such as motor imagery patterns and brainwave frequencies will be extracted. Recurrent Neural Networks (RNNs) or Long Short-Term Memory (LSTM) networks will be used to capture temporal dependencies and classify EEG data for Parkinson’s detection.

This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds to one of 195 voice recordings from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to the "status" column which is set to 0 for healthy and 1 for PD.

Attribute Information: Matrix column entries (attributes): name - ASCII subject name and recording number MDVP:Fo(Hz) - Average vocal fundamental frequency MDVP:Fhi(Hz) - Maximum vocal fundamental frequency MDVP:Flo(Hz) - Minimum vocal fundamental frequency MDVP:Jitter(%), MDVP:Jitter(Abs), MDVP:RAP, MDVP:PPQ, Jitter:DDP - Several measures of variation in fundamental frequency MDVP:Shimmer,MDVP:Shimmer(dB),Shimmer:APQ3,Shimmer:APQ5,MDVP:APQ,Shimmer:DDA - Several measures of variation in amplitude NHR, HNR - Two measures of the ratio of noise to tonal components in the voice status - The health status of the subject (one) - Parkinson's, (zero) - healthy RPDE, D2 - Two nonlinear dynamical complexity measures DFA - Signal fractal scaling exponent spread1,spread2,PPE - Three nonlinear measures of fundamental frequency variation

The ALAMEDA_PD_tremor_dataset.csv contains 92 features extracted from raw accelerometer data after pre-processing, 4 tremor-related labels and some other metadata. In total, it includes 99 columns:

1. The first two columns correspond to the start_timestamp and the end_timestamp of the time window from which the respective features have been extracted.

2. The third column corresponds to the subject_id, which is used to uniquely identify PD patients enrolled in the current study.

3. The next 92 columns correspond to features extracted from raw triaxial accelerometer data collected with the GENEActiv smart bracelets throughout 30-min MDS-UPDRS assessment during in-clinic visits, after applying some preprocessing steps. First, the accelerometer signals were band-pass filtered [2.5 Hz, 12.5 Hz] to enable tremor detection. Then, the magnitude and the first principal component of the filtered signals were computed to attenuate the dependency on sensor placement and orientation. Finally, the transformed signals were segmented into time windows of 2048 samples (or 20.48 sec) with 50% overlap. Then, 92 features were extracted in both time and frequency domains. Spectral features were extracted after applying Fast Fourier Transform. The full list of the extracted features is demonstrated in the table below. These features can feed Machine Learning models to predict the presence/absence of PD tremor.

4. The final 4 columns correspond to tremor-related labels (Constancy_of_rest, Kinetic_tremor, Postural_tremor and Rest_tremor). They derive from the respective MDS-UPDRS III annotations, after transforming them to make them suitable for binary classification. More specifically, zero scores remained 0 to indicate the absence of tremor while positive scores were transformed to 1 to indicate the presence of tremor. Each of these columns can be used as a target to be predicted with the help of Machine Learning models.

Dataset

This dataset was created by Deep pratap Singh

Released under Apache 2.0

Contents

Dataset

This dataset was created by syed vajid

Contents

Dataset

This dataset was created by KhumanShubh

Contents

Context

More than 10 million people worldwide are living with Parkinson's disease. Improving machine learning model which identifies Parkinson's disease will lead to helping patients with early dialogs and reduction of treatment cost.

Content

Handwriting database consists of 62 PWP(People with Parkinson) and 15 healthy individuals. The data was collected in 2009.

Number of instances: 77, Number of attributes: 7

Acknowledgements

Source: https://archive.ics.uci.edu/ml/datasets/Parkinson+Disease+Spiral+Drawings+Using+Digitized+Graphics+Tablet

Citation:

1.Isenkul, M.E.; Sakar, B.E.; Kursun, O. . 'Improved spiral test using digitized graphics tablet for monitoring Parkinson's disease.' The 2nd International Conference on e-Health and Telemedicine (ICEHTM-2014), pp. 171-175, 2014.

2.Erdogdu Sakar, B., Isenkul, M., Sakar, C.O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., Kursun, O., 'Collection and Analysis of a Parkinson Speech Dataset with Multiple Types of Sound Recordings', IEEE Journal of Biomedical and Health Informatics, vol. 17(4), pp. 828-834, 2013.

3_cls folder has 2 directories: - train - test

Each directory has 3 sub-directories: - CONTROL - AD - PD

Dataset is collected from here

This dataset captures the progression of chronic diseases over time, focusing on Diabetes, Parkinson’s disease, and Alzheimer’s. It includes biometric measurements, medication data, lifestyle factors, and disease stage information for synthetic patients. The data is structured in a time-series format, with multiple entries per patient, making it ideal for modeling disease progression, forecasting future biometrics, and understanding risk factors across stages of chronic illness.

K. Scott Mader created the original dataset of 204 hand-drawn images for Parkinson’s disease diagnosis, consisting of two classes: Healthy and Parkinson. The dataset includes spiral and wave drawings. For my thesis, the original 204 images were expanded to 3,264 across the same two classes. This increase was achieved through data augmentation techniques, including rotations of 90°, 180°, and 270°, vertical flipping at 180°, and conversion to color images. The augmented data gives the model more opportunities to generalize, enhancing training and testing processes.

Context

This dataset is collected from UCI Machine Learning Repository through the following link: https://archive.ics.uci.edu/ml/datasets/Parkinson%27s+Disease+Classification#

Data Set Information:

The data used in this study were gathered from 188 patients with PD (107 men and 81 women) with ages ranging from 33 to 87 (65.1±10.9) at the Department of Neurology in Cerrahpaşa Faculty of Medicine, Istanbul University. The control group consists of 64 healthy individuals (23 men and 41 women) with ages varying between 41 and 82 (61.1±8.9). During the data collection process, the microphone is set to 44.1 KHz and following the physician’s examination, the sustained phonation of the vowel /a/ was collected from each subject with three repetitions.

Attribute Information:

Various speech signal processing algorithms including Time Frequency Features, Mel Frequency Cepstral Coefficients (MFCCs), Wavelet Transform based Features, Vocal Fold Features and TWQT features have been applied to the speech recordings of Parkinson's Disease (PD) patients to extract clinically useful information for PD assessment.

Citation Request:

If you use this dataset, please cite: Sakar, C.O., Serbes, G., Gunduz, A., Tunc, H.C., Nizam, H., Sakar, B.E., Tutuncu, M., Aydin, T., Isenkul, M.E. and Apaydin, H., 2018. A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing, DOI: [Web Link] https://doi.org/10.1016/j.asoc.2018.10.022

Content

In this dataset you'll find the conditions and characteristics of several patients diagnosed with Parkinson's Disease.

Acknowledgements

This data comes from https://data.world/uci/parkinsons/workspace/file?filename=parkinsons.names.txt.

Data Description

Parkinson's disease (PD) is one of the most common movement disorders, affecting approximately 1 million Americans (estimates range between 4 and 6.5 million people worldwide) and about 1% of older adults. In the US alone, 60,000 new cases are diagnosed each year. PD is a chronic and progressive neurological disorder that results in tremor, rigidity, slowness, and postural instability. A disturbed gait is a common, debilitating symptom; patients with severe gait disturbances are prone to falls and may lose their functional independence.

This database contains measures of gait from 93 patients with idiopathic PD (mean age: 66.3 years; 63% men), and 73 healthy controls (mean age: 66.3 years; 55% men). The database includes the vertical ground reaction force records of subjects as they walked at their usual, self-selected pace for approximately 2 minutes on level ground. Underneath each foot were 8 sensors (Ultraflex Computer Dyno Graphy, Infotronic Inc.) that measure force (in Newtons) as a function of time. The output of each of these 16 sensors has been digitized and recorded at 100 samples per second, and the records also include two signals that reflect the sum of the 8 sensor outputs for each foot. For details about the format of the data, please see this note.

With this information, one can investigate the force record as a function of time and location, derive measures that reflect the center-of-pressure as a function of time, and determine timing measures (e.g., stride time, swing time) for each foot as functions of time. Thus, one can study the stride-to-stride dynamics and the variability of these time series.

This database also includes demographic information, measures of disease severity (i.e., using the Hoehn & Yahr staging and/or the Unified Parkinson's Disease Rating Scale) and other related measures (available in HTML or xls spreadsheet format).

A subset of the database includes measures recorded as subjects performed a second task (serial 7 subtractions) while walking, as in the figure above, which shows excerpts of swing time series from a patient with PD (lower panels) and a control subject (upper panels), under usual walking conditions (at left) and when performing serial 7 subtractions (at right). Under usual walking conditions, variability is larger in the patient with PD (Coefficient of Variation = 2.7%), compared to the control subject (CV = 1.3%). Variability increases during dual tasking in the subject with PD (CV = 6.5%), but not in the control subject (CV = 1.2%). From Yogev et al. (reference [4] below)

References

• Frenkel-Toledo S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM. Effect of gait speed on gait rhythmicity in Parkinson's disease: variability of stride time and swing time respond differently. Journal of NeuroEngineering and Rehabilitation 2005: 2:23.
• Frenkel-Toledo, S, Giladi N, Peretz C, Herman T, Gruendlinger L, Hausdorff JM. Treadmill walking as a pacemaker to improve gait rhythm and stability in Parkinson's disease. Movement Disorders 2005; 20(9):1109-1114.
• Hausdorff JM, Lowenthal J, Herman T, Gruendlinger L, Peretz C, Giladi N. Rhythmic auditory stimulation modulates gait variability in Parkinson's disease Eur J Neuroscience 2007; 26: 2369-2375.
• Yogev G, Giladi N, Peretz C, Springer S, Simon ES, Hausdorff JM. Dual tasking, gait rhythmicity, and Parkinson's disease: Which aspects of gait are attention demanding? Eur J Neuroscience 2005; 22:1248-1256.

Contributors

For more information about PD, see Parkinson's Disease: Hope Through Research at the NIH NINDS web site, and Living With Parkinson's at the web site of the Michael J. Fox Foundation for Parkinson's Research.

This work was supported in part by grants from the National Institutes of Health, National Parkinson Foundation, and the Parkinson's Disease Foundation and was collected at the Laboratory for Gait & Neurodynamics, Movement Disorders Unit of the Tel-Aviv Sourasky Medical Center. For more information, please contact Dr. Jeffrey M. Hausdorff (jhausdor@tasmc.health.gov.il or jhausdor@bidmc.harvard.edu).

Source: https://data.mendeley.com/datasets/mt6rzbsf6p/1

Data on daytime sleepiness in patients with Parkinson disease, narcolepsy HLA DQB1 risk and protective alleles and dopaminergic drug use. The data was collected in a convenience sample of 150 participants with Parkinson disease from Albany Medical Center, Albany, New York. Data available for each participant includes demographic data, past medical history, medications, the Unified Parkinson Disease Rating Scale, Schwab and England scale, Hoehn and Yahr scale, the Montreal Cognitive Assessment, the modified Epworth Sleepiness Scale, the Parkinson Disease Sleep Scale version 2, the Mayo Sleep questionnaire (select questions). Dopaminergic drugs are expressed are levodopa equivalent daily dosage. All participants were genotyped for narcolepsy DQB1 risk alleles. A data dictionary is included.

Context

The data includes 2D human pose estimates of Parkinson's patients performing a variety of tasks (e.g. communication, drinking from a cup, leg agility). Pose estimates were produced using Convolutional Pose Machines (CPM, https://arxiv.org/abs/1602.00134).

The goal of this project was to use features derived from videos of Parkinson's assessment to predict the severity of parkinsonism and dyskinesia based on clinical rating scales.

Content

Data was acquired as part of a study to measure the minimally clinically important difference in Parkinson's rating scales. Participants received a two hour infusion of levodopa followed by up to two hours of observation. During this time, they were assessed at regular intervals and assessments were video recorded for post-hoc ratings by neurologists. There were between 120-130 videos per task.

The data includes all movement trajectories (extracted frame-by-frame) from the videos of Parkinson's assessments using CPM, as well as confidence values produced by CPM. Ground truth ratings of parkinsonism and dyskinesia severity are included using the UDysRS, UPDRS, and CAPSIT rating scales.

Camera shake has been removed from trajectories (see paper for more details). No other preprocessing has been performed. Files are saved in JSON format. For information on how to deal with files, see data_import_demo.ipynb or view online at https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset.

Acknowledgements

We would like to acknowledge the staff and patients at Toronto Western Hospital for their time and assistance in this study.

Citations

If you use this dataset in your work, please cite the following reference:
M.H. Li, T.A. Mestre, S.H. Fox, and B. Taati, Vision-based assessment of parkinsonism and levodopa-induced dyskinesia with pose estimation, Journal of NeuroEngineering and Rehabilitation, vol. 15, no. 1, p. 97, Nov. 2018. doi:10.1186/s12984-018-0446-z.

You may also find the following paper useful. In this paper, we evaluated the responsiveness of features to clinically relevant changes in dyskinesia severity:
M.H. Li, T.A. Mestre, S.H. Fox, B. Taati, Automated assessment of levodopa-induced dyskinesia: Evaluating the responsiveness of video-based features, Parkinsonism & Related Disorders. (2018). doi:10.1016/j.parkreldis.2018.04.036.

Inspiration

In our study, we aimed to evaluate the readiness of off-the-shelf human pose estimation and deep learning for clinical applications in Parkinson's disease. We hope that others may find this dataset useful for furthering progress in technology-based monitoring of neurological disorders.

License

This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Photo by jesse orrico on Unsplash.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20292

Zhang Y, James M, Middleton FA, Davis RL. Transcriptional analysis of multiple brain regions in Parkinson's disease supports the involvement of specific protein processing, energy metabolism, and signaling pathways, and suggests novel disease mechanisms. Am J Med Genet B Neuropsychiatr Genet 2005 Aug 5;137B(1):5-16. PMID: 15965975

Zheng B, Liao Z, Locascio JJ, Lesniak KA et al. PGC-1α, a potential therapeutic target for early intervention in Parkinson's disease. Sci Transl Med 2010 Oct 6;2(52):52ra73. PMID: 20926834

Transcriptional analysis of whole substantia nigra in Parkinson's disease Expression profiling by array

15 control 11 disease (3 outlier controls omitted)

Top 250 genes ranked by p value.

value distribution https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4117206%2F5cb17fa474114961427c4786f49492ae%2Fgse20292valuedistributionpost.txt?generation=1576039260414717&alt=media" alt="">