Parkinson’s disease is a brain disorder that causes unintended or uncontrollable movements, such as shaking, stiffness, and difficulty with balance and coordination.
Symptoms usually begin gradually and worsen over time. As the disease progresses, people may have difficulty walking and talking. They may also have mental and behavioral changes, sleep problems, depression, memory difficulties, and fatigue.
One of the symptoms of this disease is a change in the person's speech, such as speaking in a low voice or slow speed or voice tremors.
Based on this indication, a dataset with the following characteristics has been collected:
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.
For more information, visit the following link:
https://www.kaggle.com/datasets/dipayanbiswas/parkinsons-disease-speech-signal-features
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