Parkinson?s disease (PD) is the second most common neurodegenerative disorder in the world. However, there is no diagnostic tool for PD and this can make it difficult to diagnose, especially in the early stages. But strong evidences suggest that speech is affected very early and continues to deterio
Parkinson’s disease (PD) is the second most common neurodegenerative disorder in the world. However, there is no diagnostic tool for PD and this can make it difficult to diagnose, especially in the early stages. But strong evidences suggest that speech is affected very early and continues to deteriorate as the disease progresses. This makes speech signal a natural candidate for early diagnosis and quantification of PD. Hence in this project we will develop a machine learning algorithm for the automatic discrimination of speech of people with PD and healthy speakers. Early diagnosis of PD will make the treatments more effective. Also non-pharmacologic treatments, such as increased exercise, are also easier to perform in the early stages of PD and may help slow down disease progression.
To design and develop a machine learning algorithm for the automatic classification of Parkinson’s vs. non-Parkinson’s speech signals. The speech signals will be recorded from native Pakistani speakers. And the speech recordings will be modelled for four speech dimensions (i.e. phonation, prosody, articulation and intelligibility).
The final form of the project will consist of two parts. The first part will be a headset for recording speech signals. The second part will be a software system for the automatic diagnosis of Parkinson’s disease.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Headset (Logitech H390e) | Equipment | 3 | 6500 | 19500 |
| stationary and printouts | Miscellaneous | 1 | 5000 | 5000 |
| others | Miscellaneous | 1 | 5000 | 5000 |
| Data collection cost | Equipment | 25 | 1000 | 25000 |
| Total in (Rs) | 54500 |
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