DEEP LEARNING FRAMEWORK USED IN PARKINSON'S DISEASE ANALYSIS
| Main Authors: | Folador, João Paulo, Andrade, Adriano Oliveira |
|---|---|
| Format: | Proceeding poster eJournal |
| Bahasa: | eng |
| Terbitan: |
, 2018
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/3559195 |
Daftar Isi:
- In Biomedical Engineering, the data collected of physiological signals assists in the detection of diseases, treatment and rehabilitation. In Parkinson’s disease (PD) which is an illness that remains incurable the knowledge and understanding of the symptoms help to bring better conditions to patients life. PD has a vary of symptoms and its data collection has been performed by different exams and monitoring periods, generating complex and voluminous data. In this sense, techniques to analyze these types of information have been improved, and some developers have invested in the use of artificial intelligence, specifically in Deep Learning (DL) frameworks, which contains different methods in a single tool. In this review, the main contributions were to conceptualize and understand the term Deep Learning, the basic characteristics of the most used frameworks and point the methods supported by each one. In addition, elucidate if DL has been used to process PD data