Please use this identifier to cite or link to this item:
|Title:||Language-Independent Bimodal System for Early Parkinson’s Disease Detection||Authors:||Taleb, Catherine
|Affiliations:||Department of Computer Engineering||Keywords:||1D CNN-BLSTM
Parkinson’s disease (PD)
|Issue Date:||2021||Part of:||Document Analysis and Recognition – ICDAR 2021||Start page:||397||End page:||413||Conference:||International Conference on Document Analysis and Recognition (ICDAR) ( 16th : 5-10 Sep, 2021 : Lausanne )||Abstract:||
Parkinson’s disease (PD) is a complex disorder characterized by several motor and non-motor symptoms that worsen over time, and that differ from person to another. In the early stages, when the symptoms are often incomplete, the diagnosis becomes difficult and at times, the subject may remain undiagnosed. This difficulty is a strong motivation for computer-based assessment tools that can aid in the early diagnosing and predicting the progression of PD. Handwriting’s deterioration, vocal and eye movement impairments may be ones of the earliest indicators for the onset of the illness. A language independent model to detect PD at early stages by using multimodal signals has not been enough addressed. Due to the lack of multimodal and multilingual databases, database which includes online handwriting, speech signals, and eye movement’s recordings have been recently collected. After succeeding in building language independent models for PD early diagnosis using pure handwriting or speech, we propose in this work language independent models based on bimodal analyses (handwriting and speech), where both SVM and deep learning models are studied. Our experiments show that classification accuracy up to 100% can be obtained by our SVM model through handwriting/speech bimodal analysis.
|Appears in Collections:||Department of Computer Engineering|
Show full item record
checked on Dec 1, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.