Loading...

Media is loading
 

Paper Type

Complete

Abstract

Currently, the detection and assessment of Parkinson's Disease (PD) continue to be a challenge for modern medicine. This study investigated the possibility of implementing a multimodal audio-visual deep-learning model to detect PD. To begin, we designed a bidirectional long short-term memory model (BiLSTM) under different considerations. After comparing the model combinations with their unimodal counterparts, we conducted a three-way repeated measures analysis of variance (RMANOVA) and multiple pairwise t-tests to validate the following results. First, the audio-visual models performed significantly better than the unimodal models. However, an outlier in the performance showed the instability of the models when they were trained on insufficient data. Second, the models only compensate for the disturbances if both modalities are good quality. Finally, the models work better on raw and low-level features than handcrafted ones.

Paper Number

1431

Comments

SIGHEALTH

Share

COinS
 
Aug 16th, 12:00 AM

Detection of Parkinson’s Disease with Multimodal Deep-Learning

Currently, the detection and assessment of Parkinson's Disease (PD) continue to be a challenge for modern medicine. This study investigated the possibility of implementing a multimodal audio-visual deep-learning model to detect PD. To begin, we designed a bidirectional long short-term memory model (BiLSTM) under different considerations. After comparing the model combinations with their unimodal counterparts, we conducted a three-way repeated measures analysis of variance (RMANOVA) and multiple pairwise t-tests to validate the following results. First, the audio-visual models performed significantly better than the unimodal models. However, an outlier in the performance showed the instability of the models when they were trained on insufficient data. Second, the models only compensate for the disturbances if both modalities are good quality. Finally, the models work better on raw and low-level features than handcrafted ones.

When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.