Attention-Based Deep Learning Model for Early Detection of Parkinson's Disease

Mohd Sadiq,Mohd Tauheed Khan,Sarfaraz Masood

doi:10.32604/cmc.2022.020531

Abstract

Parkinson's disease (PD), classified under the category of a neurological syndrome, affects the brain of a person which leads to the motor and non-motor symptoms. Among motor symptoms, one of the major disabling symptom is Freezing of Gait (FoG) that affects the daily standard of living of PD patients. Available treatments target to improve the symptoms of PD. Detection of PD at the early stages is an arduous task due to being indistinguishable from a healthy individual. This work proposed a novel attention-based model for the detection of FoG events and PD, and measuring the intensity of PD on the United Parkinson's Disease Rating Scale. Two separate datasets, that is, UCF Daphnet dataset for detection of Freezing of Gait Events and PhysioNet Gait in PD Dataset were used for training and validating on their respective problems. The results show a definite rise in the various performance metrics when compared to landmark models on these problems using these datasets. These results strongly suggest that the proposed state of the art attention-based deep learning model provide a consistent as well as an efficient solution to the selected problem. High values were obtained for various performance metrics like accuracy of 98.74% for detection FoG, 98.72% for detection of PD and 98.05% for measuring the intensity of PD on UPDRS. The model was also analyzed for robustness against noisy samples, where also model exhibited consistent performance. These results strongly suggest that the proposed model provides a better classification method for selected problem.

Highlights

1 Introduction A critical step considered in treatment of a Parkinson’s disease (PD) patient is the early detection of the disease, which has been a major interest of research
The proposed model achieved an accuracy of 98.74% on detection of freezing of gait (FoG), 98.72% on detection of PD, and 98.05% on measuring the intensity of PD
The use of a Deep Learning (DL) model approach was proposed for one such neurodegenerative disease known as Parkinson’s disease

Summary

Introduction

A critical step considered in treatment of a PD patient is the early detection of the disease, which has been a major interest of research. PD, being a neurodegenerative disorder, affects parts of the brain whose intense effects can lead to gait imbalance, which leads to motor disorders in patients It suggests that the number of patients is going to increase by a factor of two in the coming 20 years, most of whom will have an age between the ranges of 60–80 years. The mentioned methods are invasive, highly expensive to carry out, and are effective at the later stages of the disease where it has significantly spread over the brain. Detection of PD at the early stages is crucial to prevent damage caused to brain cells and the methods of diagnosis need to be non-invasive and inexpensive

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Discussion

Conclusion