A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics

TL;DR

This paper introduces a lightweight CNN-LSTM model for Parkinson's disease diagnosis using time-series data, achieving high accuracy with fewer parameters than traditional methods.

Contribution

A novel hybrid CNN-LSTM architecture designed specifically for efficient Parkinson's diagnosis from time-series data.

Findings

Outperforms traditional classifiers like SVM, RF, and LightGBM.

Achieves high diagnostic accuracy with fewer model parameters.

Demonstrates effectiveness on multiple evaluation metrics.

Abstract

In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods.