  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.
