LSTM-CNN: An efficient diagnostic network for Parkinson's disease
utilizing dynamic handwriting analysis
- URL: http://arxiv.org/abs/2311.11756v1
- Date: Mon, 20 Nov 2023 13:34:08 GMT
- Title: LSTM-CNN: An efficient diagnostic network for Parkinson's disease
utilizing dynamic handwriting analysis
- Authors: Xuechao Wang, Junqing Huang, Sven Nomm, Marianna Chatzakou, Kadri
Medijainen, Aaro Toomela, Michael Ruzhansky
- Abstract summary: We design a compact and efficient network architecture to analyse the distinctive handwriting patterns of patients' dynamic handwriting signals.
The proposed network is based on a hybrid deep learning approach that fully leverages the advantages of both long short-term memory (LSTM) and convolutional neural networks (CNNs)
- Score: 2.1063903985563988
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Background and objectives: Dynamic handwriting analysis, due to its
non-invasive and readily accessible nature, has recently emerged as a vital
adjunctive method for the early diagnosis of Parkinson's disease. In this
study, we design a compact and efficient network architecture to analyse the
distinctive handwriting patterns of patients' dynamic handwriting signals,
thereby providing an objective identification for the Parkinson's disease
diagnosis.
Methods: The proposed network is based on a hybrid deep learning approach
that fully leverages the advantages of both long short-term memory (LSTM) and
convolutional neural networks (CNNs). Specifically, the LSTM block is adopted
to extract the time-varying features, while the CNN-based block is implemented
using one-dimensional convolution for low computational cost. Moreover, the
hybrid model architecture is continuously refined under ablation studies for
superior performance. Finally, we evaluate the proposed method with its
generalization under a five-fold cross-validation, which validates its
efficiency and robustness.
Results: The proposed network demonstrates its versatility by achieving
impressive classification accuracies on both our new DraWritePD dataset
($96.2\%$) and the well-established PaHaW dataset ($90.7\%$). Moreover, the
network architecture also stands out for its excellent lightweight design,
occupying a mere $0.084$M of parameters, with a total of only $0.59$M
floating-point operations. It also exhibits near real-time CPU inference
performance, with inference times ranging from $0.106$ to $0.220$s.
Conclusions: We present a series of experiments with extensive analysis,
which systematically demonstrate the effectiveness and efficiency of the
proposed hybrid neural network in extracting distinctive handwriting patterns
for precise diagnosis of Parkinson's disease.
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