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. 2023 Sep 13;23(18):7850.
doi: 10.3390/s23187850.

Detecting Minor Symptoms of Parkinson's Disease in the Wild Using Bi-LSTM with Attention Mechanism

Vasileios Skaramagkas  1   2 , Iro Boura  3   4 , Cleanthi Spanaki  3   5 , Emilia Michou  6 , Georgios Karamanis  7 , Zinovia Kefalopoulou  7 , Manolis Tsiknakis  1   2
Affiliations

Detecting Minor Symptoms of Parkinson's Disease in the Wild Using Bi-LSTM with Attention Mechanism

Vasileios Skaramagkas et al. Sensors (Basel). .

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and nonmotor impairment with various implications on patients' quality of life. Since currently available therapies are only symptomatic, identifying individuals with prodromal, preclinical, or early-stage PD is crucial, as they would be ideal candidates for future disease-modifying therapies. Our analysis aims to develop a robust model for accurate PD detection using accelerometer data collected from PD and non-PD individuals with mild or no tremor during phone conversations. An open-access dataset comprising accelerometer recordings from 22 PD patients and 11 healthy controls (HCs) was utilized. The data were preprocessed to extract relevant time-, frequency-, and energy-related features, and a bidirectional long short-term memory (Bi-LSTM) model with attention mechanism was employed for classification. The performance of the model was evaluated using fivefold cross-validation, and metrics of accuracy, precision, recall, specificity, and f1-score were computed. The proposed model demonstrated high accuracy (98%), precision (99%), recall (98%), specificity (96%), and f1-score (98%) in accurately distinguishing PD patients from HCs. Our findings indicate that the proposed model outperforms existing approaches and holds promise for detection of PD with subtle symptoms, like tremor, in the wild. Such symptoms can present in the early or even prodromal stage of the disease, and appropriate mobile-based applications may be a practical tool in real-life settings to alert individuals at risk to seek medical assistance or give patients feedback in monitoring their symptoms.

Keywords: Bi-LSTM with attention; Parkinson’s disease; accelerometer; deep learning; in the wild detection.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
LSTM cell internal architecture.
Figure 2
Figure 2
Bidirectional LSTM network model.
Figure 3
Figure 3
Proposed Bi-LSTM with attention network architecture.
Figure 4
Figure 4
Training and validation accuracy curves for every epoch for BiLSTM with attention model.
Figure 5
Figure 5
Training and validation loss curves for every epoch for BiLSTM with attention model.
Figure 6
Figure 6
Box plots showing the distribution of training loss, validation loss, training accuracy, and validation accuracy across different folds at each epoch.
Figure 7
Figure 7
ROC curves for each fold for BiLSTM with attention model.
See this image and copyright information in PMC

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