Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism
Abstract
:1. Introduction
2. Literature Review: Deep Learning for Parkinson’s Disease Identification Based on Upper Limb Motion Data
2.1. Artificial Neural Networks
2.2. Convolutional Neural Networks
2.3. Long Short-Term Memory
2.4. Hybrid Deep Learning Architectures
2.5. Limitations in Previous Studies, Addressed in the Present Study
3. Methodology
3.1. Dataset Description
3.2. Data Preprocessing
3.3. Windowing and Feature Extraction
3.4. Implementation of Bidirectional LSTM with Attention
3.4.1. Bidirectional LSTM Layer
3.4.2. Attention Layer
3.4.3. Network Architecture
3.4.4. Experimental Setup and Implementation
4. Results
4.1. Training and Validation
4.2. Performance Evaluation on the Test Set
4.3. An Additional Experiment with a Subset of the Dataset
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
PD | Parkinson’s disease |
HC | Healthy control |
ML | Machine learning |
DL | Deep learning |
AI | Artificial intelligence |
Bi-LSTM | Bidirectional long short-term memory |
CNN | Convolutional neural network |
DNN | Deep neural network |
RNN | Recurrent neural network |
ANN | Artificial neural network |
IQR | Interquartile range |
AUC | Area under the curve |
ROC | Receiving operating characteristic |
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Feature | Description | Formula |
---|---|---|
Maximum | The maximum value in the signal (per axis). | |
Minimum | The minimum value in the signal (per axis). | |
Mean | The average value of the signal (per axis). | |
Standard deviation (std) | A measure of the dispersion or spread of the signal values around the mean (per axis). | |
Kurtosis | A measure of the peakedness or flatness of the signal’s distribution (per axis). | |
Zero Crossing Rate | The rate at which the signal changes its sign. | |
Skewness | A measure of the asymmetry of the signal’s distribution (per axis). | |
Correlation | The correlation coefficients between different signal components or dimensions (for xy, xz, yz axes). | |
Maximum PSD | The maximum power spectral density value in the signal. | |
Average PSD | The average power spectral density value in the signal. | |
Standard Deviation of PSD | A measure of the variation or spread of the power spectral density values. | |
Spectral Centroid | The center of mass of the power spectral density distribution, representing the average frequency content of the signal. | |
Spectral Rolloff | The frequency below which a specified percentage of the total power of the signal is contained. | |
Spectral Flatness | A measure of the tonality or noisiness of the signal. | |
Spectral Skewness | A measure of the asymmetry of the power spectral density distribution around its centroid. | |
Spectral Kurtosis | A measure of the peakedness or flatness of the power spectral density distribution around its centroid. | |
Entropy | A measure of the randomness or unpredictability of the signal, calculated using Shannon’s entropy formula [56]. | |
Total Energy | The total energy or power in the signal, calculated as the sum of the squared values. | |
Signal Magnitude Area | The sum of the absolute values of the signal. |
Layer | Neurons | Output Shape | Parameters | Activation |
---|---|---|---|---|
Input layer | 33 | (-, 33, 1) | 0 | - |
Bi-LSTM layer 1 | 32 | (-, 33, 64) | 8704 | - |
Dropout 1 | - | (-, 33, 64) | 0 | - |
Bi-LSTM layer 2 | 64 | (-, 33, 128) | 66,048 | - |
Dropout 2 | - | (-, 33, 128) | 0 | - |
Bi-LSTM layer 3 | 128 | (-, 33, 256) | 263,168 | - |
Attention layer | - | (-, 33, 256) | 0 | - |
Dropout 3 | - | (-, 33, 256) | 0 | - |
Flatten layer | - | (-, 8448) | 0 | - |
Dense layer | 1 | (-, 1) | 8449 | Sigmoid |
Model | Experiment | Accuracy | Precision | Recall | Specificity | F1-Score |
---|---|---|---|---|---|---|
LSTM [45] | Typing | 0.73 | ||||
Two-Stacked LSTM [46] | Writing | 0.91 | - | 1.00 | 0.65 | 0.94 |
CNN-BiLSTM [50] | Writing | 0.98 | - | 0.95 | 1.00 | |
DNN [33] | Hand poses | 0.95 | - | - | - | - |
BiLSTM + attention (proposed model) | Talking on the phone | 0.98 | 0.99 | 0.98 | 0.96 | 0.98 |
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Skaramagkas, V.; Boura, I.; Spanaki, C.; Michou, E.; Karamanis, G.; Kefalopoulou, Z.; Tsiknakis, M. Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism. Sensors 2023, 23, 7850. https://doi.org/10.3390/s23187850
Skaramagkas V, Boura I, Spanaki C, Michou E, Karamanis G, Kefalopoulou Z, Tsiknakis M. Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism. Sensors. 2023; 23(18):7850. https://doi.org/10.3390/s23187850
Chicago/Turabian StyleSkaramagkas, Vasileios, Iro Boura, Cleanthi Spanaki, Emilia Michou, Georgios Karamanis, Zinovia Kefalopoulou, and Manolis Tsiknakis. 2023. "Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism" Sensors 23, no. 18: 7850. https://doi.org/10.3390/s23187850
APA StyleSkaramagkas, V., Boura, I., Spanaki, C., Michou, E., Karamanis, G., Kefalopoulou, Z., & Tsiknakis, M. (2023). Detecting Minor Symptoms of Parkinson’s Disease in the Wild Using Bi-LSTM with Attention Mechanism. Sensors, 23(18), 7850. https://doi.org/10.3390/s23187850