Comparison of state-of-the-art deep learning architectures for detection of freezing of gait in Parkinson’s disease - Klaver, Emilie Charlotte; Heijink, Irene B.; Silvestri, Gianluigi; van Vugt, Jeroen P. P.; Janssen, Sabine; Nonnekes, Jorik; van Wezel, Richard J. A.; Tjepkema-Cloostermans, Marleen C. (Frontiers in Neurology, 2023)
Introduction Freezing of Gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson’s disease (PD). FOG detection is possible using acceleration data from wearable sensors and a convolutional neural network (CNN) is often used to determine the presence of FOG epochs. We compared the performance of a standard CNN for the detection of FOG with two more complex networks, which are well suited for time series data, the MiniRocket and the InceptionTime. Methods We combined acceleration data of people with PD across four studies. The final data set was split into a training (80%) and hold-out test (20%) set. A fifth study was included as unseen test set. The data were windowed (2 seconds) and five-fold cross-validation was applied. The CNN, MiniRocket and InceptionTime models were evaluated using a receiver operating characteristic (ROC) curve and it’s area under the curve (AUC). Multiple sensor configurations were evaluated for the best model. The geometric mean was subsequently calculated to select the optimal threshold. The selected model and threshold were evaluated on the hold-out and unseen test set. Results Seventy participants (23.7 hours, 9% FOG) were included in this study for training and testing, and in addition, ten participants provided an unseen test set (2.4 hours, 11% FOG). The CNN performed best (AUC=0.86) in comparison the InceptionTime (AUC=0.82) and Minirocket (AUC=0.76). For the CNN we found a similar performance for a seven (¬lumbar, upper and lower legs and feet; AUC=0.86), six (upper and lower legs and feet; AUC=0.87) and two sensor configuration (lower legs; AUC=0.86). The optimal threshold of 0.45 resulted in a sensitivity of 77% and specificity of 58% for the hold-out set (AUC=0.72), and a sensitivity of 85% and a specificity of 68% for the unseen test set (AUC=0.90). Conclusion We confirmed that deep learning can be used to detect FOG in a large, heterogenous dataset. The CNN model outperformed more complex networks. This model could be employed in future personalized interventions, with the ultimate goal of using automated FOG detection to trigger real-time cues to alleviate FOG in daily life.