Automatic Detection and Assessment of Freezing of Gait Manifestations
| Autor: | Po-Kai Yang, Benjamin Filtjens, Pieter Ginis, Maaike Goris, Alice Nieuwboer, Moran Gilat, Peter Slaets, Bart Vanrumste |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 2699-2708 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1534-4320 1558-0210 |
| DOI: | 10.1109/TNSRE.2024.3431208 |
| Popis: | Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson’s disease (PD). Although described as a single phenomenon, FOG is heterogeneous and can express as different manifestations, such as trembling in place or complete akinesia. We aimed to analyze the efficacy of deep learning (DL) trained on inertial measurement unit data to classify FOG into both manifestations. We adapted and compared four state-of-the-art FOG detection algorithms for this task and investigated the advantages of incorporating a refinement model to address oversegmentation errors. We evaluated the model’s performance in distinguishing between trembling and akinesia, as well as other forms of movement cessation (e.g., stopping and sitting), against gold-standard video annotations. Experiments were conducted on a dataset of eighteen PD patients completing a FOG-provoking protocol in a gait laboratory. Results showed our model achieved an F1 score of 0.78 and segment F1@50 of 0.75 in detecting FOG manifestations. Assessment of FOG severity was strong for trembling (ICC=0.86, [0.66,0.95]) and moderately strong for akinesia (ICC=0.78, [0.51,0.91]). Importantly, our model successfully differentiated FOG from other forms of movement cessation during 360-degree turning-in-place tasks. In conclusion, our study demonstrates that DL can accurately assess different types of FOG manifestations, warranting further investigation in larger and more diverse verification cohorts. |
| Databáze: | Directory of Open Access Journals |
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