Time Series Classification for Detecting Parkinson's Disease from Wrist Motions

Parkinson's disease (PD) is a neurodegenerative disease with frequently changing motor symptoms where continuous symptom monitoring enables more targeted treatment. Classical time series classification (TSC) and deep learning techniques have limited performance for PD symptom monitoring using wearable accelerometer data because PD movement patterns are complex, but datasets are small. We investigate InceptionTime and RandOm Convolutional KErnel Transform (ROCKET) because they are state-of-the-art for TSC and promising for PD symptom monitoring: InceptionTime's high learning capacity is suited to modeling complex movement patterns while ROCKET is suited to small datasets. We used a random search to find the highest-scoring InceptionTime architecture and compared it to ROCKET with a ridge classifier and a multi-layer perceptron (MLP) on wrist motions of PD patients. We find that all approaches are suitable for estimating tremor severity and bradykinesia presence but struggle with detecting dyskinesia. ROCKET performs better for dyskinesia, whereas InceptionTime is slightly better for tremor and bradykinesia but has much higher variability in performance. Both outperform the MLP. In conclusion, both InceptionTime and ROCKET are suitable for continuous symptom monitoring, with the choice depending on the symptom of interest and desired robustness.