Prediction of Pathological Tremor Signals Using Long Short-Term Memory Neural Networks

Previous implementations of closed-loop peripheral electrical stimulation (PES) strategies have provided evidence about the effect of the stimulation timing on tremor reduction. However, these strategies have used traditional signal processing techniques that only consider phase prediction and might not model the non-stationary behavior of tremor. Here, we tested the use of long short-term memory (LSTM) neural networks to predict tremor signals using kinematic data recorded from Essential Tremor (ET) patients. A dataset comprising wrist flexion-extension data from 12 ET patients was pre-processed to feed the predictors. A total of 180 models resulting from the combination of network (neurons and layers of the LSTM networks, length of the input sequence and prediction horizon) and training parameters (learning rate) were trained, validated and tested. Predicted tremor signals using LSTM-based models presented high correlation values (from 0.709 to 0.998) with the expected values, with a phase delay between the predicted and real signals below 15 ms, which corresponds approximately to 7.5% of a tremor cycle. The prediction horizon was the parameter with a higher impact on the prediction performance. The proposed LSTM-based models were capable of predicting both phase and amplitude of tremor signals outperforming results from previous studies (32--56% decreased phase prediction error compared to the out-of-phase method), which might provide a more robust PES-based closed-loop control applied to PES-based tremor reduction.