Adaptive time-frequency decomposition informer for pathological rest tremor sequence prediction

Pathological tremor is a common symptom of various neurological disorders. Pathological tremor signal prediction is important to the tremor suppression equipment. However, how to improve the accuracy of multi-step prediction of tremor motion is a difficult problem. In this paper, an adaptive time-frequency decomposition informer (ATFDI) method is proposed to predict the signal of pathological tremor. For this method, an adaptive oscillator is used to model the tremor signal, and the tremor signal is decomposed into multiple sub-signals with narrow band and single main frequency. After eliminating the redundant signal, the Informer method is used to predict multiple sub-signals, and the actual prediction signal is obtained. Corresponding to prediction length of 100 ms, 300 ms, 500 ms, 700 ms, and 1000 ms, the root mean square error (RMSE), correlation coefficient (r), and mean absolute error (MAE) between the predicted signal and the actual signal are 0.0827 ± 0.0424, 0.9186 ± 0.1192, and 0.0675 ± 0.0357, respectively. The average RMSE, r and MAE between the predicted signal and the actual signal for 100 ms prediction length are 0.0521 ± 0.0227, 0.9636 ± 0.0360, and 0.0443 ± 0.0190, respectively. Under balanced leave-one-subject-out (LOSO) evaluation across 20 subjects and five horizons from 100 to 1000 ms, forecasting performance degraded with longer horizons with mean MAE increasing from 0.133 to 0.200 and mean correlation decreasing from 0.813 to 0.545. The corresponding required execution time is 34.6 ms, which meets the real-time requirement. The proposed method is inspired by oscillatory properties of pathological tremor and attention-related signal modulation, and combines these ideas in an engineering prediction framework.