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

Feiyun Xiao1, Ruixue Gao1, Cheng Huang1

  • 1School of Mechanical Engineering, Hefei University of Technology, 230009, Hefei, China.

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|April 23, 2026
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Summary
This summary is machine-generated.

This study introduces an adaptive time-frequency decomposition informer (ATFDI) method for accurate pathological tremor signal prediction. The ATFDI method effectively models tremor signals, improving prediction accuracy for tremor suppression equipment.

Keywords:
Adaptive oscillatorInformerPathological tremorSignal predictionTime-frequency decomposition

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Pathological tremor is a prevalent symptom in various neurological disorders.
  • Accurate prediction of pathological tremor signals is crucial for developing effective tremor suppression equipment.
  • Improving the accuracy of multi-step tremor motion prediction remains a significant challenge.

Purpose of the Study:

  • To propose and evaluate an novel adaptive time-frequency decomposition informer (ATFDI) method for pathological tremor signal prediction.
  • To enhance the accuracy and real-time applicability of tremor signal forecasting.
  • To investigate the performance of the ATFDI method across different prediction horizons.

Main Methods:

  • Utilized an adaptive oscillator to model the tremor signal.
  • Decomposed the tremor signal into multiple narrow-band sub-signals with a single main frequency.
  • Applied the Informer method for multi-sub-signal prediction after redundant signal elimination.
  • Conducted leave-one-subject-out (LOSO) cross-validation across 20 subjects.

Main Results:

  • The ATFDI method demonstrated strong prediction accuracy, with root mean square error (RMSE) and mean absolute error (MAE) values as low as 0.0521 ± 0.0227 and 0.0443 ± 0.0190 for a 100 ms prediction length, respectively.
  • Prediction performance degraded with longer prediction horizons, showing an increase in mean MAE from 0.133 to 0.200 and a decrease in mean correlation from 0.813 to 0.545 for horizons from 100 ms to 1000 ms.
  • The method achieved a required execution time of 34.6 ms, meeting real-time requirements for tremor suppression applications.

Conclusions:

  • The proposed ATFDI method effectively predicts pathological tremor signals by leveraging adaptive time-frequency decomposition and the Informer model.
  • The method shows promise for real-time applications in tremor suppression equipment due to its speed and accuracy.
  • Future work could explore further optimizations for longer prediction horizons and diverse tremor characteristics.