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Subclinical tremor differentiation using long short-term memory networks.

Gerard Ruchin Randil Nanayakkara¹, Ping Yi Chan^2,3,4

¹Electrical and Robotics Engineering Department, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Selangor, Malaysia.

Physical and Engineering Sciences in Medicine

|February 24, 2025

Summary

Artificial intelligence (AI) can now differentiate subtle tremors in Parkinson's disease (PD) and essential tremor (ET). A deep learning model achieved high accuracy, improving diagnosis for subclinical tremors.

Keywords:

Essential tremor LSTM Parkinson’s disease Subclinical tremor

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Area of Science:

Neurology
Biomedical Engineering
Artificial Intelligence

Background:

Subclinical tremors in Parkinson's disease (PD) and essential tremor (ET) present diagnostic challenges, even with apparent tremors.
Up to 30% of PD cases have subclinical tremors, a poorly understood phenomenon.
Accurate differentiation is crucial for timely diagnosis and treatment planning.

Purpose of the Study:

To develop and evaluate a deep learning model for differentiating subclinical tremors.
To distinguish between tremors associated with PD, ET, and normal physiological tremors.
To leverage artificial intelligence (AI) for improved diagnostic accuracy in movement disorders.

Main Methods:

Utilized inertial sensor data from hands and arms of 51 PD, 15 ET, and 58 normal subjects.
Developed a deep learning model using a long short-term memory (LSTM) network.
Trained the LSTM network on short-time Fourier transformed subclinical tremor data.

Main Results:

The LSTM model achieved 95% accuracy differentiating PD and ET tremors.
The model achieved 93% accuracy differentiating PD, ET, and physiological tremors.
The proposed method demonstrated 30-50% higher accuracy for low-amplitude tremors compared to existing methods.

Conclusions:

The AI-driven LSTM model shows significant potential for differentiating subclinical tremors.
This approach can enhance diagnostic accuracy for Parkinson's disease and essential tremor.
Future work includes model interpretability and validation on larger, diverse datasets.