Trial Report Machine Learning Algorithms for Detection of Visuomotor Neural Control Differences in Individuals with PASC and ME, 2024, Ahuja

https://www.frontiersin.org/articles/10.3389/fnhum.2024.1359162/abstract

Machine Learning Algorithms for Detection of Visuomotor Neural Control Differences in Individuals with PASC and ME

ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Brain-Computer Interfaces
Volume 18 - 2024 | doi: 10.3389/fnhum.2024.1359162
Harit Ahuja1* Smriti Badhwar1 Heather Edgell1 Lauren E. Sergio1 Marin Litoiu1

• 1York University, Canada

The final, formatted version of the article will be published soon.


The COVID-19 pandemic has affected millions worldwide, giving rise to long-term symptoms known as post-acute sequelae of SARS-CoV-2 (PASC) infection, colloquially referred to as long COVID.

With an increasing number of people experiencing these symptoms, early intervention is crucial.

In this study, we introduce a novel method to detect the likelihood of PASC or Myalgic Encephalomyelitis (ME) using a wearable four-channel headband that collects Electroencephalogram (EEG) data.

The raw EEG signals are processed using Continuous Wavelet Transform (CWT) to form a spectrogram-like matrix, which serves as input for various machine learning and deep learning models. We employ models such as CONVLSTM (Convolutional Long Short-Term Memory), CNN-LSTM, and Bi-LSTM (Bidirectional Long shortterm memory).

Additionally, we test the dataset on traditional machine learning models for comparative analysis.

Our results show that the best-performing model, CNN-LSTM, achieved an accuracy of 83%. In addition to the original spectrogram data, we generated synthetic spectrograms using Wasserstein Generative Adversarial Networks (WGANs) to augment our dataset.

These synthetic spectrograms contributed to the training phase, addressing challenges such as limited data volume and patient privacy.

Impressively, the model trained on synthetic data achieved an average accuracy of 93%, significantly outperforming the original model.

These results demonstrate the feasibility and effectiveness of our proposed method in detecting the effects of PASC and ME, paving the way for early identification and management of the condition.

The proposed approach holds significant potential for various practical applications, particularly in the clinical domain.

It can be utilized for evaluating the current condition of individuals with PASC or ME, and monitoring the recovery process of those with PASC, or the efficacy of any interventions in the PASC and ME populations.

By implementing this technique, healthcare professionals can facilitate more effective management of chronic PASC or ME effects, ensuring timely intervention and improving the quality of life for those experiencing these conditions.



Keywords: PASC, machine learning, Mylagic encephalomyelitis/chronic fatigue syndrome (ME/CFS), cognitive motor integration, synthetic data

 

I've often wondered why there hasn't been more research along these lines. Symptoms such as brainfog or neuropathic pain should show different signals; it's just a matter of adequate sensors and processing.

I wonder whether--with enough development--a similar technique could be used to help people decide on career paths, choose entertainment, or deal optimally with problems. "Your brainscan shows that you only have a 14% chance of being happy with a career in dentistry..."

 

Full text just published here:
https://www.frontiersin.org/articles/10.3389/fnhum.2024.1359162/full