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 short-term 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.

中文翻译：

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用于检测 PASC 和 ME 个体视觉运动神经控制差异的机器学习算法

COVID-19 大流行已影响了全球数百万人，引起了被称为 SARS-CoV-2 (PASC) 感染急性后遗症的长期症状，俗称长期新冠肺炎。随着越来越多的人出现这些症状，早期干预至关重要。在这项研究中，我们引入了一种新方法，使用可收集脑电图 (EEG) 数据的可穿戴四通道头带来检测 PASC 或肌痛性脑脊髓炎 (ME) 的可能性。使用连续小波变换 (CWT) 处理原始脑电图信号，形成类似频谱图的矩阵，作为各种机器学习和深度学习模型的输入。我们采用 CONVLSTM（卷积长短期记忆）、CNN-LSTM 和 Bi-LSTM（双向长短期记忆）等模型。此外，我们还在传统机器学习模型上测试数据集以进行比较分析。我们的结果表明，性能最好的模型 CNN-LSTM 的准确率达到 83%。除了原始频谱图数据之外，我们还使用 Wasserstein 生成对抗网络 (WGAN) 生成合成频谱图来扩充我们的数据集。这些合成频谱图有助于训练阶段，解决数据量有限和患者隐私等挑战。令人印象深刻的是，在合成数据上训练的模型达到了 93% 的平均准确率，显着优于原始模型。这些结果证明了我们提出的方法在检测 PASC 和 ME 效果方面的可行性和有效性，为早期识别和管理病情铺平了道路。所提出的方法在各种实际应用中具有巨大的潜力，特别是在临床领域。它可用于评估 PASC 或 ME 患者的当前状况，并监测 PASC 患者的恢复过程，或任何干预措施对 PASC 和 ME 人群的有效性。通过实施这项技术，医疗保健专业人员可以促进更有效地管理慢性 PASC 或 ME 效应，确保及时干预并改善患有这些疾病的人的生活质量。