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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.

Objectives There are substantial variations in entry criteria for heart failure (HF) clinics, leading to variations in whom providers refer for these life-saving services. This study investigated actual versus ideal HF clinic inclusion or exclusion criteria and how that related to referring providers' perspectives of ideal criteria.Design, setting and participants Two cross-sectional surveys were administered via research electronic data capture to clinic providers and referrers (eg, cardiologists, family physicians and nurse practitioners) across Canada.Measures Twenty-seven criteria selected based on the literature and HF guidelines were tested. Respondents were asked to list any additional criteria. The degree of agreement was assessed (eg, Kappa).Results Responses were received from providers at 48 clinics (37.5% response rate). The most common actual inclusion criteria were newly diagnosed HF with reduced or preserved ejection fraction, New York Heart Association class IIIB/IV and recent hospitalisation (each endorsed by >74% of respondents). Exclusion criteria included congenital aetiology, intravenous inotropes, a lack of specialists, some non-cardiac comorbidities and logistical factors (eg, rurality and technology access). There was the greatest discordance between actual and ideal criteria for the following: inpatient at the same institution (κ=0.14), congenital heart disease, pulmonary hypertension or genetic cardiomyopathies (all κ=0.36). One-third (n=16) of clinics had changed criteria, often for non-clinical reasons. Seventy-three referring providers completed the survey. Criteria endorsed more by referrers than clinics included low blood pressure with a high heart rate, recurrent defibrillator shocks and intravenous inotropes—criteria also consistent with guidelines.Conclusions There is considerable agreement on the main clinic entry criteria, but given some discordance, two levels of clinics may be warranted. Publicising evidence-based criteria and applying them systematically at referral sources could support improved HF patient care journeys and outcomes.