Diagnosis of chronic fatigue syndrome using beat-to-beat autonomic measurements.

BACKGROUND: An artificial intelligence (AI) pipeline was used to differentiate patients suffering from Chronic Fatigue Syndrome (CFS) from healthy controls (HC) based on high-frequency, large-scale data obtained using beat-to-beat measurement of the autonomic nervous system (ANS) and cardiovascular function. METHODS: This prospective, case-control study included a cohort of 112 CFS patients and 61 HCs examined. Heart rate (HR), high-frequency R-to-R interval (HF RRI), diastolic blood pressure (dBP), stroke volume (SV), and SV index (SV/FFM) were measured using the Task Force Monitor. A novel sequential learning approach was applied: first, a Transformer model was trained, followed by an XGBoost classifier that learned from the errors of the Transformer. Matthews correlation coefficient (MCC), accuracy, and Area Under the Receiver Operating Characteristic Curve (ROC AUC) were assessed. Model classifications were explained globally. RESULTS: The applied classifier achieved a subject-level accuracy of 0.89, an MCC of 0.79, and an AUC of 1.00. Lower values of beat-to-beat difference in HR and raw HF RRI (indicating reduced cardiac vagal tone) and higher values of dBP difference (more beat-to-beat increases, indicating higher sympathetic vascular tone) were related to being more likely classified as CFS patients. Low values of SV difference and low values of SV/FFM (both indicating less effective cardiac hemodynamics) were related to being more likely classified as CFS patients. CONCLUSIONS: The AI-driven classifier demonstrates remarkable proficiency in distinguishing between patients with CFS and HC. By leveraging this automated pipeline, beat-to-beat measurements of the ANS can significantly enhance the objective assessment of CFS diagnosis.