Deep Learning-based Classification of Patients with Postural Orthostatic Tachycardia Syndrome using Wearable ECG and Accelerometer Data

Postural Orthostatic Tachycardia Syndrome (POTS) is a chronic autonomic disorder characterized by chronic (> 3 months) orthostatic intolerance and an increase in heart rate (HR) of ≥ 30 beats per minute (bpm) without orthostatic hypotension. Traditional diagnostic approaches, such as the active standing or tilt-table test, are typically conducted under controlled clinical conditions, limiting their ability to capture the natural variability of symptoms and the intricate physiological responses occurring in daily life. These tests may cause patient discomfort, dizziness, nausea, or syncope. Furthermore, they are timeconsuming and cannot be used as a screening tool for POTS. To address these limitations, this study explored wearable devices that continuously collect physiological data-specifically, electrocardiogram (ECG) and accelerometer (ACC)-derived metrics-from POTS patients and healthy controls during routine daily activities. Physiological features around posturechange events identified in the data were processed and used to train and test a baseline deep learning model. The model demonstrated promising performance in accurately differentiating POTS patients from healthy controls in a relatively small cohort (66 from POTS patients and 20 from controls), indicating its potential as a feasibility study for clinical decision support. Future studies involving larger and more diverse samples under varying clinical conditions would be necessary to enhance the robustness and viability of our diagnostic model.