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

Hyunjun Choi1, Nicholas Matsumoto1, Xi Li2

  • 1Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G-541H, West Hollywood, USA www.cedars-sinai.org, AISupport@csmc.edu.

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Wearable sensors and AI can help diagnose Postural Orthostatic Tachycardia Syndrome (POTS), a chronic autonomic disorder. This study shows a deep learning model using ECG and accelerometer data can distinguish POTS patients from healthy individuals during daily activities.

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Area of Science:

  • Cardiology
  • Autonomic Neuroscience
  • Biomedical Engineering

Background:

  • Postural Orthostatic Tachycardia Syndrome (POTS) is a complex autonomic disorder causing orthostatic intolerance.
  • Current diagnostic methods like tilt-table tests have limitations, including discomfort and inability to capture real-world symptom variability.
  • There is a need for objective, non-invasive, and scalable diagnostic tools for POTS.

Purpose of the Study:

  • To investigate the utility of wearable devices for continuous physiological data collection in POTS patients.
  • To develop and evaluate a deep learning model for differentiating POTS patients from healthy controls using wearable sensor data.
  • To explore the potential of AI-driven diagnostics for POTS in routine daily activities.

Main Methods:

  • Continuous collection of electrocardiogram (ECG) and accelerometer (ACC) data using wearable devices during daily life.
  • Identification and processing of physiological features related to posture changes.
  • Training and testing a deep learning model on the collected physiological data from POTS patients and healthy controls.

Main Results:

  • The deep learning model demonstrated promising accuracy in distinguishing POTS patients from healthy controls.
  • The model effectively utilized ECG and ACC data to identify physiological patterns associated with POTS.
  • Feasibility of using wearable technology and AI for POTS diagnostics was established in a pilot cohort.

Conclusions:

  • Wearable sensors combined with deep learning offer a potential non-invasive approach for POTS diagnosis.
  • This technology could complement traditional diagnostic methods and facilitate earlier detection.
  • Further validation with larger, diverse cohorts is recommended to enhance model robustness for clinical decision support.