Postdoctoral Research Fellow - Center for Advanced Cardiopulmonary Therapies and Transplantation (ACTAT)
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About The Position
This project aims to develop and validate a cardiac monitoring algorithm that uses commercially available smartwatches to detect and predict cardiac arrest in real time. Unlike existing consumer wearable technology, which primarily tracks single biomarkers such as heart rate, our approach will integrate multi-parameter physiological data, including heart rate variability, blood oxygen levels, blood pressure, respiration patterns, and signs of consciousness loss. By analyzing these signals together, the system will identify high-risk conditions and issue an early warning before cardiac arrest occurs. To achieve this, we will collect data from 300 patients, integrating smartwatch-derived physiological signals with hospital monitoring systems and implantable cardioverter-defibrillators (ICDs), which serve as the gold standard for cardiac event detection. The data will undergo statistical and machine learning-based validation using Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN) to improve the accuracy of smartwatch-based monitoring. Our goal is to develop an algorithm that can differentiate between normal physiological variations and early indicators of cardiac arrest, enhancing the reliability of consumer wearable devices for medical use.
