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Found 4 papers, 2 papers with code
Boosted-SpringDTW for Comprehensive Feature Extraction of Physiological Signals
Goal: To achieve-high quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies.
Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise.
Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks
A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive.
A Survey of Challenges and Opportunities in Sensing and Analytics for Cardiovascular Disorders
We highlight three primary needs in the design of new smart health technologies: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making.
