Real-Time Autoregressive Forecast of Cardiac Features for Psychophysiological Applications

• Published in: IEEE journal of biomedical and health informatics Vol. PP; pp. 1 - 12
• Main Authors: Yaldiz, Cem O., Lin, David J., Gazi, Asim H., Cestero, Gabriela, Chen, Chuoqi, Bracken, Bethany K., Winder, Aaron, Lynn, Spencer, Sameni, Reza, Inan, Omer T.
• Format: Journal Article
• Language: English
• Published: United States IEEE 26.02.2025
• Subjects: aortic closing; aortic opening; Autoregressive modeling; Biomedical monitoring; Detectors; diastole; electrocardiogram; Electrocardiography; Feature extraction; Forecasting; Kalman filter; Noise; physiological forecasting; Predictive models; Psychophysics; Real-time systems; seismocardiogram; systole; Timing
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2025.3546148

Forecasting the near-exact moments of cardiac phases is crucial for several cardiovascular health applications. For instance, forecasts can enable the timing of specific stimuli (e.g., image or text presentation in psycholinguistic experiments) to coincide with cardiac phases like systole (cardiac ejection) and diastole (cardiac filling). This capability could be leveraged to enhance the amplitude of a subject's response, prompt them in fight-or-flight scenarios or conduct retrospective analysis for physiological predictive models. While autoregressive models have been employed for physiological signal forecasting, no prior study has explored their application to forecasting aortic opening and closing timings. This work addresses this gap by presenting a comprehensive comparative analysis of autoregressive models, including various forms of Kalman filter-based implementations, that use previously detected R-peak, aortic opening, and closing timings from electrocardiogram (ECG) and seismocardiogram (SCG) to forecast subsequent timings. We evaluate the robustness of these models to noise introduced in both SCG signals and the output of feature detectors. Our findings indicate that time-varying and multi-feature algorithms outperform others, with forecast errors below 2 ms for R-peak, below 3 ms for aortic opening timing, and below 10 ms for aortic closing timing. Importantly, we elucidate the distinct advantages of integrating multi-feature models, which improve noise robustness, and time-varying approaches, which adapt to rapid physiological changes. These models can be extended to a wide range of short-term physiological predictive systems, such as acute stress detection, neuromodulation sensor feedback, or muscle fatigue monitoring, broadening their applicability beyond cardiac feature forecasting.