A unified methodology for heartbeats detection in seismocardiogram and ballistocardiogram signals

This work presents a methodology to analyze and segment both seismocardiogram (SCG) and ballistocardiogram (BCG) signals in a unified fashion. An unsupervised approach is followed to extract a template of SCG/BCG heartbeats, which is then used to fine-tune temporal waveform annotation. Rigorous performance assessment is conducted in terms of sensitivity, precision, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of annotation. The methodology is tested on four independent datasets, covering different measurement setups and time resolutions. A wide application range is therefore explored, which better characterizes the robustness and generality of the method with respect to a single dataset. Overall, sensitivity and precision scores are uniform across all datasets (p > 0.05 from the Kruskal–Wallis test): the average sensitivity among datasets is 98.7%, with 98.2% precision. On the other hand, a slight yet significant difference in RMSE and MAE scores was found (p < 0.01) in favor of datasets with higher sampling frequency. The best RMSE scores for SCG and BCG are 4.5 and 4.8 ms, respectively; similarly, the best MAE scores are 3.3 and 3.6 ms. The results were compared to relevant recent literature and are found to improve both detection performance and temporal annotation errors

Development of a bed-based nighttime monitoring toolset

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringSteven WarrenA movement is occurring within the healthcare field towards evidence-based or preventative care-based medicine, which requires personalized monitoring solutions. For medical technologies to fit within this framework, they need to adapt. Reduced cost of operation, ease-of-use, durability, and acceptance will be critical design considerations that will determine their success. Wearable technologies have shown the capability to monitor physiological signals at a reduced cost, but they require consistent effort from the user. Innovative unobtrusive and autonomous monitoring technologies will be needed to make personalized healthcare a reality. Ballistocardiography, a nearly forgotten field, has reemerged as a promising alternative for unobtrusive physiological monitoring. Heart rate, heart rate variability, respiration rate, movement, and additional hemodynamic features can be estimated from the ballistocardiogram (BCG). This dissertation presents a bed-based nighttime monitoring toolset designed to monitor BCG, respiration, and movement data motivated by the need to quantify the sleep of children with severe disabilities and autism – a capability currently unmet by commercial systems. A review of ballistocardiography instrumentation techniques (Chapter 2) is presented to 1) build an understanding of how the forces generated by the heart are coupled to the measurement apparatus and 2) provide a background of the field. The choice of sensing modalities and acquisition hardware and software for developing the unobtrusive bed-based nighttime monitoring platform is outlined in Chapters 3 and 4. Preliminary results illustrating the system’s ability to track physiological signals are presented in Chapter 5. Analyses were conducted on overnight data acquired from three lower-functioning children with autism (Chapters 6 and 9) who reside at Heartspring, Wichita, KS, where results justified the platform’s multi-sensor architecture and demonstrated the system’s ability to track physiological signals from this sensitive population over many months. Further, this dissertation presents novel BCG signal processing techniques – a signal quality index (Chapter 7) and a preprocessing inverse filter (Chapter 8) that are applicable to any ballistocardiograph. The bed-based nighttime monitoring toolset outlined in this dissertation presents an unobtrusive, autonomous, robust physiological monitoring system that could be used in hospital-based or personalized, home-based medical applications that consist of short or long-term monitoring scenarios