Nicholas Conn, PhD

There is a pressing need to reduce the hospitalization rate of heart failure patients to limit rising health care costs and improve outcomes. Tracking physiologic changes to detect early deterioration in the home has the potential to reduce hospitalization rates through early intervention. However, classical approaches to in-home monitoring have had limited success, with patient adherence cited as a major barrier. This work presents a toilet seat–based cardiovascular monitoring system that has… Show more

There is a pressing need to reduce the hospitalization rate of heart failure patients to limit rising health care costs and improve outcomes. Tracking physiologic changes to detect early deterioration in the home has the potential to reduce hospitalization rates through early intervention. However, classical approaches to in-home monitoring have had limited success, with patient adherence cited as a major barrier. This work presents a toilet seat–based cardiovascular monitoring system that has the potential to address low patient adherence as it does not require any change in habit or behavior. Show less

Wearable and connected in-home medical devices are typically utilized in uncontrolled environments and often measure physiologic signals at suboptimal locations. Motion artifacts and reduced signal-to-noise ratio, compared with clinical grade equipment, results in a highly variable signal quality that can change significantly from moment to moment. The use of signal quality classification algorithms and robust feature delineation algorithms designed to achieve high accuracy on poor quality… Show more

Wearable and connected in-home medical devices are typically utilized in uncontrolled environments and often measure physiologic signals at suboptimal locations. Motion artifacts and reduced signal-to-noise ratio, compared with clinical grade equipment, results in a highly variable signal quality that can change significantly from moment to moment. The use of signal quality classification algorithms and robust feature delineation algorithms designed to achieve high accuracy on poor quality physiologic signals can prove beneficial in addressing concerns associated with measurement accuracy, confidence, and clinical validity. Show less

Cardiovascular disease is the leading cause of death in the United States. Tracking daily
changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular
disease, such as heart failure and hypertension. A toilet seat is the ideal device for
monitoring parameters relating to a subject’s cardiac health in his or her home, because it
is used consistently and requires no change in daily habit. The present work demonstrates
the ability to accurately… Show more

Cardiovascular disease is the leading cause of death in the United States. Tracking daily
changes in one’s cardiovascular health can be critical in diagnosing and managing cardiovascular
disease, such as heart failure and hypertension. A toilet seat is the ideal device for
monitoring parameters relating to a subject’s cardiac health in his or her home, because it
is used consistently and requires no change in daily habit. The present work demonstrates
the ability to accurately capture clinically relevant ECG metrics, pulse transit time-based
blood pressures, and other parameters across subjects and physiological states using a toilet
seat-based cardiovascular monitoring system, enabled through advanced signal processing
algorithms and techniques. Show less

A novel and easily implemented delineation algorithm is presented, which allows the foot of the photoplethsymogram to be located with sufficient accuracy for heart rate variability applications. This algorithm combines classical delineation techniques with the robustness of the wavelet transform. It can be implemented with a set of FIR filters and simple non-adaptive thresholding, making it suitable for real-time ambulatory applications. Results show that the accuracy of the algorithm matches… Show more

A novel and easily implemented delineation algorithm is presented, which allows the foot of the photoplethsymogram to be located with sufficient accuracy for heart rate variability applications. This algorithm combines classical delineation techniques with the robustness of the wavelet transform. It can be implemented with a set of FIR filters and simple non-adaptive thresholding, making it suitable for real-time ambulatory applications. Results show that the accuracy of the algorithm matches that of a standard electrocardiogram delineation algorithm, the current standard for heart rate variability applications. The algorithm presented herein is also compared against four state-of-the-art delineation algorithms. Using a database that contains exercise data from thirteen patients across six activity levels and 7012 beats, a temporal accuracy of 3.8±2.6 ms (mean±std) was achieved with a sensitivity of 99.29% and a positive predictive value of 99.23%. Show less

Compressed sensing has the possibility to significantly decrease the power consumption of wireless medical devices. The photoplethysmogram (PPG) is a device which can greatly benefit from compressed sensing due to the large amount of power needed to capture data. The aim of this paper is to determine if the least absolute shrinkage and selection operator (LASSO) optimization algorithm is the best approach for reconstructing a compressively sampled PPG across varying physiological states. The… Show more

Compressed sensing has the possibility to significantly decrease the power consumption of wireless medical devices. The photoplethysmogram (PPG) is a device which can greatly benefit from compressed sensing due to the large amount of power needed to capture data. The aim of this paper is to determine if the least absolute shrinkage and selection operator (LASSO) optimization algorithm is the best approach for reconstructing a compressively sampled PPG across varying physiological states. The results show that LASSO reconstruction approaches, but does not surpass, the reliability of constrained optimization. Show less