We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected f...
Continuous and non-invasive respiratory rate (RR) monitoring would significantly improve patient outcomes. Currently, RR is under-recorded in clinical environments and is often measured by manually counting breaths. In this work, we investigate the u...
Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient's daily activities and...
Non-contact physiological measurements have been under investigation for many years, and among these measurements is non-contact spirometry, which could provide acute and chronic pulmonary disease monitoring and diagnosis. This work presents a feasib...
Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates t...
In the past decade, deep learning models have been applied to bio-sensors used in a body sensor network for prediction. Given recent innovations in this field, the prediction accuracy of novel models needs to be evaluated for bio-signals. In this pap...
. To monitor the human respiration rate (RR) using infrared thermography (IRT) and artificial intelligence, in a completely contactless, automated, and non-invasive manner.. The human breathing signals (BS) were obtained using IRT, by plotting the ch...
Advanced materials (Deerfield Beach, Fla.)
35306703
Wearable respiratory monitoring is a fast, non-invasive, and convenient approach to provide early recognition of human health abnormalities like restrictive and obstructive lung diseases. Here, a computational fluid dynamics assisted on-mask sensor n...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
36086440
This paper evaluates a range of deep learning frameworks for detecting respiratory anomalies from input audio. Audio recordings of respiratory cycles collected from patients are transformed into time-frequency spectrograms to serve as front-end two-d...
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
36086237
Ambulatory respiration signal extraction system is required to maintain continuous surveillance of a patient with respiratory deficiency. The capnograph signal has received a lot of attention in recent years as a valuable indicator of respiratory con...