Journal of clinical monitoring and computing
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J Clin Monit Comput · Aug 2023
Continuous perioperative heart rate variability monitoring in video-assisted thoracoscopic surgery lobectomy-a pilot study.
Heart rate variability (HRV) is a measure of cardiac autonomic modulation and is potentially related to hypotension, postoperative atrial fibrillation, and orthostatic intolerance. However, there is a lack of knowledge on which specific time points and indices to measure. To improve future study design, there is a need for procedure-specific studies in an enhanced recovery after surgery (ERAS) video-assisted thoracic surgery (VATS) lobectomy setting, and for continuous measurement of perioperative HRV. ⋯ Further, preoperative HRV measures showed circadian variation. The patch was well tolerated among participants, but actions should be taken to ensure proper mounting of the measuring device. These results demonstrate a valid design platform for future HRV studies in relation to postoperative outcomes.
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J Clin Monit Comput · Aug 2023
Robust Non-Contact Monitoring of Respiratory Rate using a Depth Camera.
Respiratory rate (RR) is one of the most common vital signs with numerous clinical uses. It is an important indicator of acute illness and a significant change in RR is often an early indication of a potentially serious complication or clinical event such as respiratory tract infection, respiratory failure and cardiac arrest. Early identification of changes in RR allows for prompt intervention, whereas failing to detect a change may result in poor patient outcomes. Here, we report on the performance of a depth-sensing camera system for the continuous non-contact 'touchless' monitoring of Respiratory Rate. ⋯ We have demonstrated high accuracy in performance for respiratory rate based on a depth camera system. We have shown the ability to perform well at both high and low rates which are clinically important.
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J Clin Monit Comput · Aug 2023
Tracheal sound-based apnea detection using hidden Markov model in sedated volunteers and post anesthesia care unit patients.
The current method of apnea detection based on tracheal sounds is limited in certain situations. In this work, the Hidden Markov Model (HMM) algorithm based on segmentation is used to classify the respiratory and non-respiratory states of tracheal sounds, to achieve the purpose of apnea detection. Three groups of tracheal sounds were used, including two groups of data collected in the laboratory and a group of patient data in the post anesthesia care unit (PACU). ⋯ For the laboratory test data, apnea detection sensitivity, specificity, and accuracy were 96.9%, 95.5%, and 95.7%, respectively. For the clinical test data, apnea detection sensitivity, specificity, and accuracy were 83.1%, 99.0% and 98.6%. Apnea detection based on tracheal sound using HMM is accurate and reliable for sedated volunteers and patients in PACU.