A Guide to Measuring Heart and Respiratory Rates Based on Off-the-Shelf Photoplethysmographic Hardware and Open-Source Software.

The remote monitoring of vital signs via wearable devices holds significant potential for alleviating the strain on hospital resources and elder-care facilities. Among the various techniques available, photoplethysmography stands out as particularly promising for assessing vital signs such as heart rate, respiratory rate, oxygen saturation, and blood pressure. Despite the efficacy of this method, many commercially available wearables, bearing Conformité Européenne marks and the approval of the Food and Drug Administration, are often integrated within proprietary, closed data ecosystems and are very expensive. In an effort to democratize access to affordable wearable devices, our research endeavored to develop an open-source photoplethysmographic sensor utilizing off-the-shelf hardware and open-source software components. The primary aim of this investigation was to ascertain whether the combination of off-the-shelf hardware components and open-source software yielded vital-sign measurements (specifically heart rate and respiratory rate) comparable to those obtained from more expensive, commercially endorsed medical devices. Conducted as a prospective, single-center study, the research involved the assessment of fifteen participants for three minutes in four distinct positions, supine, seated, standing, and walking in place. The sensor consisted of four PulseSensors measuring photoplethysmographic signals with green light in reflection mode. Subsequent signal processing utilized various open-source Python packages. The heart rate assessment involved the comparison of three distinct methodologies, while the respiratory rate analysis entailed the evaluation of fifteen different algorithmic combinations. For one-minute average heart rates' determination, the Neurokit process pipeline achieved the best results in a seated position with a Spearman's coefficient of 0.9 and a mean difference of 0.59 BPM. For the respiratory rate, the combined utilization of Neurokit and Charlton algorithms yielded the most favorable outcomes with a Spearman's coefficient of 0.82 and a mean difference of 1.90 BrPM. This research found that off-the-shelf components are able to produce comparable results for heart and respiratory rates to those of commercial and approved medical wearables.

Feasibility study of the use of a wearable vital sign patch in an intensive care unit setting.

Multiple studies and review papers have concluded that early warning systems have a positive effect on clinical outcomes, patient safety and clinical performances. Despite the substantial evidence affirming the efficacy of EWS applications, persistent barriers hinder their seamless integration into clinical practice. Notably, EWS, such as the National Early Warning Score, simplify multifaceted clinical conditions into singular numerical indices, thereby risking the oversight of critical clinical indicators and nuanced fluctuations in patients' health status. Furthermore, the optimal deployment of EWS within clinical contexts remains elusive. Manual assessment of EWS parameters exacts a significant temporal toll on healthcare personnel. Addressing these impediments necessitates innovative approaches. In this regard, wearable medical technologies emerge as promising solutions capable of continual monitoring of hospitalized patients' vital signs. To overcome the barriers of the use of early warning scores, wearable medical technology has the potential to continuously monitor vital signs of hospitalised patients. However, a fundamental inquiry arises regarding the comparability of their reliability to the current used golden standards. This inquiry underscores the imperative for rigorous evaluation and validation of wearable medical technologies to ascertain their efficacy in augmenting extant clinical practices. This prospective, single-center study aimed to evaluate the accuracy of heart rate and respiratory rate measurements obtained from the Vivalink Cardiac patch in comparison to the ECG-based monitoring system utilized at AZ Maria Middelares Hospital in Ghent. Specifically, the study focused on assessing the concordance between the data obtained from the Vivalink Cardiac patch and the established ECG-based monitoring system among a cohort of ten post-surgical intensive care unit (ICU) patients. Of these patients, five were undergoing mechanical ventilation post-surgery, while the remaining five were not. The study proceeded by initially comparing the data recorded by the Vivalink Cardiac patch with that of the ECG-based monitoring system. Subsequently, the data obtained from both the Vivalink Cardiac patch and the ECG-based monitoring system were juxtaposed with the information derived from the ventilation machine, thereby providing a comprehensive analysis of the patch's performance in monitoring vital signs within the ICU setting. For heart rate, the Vivalink Cardiac patch was on average within a 5% error range of the ECG-based monitoring system during 85.11±10.81% of the measured time. For respiratory rate this was during 40.55±17.28% of the measured time. Spearman's correlation coefficient showed a very high correlation of ρ = 0.9 8 for heart rate and a moderate correlation of ρ = 0.66 for respiratory rate. In comparison with the ventilated respiratory rate (ventilation machine) the Vivalink and ECG-based monitoring system both had a moderate correlation of ρ = 0.68 . A very high correlation was found between the heart rate measured by the Vivalink Cardiac patch and that of the ECG-based monitoring system of the hospital. Concerning respiratory rate the correlation between the data from the Vivalink Cardiac patch, the ECG-based monitoring system and the ventilation machine was found to be moderate.

An accelerometry and gyroscopy-based system for detecting swallowing and coughing events.

Measuring spontaneous swallowing frequencies (SSF), coughing frequencies (CF), and the temporal relationships between swallowing and coughing in patients could provide valuable clinical insights into swallowing function, dysphagia, and the risk of pneumonia development. Medical technology with these capabilities has potential applications in hospital settings. In the management of intensive care unit (ICU) patients, monitoring SSF and CF could contribute to predictive models for successful weaning from ventilatory support, extubation, or tracheal decannulation. Furthermore, the early prediction of pneumonia in hospitalized patients or home care residents could offer additional diagnostic value over current practices. However, existing technologies for measuring SSF and CF, such as electromyography and acoustic sensors, are often complex and challenging to implement in real-world settings. Therefore, there is a need for a simple, flexible, and robust method for these measurements. The primary objective of this study was to develop a system that is both low in complexity and sufficiently flexible to allow for wide clinical applicability. To construct this model, we recruited forty healthy volunteers. Each participant was equipped with two medical-grade sensors (Movesense MD), one attached to the cricoid cartilage and the other positioned in the epigastric region. Both sensors recorded tri-axial accelerometry and gyroscopic movements. Participants were instructed to perform various conscious actions on cue, including swallowing, talking, throat clearing, and coughing. The recorded signals were then processed to create a model capable of accurately identifying conscious swallowing and coughing, while effectively discriminating against other confounding actions. Training of the algorithm resulted in a model with a sensitivity of 70% (14/20), a specificity of 71% (20/28), and a precision of 66.7% (14/21) for the detection of swallowing and, a sensitivity of 100% (20/20), a specificity of 83.3% (25/30), and a precision of 80% (20/25) for the detection of coughing. SSF, CF and the temporal relationship between swallowing and coughing are parameters that could have value as predictive tools for diagnosis and therapeutic guidance. Based on 2 tri-axial accelerometry and gyroscopic sensors, a model was developed with an acceptable sensitivity and precision for the detection of swallowing and coughing movements. Also due to simplicity and robustness of the set-up, the model is promising for further scientific research in a wide range of clinical indications.