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.

Echocardiographic assessment of aortic elastic properties with automated border detection in an ICU: in vivo application of the arctangent Langewouters model.

We studied whether combined pressure and transesophageal ultrasound monitoring is feasible in the intensive care unit (ICU) setting for global cardiovascular hemodynamic monitoring [systemic vascular resistance (SVR) and total arterial compliance (C(PPM))] and direct estimation of local ascending and descending aortic mechanical properties, i.e., distensibility and compliance coefficients (DC and CC). Pressure-area data were fitted to the arctangent Langewouters model, with aortic cross-sectional area obtained via automated border detection. Data were measured in 19 subjects at baseline, during infusion of sodium nitroprusside (SNP), and after washout. SNP infusion lowered SVR from 1.15 +/- 0.40 to 0.80 +/- 0.32 mmHg.ml(-1).s (P < 0.05), whereas C(PPM) increased from 0.87 +/- 0.46 to 1.02 +/- 0.42 ml/mmHg (P < 0.05). DC and CC increased from 0.0018 +/- 0.0007 to 0.0025 +/- 0.0009 l/mmHg (P < 0.05) and from 0.0066 +/- 0.0028 to 0.0083 +/- 0.0026 cm2/mmHg (P < 0.05), respectively, at the descending, but not ascending, aorta. The Langewouters model fitted the descending aorta data reasonably well. Assessment of local mechanical properties of the human ascending aorta in a clinical setting by automated border detection remains technically challenging.