PDF(Pubmed)
Abstract：
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.
摘要：
通过可穿戴设备远程监测生命体征对于减轻医院资源和老年护理设施的压力具有巨大潜力。在各种可用的技术中，光电体积描记术是特别有前途的评估生命体征，如心率，呼吸频率,氧饱和度,还有血压.尽管这种方法有效，许多市售的可穿戴设备，带有欧洲认证标志和食品药品监督管理局的批准，通常集成在专有的内部，封闭的数据生态系统，非常昂贵。为了使人们获得负担得起的可穿戴设备民主化，我们的研究努力开发一个开源的光电容积描记传感器利用现成的硬件和开源的软件组件。这项调查的主要目的是确定现成的硬件组件和开源软件的组合是否产生了与从更昂贵的，商业认可的医疗器械。作为一个潜在的，单中心研究,这项研究包括在四个不同的位置对15名参与者进行三分钟的评估，仰卧,就座,站立,在原地行走。传感器由四个PulseSensors组成，在反射模式下使用绿光测量光电体积描记信号。随后的信号处理使用了各种开源Python包。心率评估涉及三种不同方法的比较，而呼吸频率分析需要评估15种不同的算法组合。对于一分钟平均心率的测定，Neurokit工艺管道在Spearman\s系数为0.9且平均差为0.59BPM的坐姿下取得了最佳结果。对于呼吸频率，Neurokit和Charlton算法的联合应用产生了最有利的结果,Spearman's系数为0.82,平均差为1.90BrPM.这项研究发现，现成的组件能够产生与商业和批准的医疗可穿戴设备相当的心脏和呼吸频率结果。
