Non-invasive cuffless blood pressure and heart rate monitoring using impedance cardiography

IF 4.4 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Sudipta Ghosh , Bhabani Prasad Chattopadhyay , Ram Mohan Roy , Jayanta Mukherjee , Manjunatha Mahadevappa
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引用次数: 3

Abstract

Background

Continuous blood pressure (BP) monitoring provides additional information about how changes in BP may correlate with daily activities and sleep patterns. Recommendations from the American Heart Association and American College of Cardiology strongly suggest confirming a diagnosis of hypertension with continuous BP monitoring. Non-invasive and non-intrusive detection of haemodynamic parameters is emerging as a norm, based on self-monitoring wearable medical devices. Researchers have carried out several studies using non-invasive and continuous BP measurements as an alternative to conventional cuff-based measurements. In this work, we proposed a novel method for cuffless estimation of BP using impedance cardiography (ICG).

Methods

We conducted a single-centre, cross-sectional study of 104 subjects (of whom 30 were categorized as controls and the remaining 74 as the disease group) at the Medical College and Hospital, Kolkata. The disease group consisted of patients with confirmed coronary artery disease, while the individuals in the control group were deemed to be healthy. All subjects underwent electrocardiogram recording by on-duty doctors in order to determine their health status. A custom-made device based on the principle of impedance plethysmography was designed to record impedance changes due to subjects’ peripheral blood flow. The device was used to record ICG signals. In this study, we developed a novel auto-adaptive algorithm based on ICG signals for non-invasive, cuffless, continuous monitoring of BP and heart rate. Separate mathematical models were developed for all the estimated parameters (BP and heart rate) for both the study groups (control and disease). The developed models were auto-adaptive and did not require subject-specific calibration. Performance indicators including, r2, error percentage, standard deviation, and mean difference were used to quantify the performance of the models.

Results

The ICG signal recorded by the device was used to extract features and compute the augmentation index. The calculated augmentation index values showed strong correlations with systolic BP (r=0.99, P<0.05), diastolic BP (r=0.95, P<0.05), and heart rate (r=0.78, P<0.05). The models were also shown to have a high degree of accuracy for systolic and diastolic BP. Error margins were in the range ±2.33 and ±1.79 mmHg for systolic BP in disease and control subjects, respectively, and ±3.60 and ±1.82 mmHg for diastolic BP. However, the accuracy was lower in the prediction of heart rate in disease subjects, with a reported r2 value of 0.72 and an error margin of ±2.88 beats per min; for healthy subjects, the results were marginally better, with an error margin of ±1.82 beats per min. All statistical analyses were performed using MATLAB (R2017a, MathWorks, USA).

Conclusion

In this study, we developed a non-invasive cuffless approach for estimation of systemic peripheral BP and heart rate using ICG. The proposed methodology eliminated any discomfort to patients caused by inflation of the cuff (in the case of cuff-based BP monitoring) or the need to constantly wear a fingertip photoplethysmography device (in the case of cuffless BP monitoring). The results obtained appeared promising and increased the potential scope of ICG for monitoring other haemodynamic parameters related to heart function.

无创无袖带血压和心率监测使用阻抗心动图
背景:持续监测血压(BP)可以提供额外的信息,了解血压变化与日常活动和睡眠模式之间的关系。美国心脏协会和美国心脏病学会的建议强烈建议通过持续血压监测来确认高血压的诊断。基于自我监测的可穿戴医疗设备,非侵入性和非侵入性血液动力学参数检测正在成为一种规范。研究人员已经进行了几项研究,使用无创和连续的血压测量来替代传统的袖带测量。在这项工作中,我们提出了一种使用阻抗心动图(ICG)进行无断口估计的新方法。方法我们在加尔各答医学院和医院对104名受试者进行了单中心横断面研究(其中30人被归类为对照组,其余74人被归类为疾病组)。疾病组由确诊的冠状动脉疾病患者组成,而对照组的个体被认为是健康的。所有受试者均由值班医生进行心电图记录,以确定其健康状况。设计了一种基于阻抗容积描记原理的定制装置,用于记录受试者外周血流量引起的阻抗变化。该装置用于记录ICG信号。在这项研究中,我们开发了一种新的基于ICG信号的自适应算法,用于无创、无袖、连续监测血压和心率。为两个研究组(对照组和疾病组)的所有估计参数(血压和心率)建立了单独的数学模型。开发的模型是自适应的,不需要受试者特定的校准。采用r2、误差百分比、标准差、均差等性能指标来量化模型的性能。结果利用装置记录的ICG信号提取特征,计算增强指数。计算出的增强指数值与收缩压(r=0.99, P<0.05)、舒张压(r=0.95, P<0.05)和心率(r=0.78, P<0.05)有很强的相关性。该模型对收缩压和舒张压也有很高的准确性。疾病组和对照组的收缩压误差范围分别为±2.33和±1.79 mmHg,舒张压误差范围分别为±3.60和±1.82 mmHg。然而,在疾病受试者中预测心率的准确性较低,报告的r2值为0.72,误差范围为±2.88次/分钟;对于健康受试者,结果略好,误差范围为±1.82次/分钟。所有统计分析均使用MATLAB (R2017a, MathWorksⓇ,USA)进行。在这项研究中,我们开发了一种无创性的方法来估计全身外周血压和心率的ICG。所提出的方法消除了由于袖带膨胀(在基于袖带的血压监测的情况下)或需要经常佩戴指尖光电脉搏描记仪(在无袖带的血压监测的情况下)给患者造成的任何不适。所获得的结果看起来很有希望,并增加了ICG监测与心功能相关的其他血流动力学参数的潜在范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intelligent medicine
Intelligent medicine Surgery, Radiology and Imaging, Artificial Intelligence, Biomedical Engineering
CiteScore
5.20
自引率
0.00%
发文量
19
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