scholarly journals A Non-Invasive Continuous Blood Pressure Estimation Approach Based on Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2585 ◽  
Author(s):  
Shuo Chen ◽  
Zhong Ji ◽  
Haiyan Wu ◽  
Yingchao Xu
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Measurement Techniques ◽  
Pulse Transit Time ◽  
Estimation Errors ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
The Impact ◽  
Estimation Models

Considering the existing issues of traditional blood pressure (BP) measurement methods and non-invasive continuous BP measurement techniques, this study aims to establish the systolic BP and diastolic BP estimation models based on machine learning using pulse transit time and characteristics of pulse waveform. In the process of model construction, the mean impact value method was introduced to investigate the impact of each feature on the models and the genetic algorithm was introduced to implement parameter optimization. The experimental results showed that the proposed models could effectively describe the nonlinear relationship between the features and BP and had higher accuracy than the traditional methods with the error of 3.27 ± 5.52 mmHg for systolic BP and 1.16 ± 1.97 mmHg for diastolic BP. Moreover, the estimation errors met the requirements of the Advancement of Medical Instrumentation and British Hypertension Society criteria. In conclusion, this study was helpful in promoting the practical application of methods for non-invasive continuous BP estimation models.

scholarly journals Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

2021 ◽  
Vol 53 (2) ◽  
Author(s):  
Sen Yang ◽  
Yaping Zhang ◽  
Siu-Yeung Cho ◽  
Ricardo Correia ◽  
Stephen P. Morgan
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Deep Learning ◽  
Absolute Error ◽  
Learning Model ◽  
Physiological Measurement ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Blood Pressure Estimation ◽  
Deep Learning Model

AbstractConventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

scholarly journals Non-Invasive Risk Stratification of Hypertension: A Systematic Comparison of Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 34
Author(s):  
Giovanna Sannino ◽  
Ivanoe De Falco ◽  
Giuseppe De Pietro
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Risk Stratification ◽  
Learning Algorithms ◽  
Circulatory System ◽  
Machine Learning Algorithms ◽  
Learning Mechanisms ◽  
Data Set ◽  
Non Invasive ◽  
Blood Pressure Estimation

One of the most important physiological parameters of the cardiovascular circulatory system is Blood Pressure. Several diseases are related to long-term abnormal blood pressure, i.e., hypertension; therefore, the early detection and assessment of this condition are crucial. The identification of hypertension, and, even more the evaluation of its risk stratification, by using wearable monitoring devices are now more realistic thanks to the advancements in Internet of Things, the improvements of digital sensors that are becoming more and more miniaturized, and the development of new signal processing and machine learning algorithms. In this scenario, a suitable biomedical signal is represented by the PhotoPlethysmoGraphy (PPG) signal. It can be acquired by using a simple, cheap, and wearable device, and can be used to evaluate several aspects of the cardiovascular system, e.g., the detection of abnormal heart rate, respiration rate, blood pressure, oxygen saturation, and so on. In this paper, we take into account the Cuff-Less Blood Pressure Estimation Data Set that contains, among others, PPG signals coming from a set of subjects, as well as the Blood Pressure values of the latter that is the hypertension level. Our aim is to investigate whether or not machine learning methods applied to these PPG signals can provide better results for the non-invasive classification and evaluation of subjects’ hypertension levels. To this aim, we have availed ourselves of a wide set of machine learning algorithms, based on different learning mechanisms, and have compared their results in terms of the effectiveness of the classification obtained.

scholarly journals Analyzing 24-Hour Blood Pressure Measurements with a Novel Cuffless Pulse Transit Time Device in Clinical Practice—Does the Software for Heartbeat Detection Matter?

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 361
Author(s):  
Leo Kilian ◽  
Philipp Krisai ◽  
Thenral Socrates ◽  
Christian Arranto ◽  
Otmar Pfister ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Transit Time ◽  
Pulse Transit Time ◽  
Population Level ◽  
Raw Data ◽  
Invasive Blood Pressure ◽  
Individual Level ◽  
Non Invasive ◽  
The Individual ◽  
The Impact

Background: The Somnotouch-Non-Invasive-Blood-Pressure (NIBP) device delivers raw data consisting of electrocardiography and photoplethysmography for estimating blood pressure (BP) over 24 h using pulse-transit-time. The study’s aim was to analyze the impact on 24-hour BP results when processing raw data by two different software solutions delivered with the device. Methods: We used data from 234 participants. The Somnotouch-NIBP measurements were analyzed using the Domino-light and Schiller software and compared. BP values differing >5 mmHg were regarded as relevant and explored for their impact on BP classification (normotension vs. hypertension). Results: Mean (±standard deviation) absolute systolic/diastolic differences for 24-hour mean BP were 1.5 (±1.7)/1.1 (±1.3) mm Hg. Besides awake systolic BP (p = 0.022), there were no statistically significant differences in systolic/diastolic 24-hour mean, awake, and asleep BP. Twenty four-hour mean BP agreement (number (%)) between the software solutions within 5, 10, and 15 mmHg were 222 (94.8%), 231 (98.7%), 234 (100%) for systolic and 228 (97.4%), 232 (99.1%), 233 (99.5%) for diastolic measurements, respectively. A BP difference of >5 mmHg was present in 24 (10.3%) participants leading to discordant classification in 4–17%. Conclusion: By comparing the two software solutions, differences in BP are negligible at the population level. However, at the individual level there are, in a minority of cases, differences that lead to different BP classifications, which can influence the therapeutic decision.

scholarly journals Pengukuran Tekanan Darah Non-Invasive Tanpa Manset Menggunakan Metode Pulse Transit Time Berbasis Machine Learning Multivariat Regresi

2019 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Ernia Susana
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Transit Time ◽  
Pressure Measurement ◽  
Blood Pressure Measurement ◽  
Sequential Estimation ◽  
Pulse Transit Time ◽  
P Value ◽  
Oscillometric Method ◽  
Non Invasive

<p>Currently used non-invasive blood pressure (NIBP) measurements (oscillometric method) has disadvantages related to pumping cuffs which can cause discomfort for patients due to pressure from pumping cuffs.  The aim of this study was to measure blood pressure in a non-invasive manner without cuffs with the Pulse Transit Time (PTT) methodbase on machine learning technology.The blood pressure measurement by the PTT method is obtained from the calculation of the distance of the R-ECG wave with the peak signal <em>photoplethysmogram</em><em> </em>(PPG). The main problem of the PTT method in some previous studies is that the estimation of systolic (SBP) and diastolic (DBP) values is still inaccurate. The blood pressure measurement method in this study used a combination of PTT calculations with machine learning multivariate regression. Therefore expected to obtain a more accurate estimate of systolic (SBP) and diastolic blood pressure (DBP). This study is a laboratory experiment research on 30 healthy volunteers aged 20 ± 1 years. The measurement of the blood pressure value of the PTT-to-oscillometric method is 5 ± 5 mmHg. The blood pressure values generated by this PTT method have a p-value for the sequential estimation of SBP and DBP of 0.7374 and 0.0262.<strong></strong></p>

scholarly journals Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Sensors ◽  
2018 ◽  
Vol 18 (4) ◽  
pp. 1160 ◽  
Author(s):  
Monika Simjanoska ◽  
Martin Gjoreski ◽  
Matjaž Gams ◽  
Ana Madevska Bogdanova
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Machine Learning Techniques ◽  
Invasive Blood Pressure ◽  
Non Invasive ◽  
Learning Techniques ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

scholarly journals Non-Invasive Continuous Blood-Pressure Monitoring Models Based on Photoplethysmography and Electrocardiography

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5543 ◽  
Author(s):  
Haiyan Wu ◽  
Zhong Ji ◽  
Mengze Li
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Bp Neural Network ◽  
Blood Pressure Monitoring ◽  
Individual Characteristics ◽  
Pressure Monitoring ◽  
Estimation Errors ◽  
Non Invasive ◽  
The Impact ◽  
Continuous Blood Pressure

Blood pressure is an extremely important blood hemodynamic parameter. The pulse wave contains abundant blood-pressure information, and the convenience and non-invasivity of its measurement make it ideal for non-invasive continuous monitoring of blood pressure. Based on combined photoplethysmography and electrocardiogram signals, this study aimed to extract the waveform information, introduce individual characteristics, and construct systolic and diastolic blood-pressure (SBP and DBP) estimation models using the back-propagation error (BP) neural network. During the model construction process, the mean impact value method was employed to investigate the impact of each feature on the model output and reduce feature redundancy. Moreover, the multiple population genetic algorithm was applied to optimize the BP neural network and determine the initial weights and threshold of the network. Finally, the models were integrated for further optimization to generate the final individualized continuous blood-pressure monitoring models. The results showed that the predicted values of the model in this study correlated significantly with the measured values of the electronic sphygmomanometer. The estimation errors of the model met the Association for the Advancement of Medical Instrumentation (AAMI) criteria (the SBP error was 2.5909 ± 3.4148 mmHg, and the DBP error was 2.6890 ± 3.3117 mmHg) and the Grade A British Hypertension Society criteria.

scholarly journals Pulse Transit Time Derivation Using Xiphoidal and Carotid Seismocardiograms

2017 ◽  
Author(s):  
Ajay K. Verma ◽  
John Zanetti ◽  
Reza Fazel-Rezai ◽  
Kouhyar Tavakolian
Keyword(s):  
Blood Pressure ◽  
Aortic Valve ◽  
Transit Time ◽  
Pulse Wave ◽  
Blood Pressure Measurement ◽  
Pulse Transit Time ◽  
Non Invasive ◽  
Arterial Blood ◽  
Pressure Estimation ◽  
Blood Pressure Estimation

Blood pressure is an indicator of a cardiovascular functioning and could provide early symptoms of cardiovascular system impairment. Blood pressure measurement using catheterization technique is considered the gold standard for blood pressure measurement [1]. However, due its invasive nature and complexity, non-invasive techniques of blood pressure estimation such as auscultation, oscillometry, and volume clamping have gained wide popularity [1]. While these non-invasive cuff based methodologies provide a good estimate of blood pressure, they are limited by their inability to provide a continuous estimate of blood pressure [1–2]. Continuous blood pressure estimate is critical for monitoring cardiovascular diseases such as hypertension and heart failure. Pulse transit time (PTT) is a time taken by a pulse wave to travel between a proximal and distal arterial site [3]. The speed at which pulse wave travels in the artery has been found to be proportional to blood pressure [1, 3]. A rise in blood pressure would cause blood vessels to increase in diameter resulting in a stiffer arterial wall and shorter PTT [1–3]. To avail such relationship with blood pressure, PTT has been extensively used as a marker of arterial elasticity and a non-invasive surrogate for arterial blood pressure estimation. Typically, a combination of electrocardiogram (ECG) and photoplethysmogram (PPG) or arterial blood pressure (ABP) signal is used for the purpose of blood pressure estimation [3], where the proximal and distal timing of PTT (also referred as pulse arrival time, PAT) is marked by R peak of ECG and a foot/peak of a PPG, respectively. In the literature, it has been shown that PAT derived using ECG-PPG combination infers an inaccurate estimate of blood pressure due to the inclusion of isovolumetric contraction period [1–3, 4]. Seismocardiogram (SCG) is a recording of chest acceleration due to heart movement, from which the opening and closing of the aortic valve can be obtained [5]. There is a distinct point on the dorso-ventral SCG signal that marks the opening of the aortic valve (annotated as AO). In the literature, AO has been proposed for timing the onset of the proximal pulse of the wave [6–8]. A combination of AO as a proximal pulse and PPG as a distal pulse has been used to derive pulse transit time and is shown to be correlated with blood pressure [7]. Ballistocardiogram (BCG) which is a measure of recoil forces of a human body in response to pumping of blood in blood vessels has also been explored as an alternative to ECG for timing proximal pulse [5, 9]. Use of SCG or BCG for timing the proximal point of a pulse can overcome the limitation of ECG-based PTT computation [6–7, 9]. However, a limitation of current blood pressure estimation systems is the requirement of two morphologically different signals, one for annotating the proximal (ECG, SCG, BCG) and other for annotating the distal (PPG, ABP) timing of a pulse wave. In the current research, we introduce a methodology to derive PTT from seismocardiograms alone. Two accelerometers were used for such purpose, one was placed on the xiphoid process of the sternum (marks proximal timing) and the other one was placed on the external carotid artery (marks distal timing). PTT was derived as a time taken by a pulse wave to travel between AO of both the xiphoidal and carotid SCG.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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