Elimination of Multicollinearity in Continuous Non-Invasive Blood Pressure Approximation by Information Criterion

Continuous and unobtrusive method of measuring blood pressure has been gaining more attention in the healthcare community. With the application of data analysis techniques in biosignals like Electrocardiogram (ECG) and Photoplethysmogram (PPG), several predictors are obtained that correlates well with the blood pressure. But the BP approximation regression models formed using these predictors suffers from multicollinearity (higher correlation between predictors). The article proposes the use of information criterion-based model ensemble approach to reduce the effect of multicollinearity in the continuous BP estimation. The study focuses on forming pool of candidate models from feature subsets. The best performing models are selected based on information criterion and combined to form the ensemble model. Experiments with performed with MIMIC-II dataset that consists of 104 records with simultaneously recorded PPG and arterial BP. The results suggest that the technique achieves Mean Absolute Error (MAE) of 5.81 mm Hg and 3.35 mm Hg for systolic and diastolic BP and Root Mean Square Error (RMSE) of 6.08 mm Hg and 4.12 mm Hg for systolic and diastolic BP respectively. The error measures conform to the standards set by American Association of Medical Instrumentation (AAMI). The method reveals that the ensemble model based on information criterion outperforms well compared to the usage of single model.

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

npj Digital Medicine ◽

10.1038/s41746-021-00447-y ◽

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

Optical and Quantum Electronics ◽

10.1007/s11082-020-02667-0 ◽

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.

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Does non‐invasive blood pressure measurement increase the complications and decrease indwelling time of peripheral intravenous catheters: A cohort study

Journal of Clinical Nursing ◽

10.1111/jocn.15751 ◽

2021 ◽

Author(s):

Qiling Tan ◽

Shangping Zhao ◽

Xiaoqin Li ◽

Bo Gu ◽

Zhao Ni

Keyword(s):

Blood Pressure ◽

Cohort Study ◽

Pressure Measurement ◽

Blood Pressure Measurement ◽

Invasive Blood Pressure ◽

Non Invasive ◽

Indwelling Time ◽

Intravenous Catheters ◽

Invasive Blood Pressure Measurement

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An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽

10.3390/s21051867 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1867

Author(s):

Tasbiraha Athaya ◽

Sunwoong Choi

Keyword(s):

Blood Pressure ◽

Cardiovascular Health ◽

Estimation Method ◽

Absolute Error ◽

British Hypertension Society ◽

Non Invasive ◽

Arterial Blood ◽

Blood Pressure Waveform ◽

The Mean ◽

Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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