Cardiovascular Alterations and Management of Patients With White Coat Hypertension: A Meta-Analysis

Taslim Uddin Raju S, Hossain MI, Dipto SA, M. Tanvir Uddin S, Sijan A, Chowdhury MAS, Ahmad A, Mahamudul Hasan Khan M. A continuous cuffless blood pressure measurement from optimal PPG characteristic features using machine learning algorithms

Background and objective

Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement.

Methods

In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately.

Results

The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP.

Conclusion

The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

Blood Pressure in Patients with Arterial Hypertension and Overweight or Obesity During Office Measurement and Daily Monitoring

Arterial hypertension (AH) is one of the most common chronic diseases in the world. More than 1.5 billion people suffer from hypertension. According to official statistics, in Ukraine in 2021 more than 13 million patients with AH were registered, and annually this disease is detected in 1 million Ukrainians. The objective: to determine the levels of blood pressure (BP) in overweighted or obese patients with AH who use antihypertensive drugs during office measurement and 24-hours monitoring of BP. Materials and methods. The study analyzed the results of treatment of 395 overweighted or obese patients (139 women and 236 men) with essential AH. All patients received antihypertensive treatment for at least 3 months before study start. Patients were divided into five groups depending on blood pressure. The control group included 33 age-matched individuals. During the study BP was measured in physician’s office with use of mechanical tonometer with a shoulder cuff and ambulatory 24-hours blood pressure monitoring (ABPM) was performed for all patients. Results. The results of office blood pressure measurement and ABPM were analyzed. Normal BP was detected in 34.7% of patients with AH and high BMI. In 10.9% of patients systolic blood pressure (SBP) remains above 139 mm Hg despite of drug use, in 16.3% of patients the grade 1 AH was diagnosed, in 23.2% – grade 2, and in 14.9% – grade 3 AH was deter mined during office blood pressure measurement. It was found that men are 1.7 times more likely had uncontrolled AH comparing to women. The further analyze of the BP data showed that office BP levels were higher then home. Target blood pressure levels were found in 87% of patients during ABPM, although in the office measurement this figure was 54%, 5% of patients could not withstand the BP monitoring during the day with an automatic device due to pain. No difference in heart rate was determined in study patients by different types of measurement. Conclusions. In patients with AH and high body mass index, office measurement reveals insufficient reduction of blood pressure (BP) in 35% of patients despite of antihypertensive drugs use. At the same time ABPM found target BP achievement in 87% of patients. Thus, ABPM should be performed in overweighted or obese patients with AH before making the decision to increase the antihypertensive drug dose to avoid possible undesirable BP decrease.

Cuff-less blood pressure measurement based on hybrid feature selection algorithm and multi-penalty regularized regression technique

One of the prominent reasons behind the deterioration of cardiovascular conditions is hypertension. Due to lack of specific symptoms, sometimes existing hypertension goes unnoticed until significant damage happens to the heart or any other body organ. Monitoring of BP at a higher frequency is necessary so that we can take early preventive measures to control and keep it within the normal range. The cuff-based method of measuring BP is inconvenient for frequent daily measurements. The cuffless BP measurement method proposed in this paper uses features extracted from the electrocardiogram (ECG) and photoplethysmography (PPG). ECG and PPG both have distinct characteristics, which change with the change of blood pressure levels. Feature extraction and hybrid feature selection algorithms are followed by a generalized penalty-based regression technique led to a new BP measurement process that uses the minimum number of features. The performance of the proposed technique to measure blood pressure was compared to an approach using an ordinary linear regression method with no feature selection and to other contemporary techniques. MIMIC-II database was used to train and test our proposed method. The root mean square error (RMSE) for systolic blood pressure (SBP) improved from 11.2 mmHg to 5.6 mmHg when the proposed technique was implemented and for diastolic blood pressure (DBP) improved from 12.7 mmHg to 6.69 mmHg. The mean absolute error (MAE) was found to be 4.91 mmHg for SBP and 5.77 mmHg for DBP, which have shown improvement over other existing cuffless techniques where the substantial number of patients, as well as feature selection algorithm, were implemented. In addition, according to the British Hypertension Society standard (BHS) standard for cuff-based BP measurement, the criteria for acceptable measurement are to achieve at least grade B; our proposed method also satisfies this criterion.