Nonlinear analyses of heart rate variability in hypertension

Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013-2023)

Artificial Intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify Hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities. This systematic review examines related work on automated HTN detection. We identify datasets, techniques, and classifiers used to develop AI models from clinical data, physiological signals, and fused data (a combination of both). Image-based models for assessing secondary HTN are also reviewed. The majority of the studies have primarily utilized single-modality approaches, such as biological signals (e.g., electrocardiography, photoplethysmography), and medical imaging (e.g., magnetic resonance angiography, ultrasound). Surprisingly, only a small portion of the studies (22 out of 122) utilized a multi-modal fusion approach combining data from different sources. Even fewer investigated integrating clinical data, physiological signals, and medical imaging to understand the intricate relationships between these factors. Future research directions are discussed that could build better healthcare systems for early HTN detection through more integrated modeling of multi-modal data sources.

Transcranial photobiomodulation therapy associated with cardiorespiratory rehabilitation in spastic subjects

The objective of this study was to evaluate the effects of cardiorespiratory rehabilitation (CR) and transcranial photobiomodulation (tPBM) on exercise tolerance (ET), heart rate variability (HRV), and peripheral muscle activity in individuals with spasticity. Fifteen participants with spasticity were randomly assigned to two groups: the tPBM group (tPBMG) consisted of eight volunteers who underwent tPBM (on mode) and CR, while the control group (CG) consisted of seven volunteers who underwent simulated tPBM (off mode) and CR. The CR program included 12 weeks of treatment, twice a week for one hour, involving aerobic exercises and lower limb strengthening. For tPBM, a cluster with three lasers (λ = 680 nm, 808 nm), with a power of 100 mW/laser and energy of 36 J, applied to the F7, F8, and Fpz points. The following parameters were evaluated after 8 and 12 weeks: ET, HRV, and surface electromyography (EMG) of the rectus femoris muscle during orthostasis (ORT), isometric squatting (ISOM), and isotonic squatting (ISOT). Both groups showed a 40% increase in ET for the CG and a 30% increase for the tPBMG. The CG had more pronounced parasympathetic modulation alterations during post-exercise effort and recovery compared to the tPBMG. The EMG results showed that the tPBMG exhibited progressive improvement in muscle activity during ISOM and ISOT, as well as a decrease in the interlimb difference. In conclusion, both CR and tPBMG demonstrated improvements in ET. However, tPBMG specifically showed promising effects on HRV modulation and peripheral muscle electrical activity, providing additional benefits compared to CR alone.

Heart rate variability is associated with cerebral small vessel disease in patients with diabetes

OBJECTIVE

Low heart rate variability (HRV), an indicator of autonomic nervous system dysfunction, has been associated with increased all-cause and cardiovascular mortality and incident stroke. However, the relationship between HRV and cerebral small vessel disease (CSVD) showed contradictory results. We aimed to examine the relationship of HRV and total burden of CSVD and each of the magnetic resonance imaging (MRI) markers of CSVD.

METHODS

We recruited 435 patients who attended our hospital for physical examination between June 2020 and August 2021. All underwent 24-h Holter monitoring and MRI scan. The standard deviation of normal-to-normal intervals (SDNN) was selected as the method for HRV assessment. The presence of severe white matter hyperintensity, lacunes, and >10 enlarged basal ganglia perivascular spaces, and cerebral microbleeds were added for estimating the CSVD score (0-4). Multivariate logistic analyses was performed to assess whether HRV was independently associated with the burden of CSVD and each of the MRI markers of CSVD, with and without stratification by prevalent diabetes.

RESULTS

This study included 435 subjects with a mean age of 64.0 (57.0-70.0) years; 49.4% of the patients were male, and 122 (28.0%) had a history of diabetes. In multivariate analyses, lower SDNN was independently associated with total burden of CSVD and the presence of enlarged perivascular spaces in all subjects. According to diabetes stratification, lower SDNN was independently associated with total burden of CSVD and each MRI markers of CSVD separately only in the diabetic group.

CONCLUSIONS

Lower HRV was associated with total burden of CSVD and each MRI markers of CSVD separately among diabetic patients, but not among non-diabetic patients.

A Comparative Study of Baseline Heart Rate Variability, Sleep Quality, and Oxidative Stress Levels in Hypertensive Versus Normotensive Subjects: A Cross-Sectional Study

Objectives: To understand sleep quality, oxidative stress levels, and heart rate variability (HRV) in subjects with hypertension. This study aims to create baseline data in hypertensive subjects to research the possibility of further estimating the risk of developing cardiovascular mortality and morbidity in a patient with hypertension.

Design and methods: This analytical cross-sectional study, encompassing 128 study subjects of both genders, with 64 hypertensive subjects, analyse the co-relation of sleep quality, malondialdehyde, and heart rate variability in hypertensive and normotensive subjects. The study was done in a tertiary teaching institute in northern India for 14 months. Descriptive statistics were used, and the independent t-test, Mann-Whitney U test, and Chi-square were used to find the association among the variables. Linear regression was used to estimate the effect of blood pressure on malondialdehyde levels.

Results: Subjects with hypertension were found to have poor sleep quality (Global PSQI score ≥5, p=0.0001) and an increased malondialdehyde level (0.30303±0.17193 µM/L, p=0.0001). The hypertensive subjects were found to have lower parasympathetic activity as indicated by low high frequency (2.79463±473.220280; p=0.0001) and increased sympathetic activity; low frequency/high frequency (2.29823±2.792441; p=0.0001). Multivariate linear regression predicts that with one unit increase in systolic blood pressure, the malondialdehyde level increases by 0.006 units (p=0.002; 95% CI).

Conclusion: Among the hypertensive group, there is significantly increased oxidative stress level, poor quality of sleep, and increased sympathetic activity, thereby predisposing the subjects to increased risk of cardiac morbidity and mortality.

Effects of Long-Term Regular Continuous and Intermittent Walking on Oxidative Stress, Metabolic Profile, Heart Rate Variability, and Blood Pressure in Older Adults with Hypertension

It is documented that regular exercise is beneficial for improving the antioxidant system, metabolic system, cardiac autonomic function, and blood pressure in those with hypertension. In this regard, low-intensity exercise is recommended for older adults, particularly those with chronic diseases. This study aimed to compare the effects of long-term regular continuous walking with intermittent walking on oxidative stress, metabolic profile, heart rate variability, and blood pressure in older adults with hypertension. Forty-three participants with hypertension aged 60–80 years were randomly divided into the continuous or intermittent walking (CON or INT) groups. Participants in the CON group walked for 30 min, 3 days/week for 12 weeks. Participants in the INT group split 30 min walking into 3 identical sessions punctuated by a 1 min rest after each session, 3 days/week for 12 weeks. Antioxidant and oxidative stress markers, metabolic markers, heart rate variability, and blood pressure were evaluated before and after the exercise program. Glutathione (GSH), GSH to GSH disulfide (GSSG) ratio, and total GSH increased significantly, and GSSG and malondialdehyde decreased significantly in both groups ( $p<0.05$ ) without significant differences between groups. Triglycerides, ratio of total cholesterol to high-density lipoprotein cholesterol, and atherosclerogenic index were significantly lower in the CON group than those in the INT group ( $p<0.05$ ). The standard deviation of the NN intervals and root mean square of the successive differences were significantly higher, and low-frequency power was significantly lower in the INT group than that in the CON group ( $p<0.05$ ). No significant changes in blood pressure were noted in both groups, and nor were there any significant differences between groups. Long-term regular continuous and intermittent walking may comparably increase antioxidants, reduce oxidative stress, and be beneficial for improving important blood pressure-related outcomes, including metabolic profile or cardiac autonomic function in older adults with hypertension.

Automated Detection of Hypertension Using Physiological Signals: A Review

Arterial hypertension (HT) is a chronic condition of elevated blood pressure (BP), which may cause increased incidence of cardiovascular disease, stroke, kidney failure and mortality. If the HT is diagnosed early, effective treatment can control the BP and avert adverse outcomes. Physiological signals like electrocardiography (ECG), photoplethysmography (PPG), heart rate variability (HRV), and ballistocardiography (BCG) can be used to monitor health status but are not directly correlated with BP measurements. The manual detection of HT using these physiological signals is time consuming and prone to human errors. Hence, many computer-aided diagnosis systems have been developed. This paper is a systematic review of studies conducted on the automated detection of HT using ECG, HRV, PPG and BCG signals. In this review, we have identified 23 studies out of 250 screened papers, which fulfilled our eligibility criteria. Details of the study methods, physiological signal studied, database used, various nonlinear techniques employed, feature extraction, and diagnostic performance parameters are discussed. The machine learning and deep learning based methods based on ECG and HRV signals have yielded the best performance and can be used for the development of computer-aided diagnosis of HT. This work provides insights that may be useful for the development of wearable for continuous cuffless remote monitoring of BP based on ECG and HRV signals.

Evaluation of heart rate variability using 24-hour Holter electrocardiography in hypertensive patients

BACKGROUND

Hypertension (HTN) remains a serious risk factor for cardiovascular mortality across the world. Hypertensive state has been shown to be associated with autonomic nervous function. This study aimed to explore the association between autonomic nervous impairment assessed by heart rate variability (HRV) and HTN.

METHODS

A total of 52 hypertensive and 55 non-hypertensive patients were consecutively studied using 24-hour Holter. The hypertensive patients were grouped into controlled blood pressure (BP) and uncontrolled BP. This study compared HRV in non-hypertensive and hypertensive patients; and hypertensive patients with controlled and uncontrolled BP. HRV parameters include time and frequency domain.

RESULTS

Mean age for hypertensive and non-hypertensive patients were 53.58 ± 14.31 and 44.89 ± 16.63 years old, respectively. Median (IQR) SDNN for hypertensive and non-hypertensive group were 109.00 (90.00-145.00) and 129.00 (107.00-169.00), respectively. SDNN, ASDNN, rMSSD, pNN50, BB50, VLF, and HF values were significantly lower in the hypertensive group compared to non-hypertensive group (all P < .05). A multiple regression analysis showed that HRV parameters: SDANN, ASDNN, rMSSD, and LF values were independent risk factors of HTN. SDNN, SDANN, ASDNN, VLF, LF, and HF values were significantly lower in the uncontrolled BP compared to controlled BP group (all P < .05). A multiple regression analysis showed that HRV parameters: SDNN, SDANN, rMSSD, and HF values were independent risk factors of uncontrolled BP in hypertensive patients.

CONCLUSIONS

Our study showed that cardiac autonomic nervous impairment, as demonstrated by reduced HRV, is significantly associated with HTN. Decreased HRV was more evident in uncontrolled BP than in controlled BP group.

Assessment of Hypertension Using Clinical Electrocardiogram Features: A First-Ever Review

Hypertension affects an estimated 1.4 billion people and is a major cause of morbidity and mortality worldwide. Early diagnosis and intervention can potentially decrease cardiovascular events later in life. However, blood pressure (BP) measurements take time and require training for health care professionals. The measurements are also inconvenient for patients to access, numerous daily variables affect BP values, and only a few BP readings can be collected per session. This leads to an unmet need for an accurate, 24-h continuous, and portable BP measurement system. Electrocardiograms (ECGs) have been considered as an alternative way to measure BP and may meet this need. This review summarizes the literature published from January 1, 2010, to January 1, 2020, on the use of only ECG wave morphology to monitor BP or identify hypertension. From 35 articles analyzed (9 of those with no listed comorbidities and confounders), the P wave, QTc intervals and TpTe intervals may be promising for this purpose. Unfortunately, with the limited number of articles and the variety of participant populations, we are unable to make conclusions about the effectiveness of ECG-only BP monitoring. We provide 13 recommendations for future ECG-only BP monitoring studies and highlight the limited findings in pregnant and pediatric populations. With the advent of convenient and portable ECG signal recording in smart devices and wearables such as watches, understanding how to apply ECG-only findings to identify hypertension early is crucial to improving health outcomes worldwide.

A computational intelligence tool for the detection of hypertension using empirical mode decomposition

Hypertension (HPT), also known as high blood pressure, is a precursor to heart, brain or kidney diseases. Some symptoms of HPT include headaches, dizziness and fainting. The potential diagnosis of masked hypertension is of specific interest in this study. In masked hypertension (MHPT), the instantaneous blood pressure appears normal, but the 24-h ambulatory blood pressure is abnormal. Hence patients with MHPT are difficult to identify and thus remain untreated or are treated insufficiently. Hence, a computational intelligence tool (CIT) using electrocardiograms (ECG) signals for HPT and possible MHPT detection is proposed in this work. Empirical mode decomposition (EMD) is employed to decompose the pre-processed signals up to five levels. Nonlinear features are extracted from the five intrinsic mode functions (IMFs) thereafter. Student's t-test is subsequently applied to select a set of highly discriminatory features. This feature set is then input to various classifiers, in which, the best accuracy of 97.70% is yielded by the k-nearest neighbor (k-NN) classifier. The developed tool is evaluated by the 10-fold cross validation technique. Our findings suggest that the developed system is useful for diagnostic computational intelligence tool in hospital settings, and that it enables the automatic classification of HPT versus normal ECG signals.

The Association of Autonomic Nervous System Function With Ischemic Stroke, and Treatment Strategies

Acute ischemic stroke, especially minor stroke, and transient ischemic attack have high risks of recurrence and exacerbation into severe ischemic strokes. It remains challenging to perform risk stratification and screen high-risk groups for initiation of early treatment in these patients. Moreover, with the growing population of patients with chronic small vessel disease, the mechanisms and clinical implications require further investigation. Traditional tools such as the ABCD2 score (age, blood pressure, clinical features, duration of symptoms, diabetes) have only moderate predictive value in patients with transient ischemic attack or minor stroke. By contrast, measurement of changes in heart rate variability (HRV) is an important and novel tool for risk stratification and outcome prediction in patients with cardiovascular diseases, as it reflects the overall level of autonomic nervous system dysfunction. Thus, abnormal HRV may be useful for prognosis and improve stratification of stroke patients with diverse risks. HRV may also partially explain autonomic nervous dysfunction and other manifestations during the process of chronic cerebral small vessel disease. In summary, measurement of HRV may contribute to early initiation of interventions in acute or chronic stroke patients using novel treatments involving rebalancing of autonomic nervous system function.