Photoplethysmography and Heart Rate Variability for the Diagnosis of Preeclampsia

Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting

Prehospital medical care is a major challenge for both civilian and military situations as resources are limited, yet critical triage and treatment decisions must be rapidly made. Prehospital medicine is further complicated during mass casualty situations or remote applications that require more extensive medical treatments to be monitored. It is anticipated on the future battlefield where air superiority will be contested that prolonged field care will extend to as much 72 h in a prehospital environment. Traditional medical monitoring is not practical in these situations and, as such, wearable sensor technology may help support prehospital medicine. However, sensors alone are not sufficient in the prehospital setting where limited personnel without specialized medical training must make critical decisions based on physiological signals. Machine learning-based clinical decision support systems can instead be utilized to interpret these signals for diagnosing injuries, making triage decisions, or driving treatments. Here, we summarize the challenges of the prehospital medical setting and review wearable sensor technology suitability for this environment, including their use with medical decision support triage or treatment guidance options. Further, we discuss recommendations for wearable healthcare device development and medical decision support technology to better support the prehospital medical setting. With further design improvement and integration with decision support tools, wearable healthcare devices have the potential to simplify and improve medical care in the challenging prehospital environment.

Finger pulse plethysmography predicts gestational hypertension, preeclampsia and gestational diabetes

BACKGROUND

Pregnancy complications related to hypertension can affect both mother and newborn. Pulse wave attenuation (PWA) captured through fingertip photoplethysmography (PPG) provide valuable insights into maternal acute hemodynamic and autonomic vascular function. Here, we quantify the nocturnal dynamics of PWA during early pregnancy and assess their association with the development of gestational hypertension, preeclampsia and gestational diabetes.

METHODS

PWA dynamics were assessed on overnight polysomnography-derived PPG signals from a cohort of 2714 pregnant women (mean age: 26.8 ± 5.5 years) enrolled in the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). We determined the average duration (PWA duration ) and depth (PWA depth ) of PWA events in all women.

RESULTS

Odds ratio (OR) analysis-adjusted common confounders indicates that an average PWA duration greater than 8.74 s was associated with the increased risk of gestational hypertension [OR = 1.75 (1.27-2.39), P  < 0.001]. Similarly, average PWA depth greater than 1.19 was associated with an increased risk of preeclampsia [OR = 1.53 (1.01-2.33), P  = 0.045] and gestational diabetes [OR = 1.66 (1.01-2.73), P  = 0.044].

CONCLUSION

PWA attenuation dynamics during early pregnancy predict the risk of developing gestational hypertension and diabetes condition for women in their later trimesters. Potentially obtainable from smart wearable consumer devices, PWA analysis offers a low-cost, accessible and scalable marker that can enhance the management of pregnancy-induced cardiometabolic issues.

The impact of healthy pregnancy on features of heart rate variability and pulse wave morphology derived from wrist-worn photoplethysmography

Due to the association between dysfunctional maternal autonomic regulation and pregnancy complications, tracking non-invasive features of autonomic regulation derived from wrist-worn photoplethysmography (PPG) measurements may allow for the early detection of deteriorations in maternal health. However, even though a plethora of these features-specifically, features describing heart rate variability (HRV) and the morphology of the PPG waveform (morphological features)-exist in the literature, it is unclear which of these may be valuable for tracking maternal health. As an initial step towards clarity, we compute comprehensive sets of HRV and morphological features from nighttime PPG measurements. From these, using logistic regression and stepwise forward feature elimination, we identify the features that best differentiate healthy pregnant women from non-pregnant women, since these likely capture physiological adaptations necessary for sustaining healthy pregnancy. Overall, morphological features were more valuable for discriminating between pregnant and non-pregnant women than HRV features (area under the receiver operating characteristics curve of 0.825 and 0.74, respectively), with the systolic pulse wave deterioration being the most valuable single feature, followed by mean heart rate (HR). Additionally, we stratified the analysis by sleep stages and found that using features calculated only from periods of deep sleep enhanced the differences between the two groups. In conclusion, we postulate that in addition to HRV features, morphological features may also be useful in tracking maternal health and suggest specific features to be included in future research concerning maternal health.

Application of photoplethysmography signals for healthcare systems: An in-depth review

BACKGROUND AND OBJECTIVES

Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

METHODS

We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

RESULTS

Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

CONCLUSIONS

We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.

Serum Levels of NT-Pro BNP in Patients with Preeclampsia

Objective

This study aims to determine the serum levels of NT-proBNP in women with preeclampsia with and without severe signs and to evaluate the cardiovascular risks in these two groups of participants.

Methods

A descriptive cross-sectional study was conducted on 52 women with preeclampsia in the Department of Gynecology and Obstetrics – Hue Central Hospital, from August 2019 to September 2020.

Results

In preeclampsia women, the rate of hypertension in stage 3, stage 2, and stage 1 were 46.1%, 32.7%, and 21.2%, respectively. The average Sokolow-Lyon index in the preeclampsia group with and without severe signs was 22.25 ± 7.38mm, 20.16 ± 5.54mm, respectively. The average left ventricular mass index in the group of preeclampsia patients without and with severe signs was 92.27 ± 14.56g/m² and 120.68 ± 16.47g/m², respectively. The average ejection fraction in the group of preeclampsia patients without severe signs and with severe signs was 65.11 ± 3.45%, 56.21 ± 7.12%, correspondingly. In contrast, the difference between the two groups was statistically significant with p < 0.05. The plasma NT-proBNP level in the preeclampsia group without severe signs was 349.12 ± 93.51pg/mL, whereas the concentration in the preeclampsia group with severe signs was 725.32 ± 290.46pg/mL (p < 0.05).

Conclusion

The NT-proBNP level was statistically significantly increased in the patients with preeclampsia. Analyzing and comparing the figures and changes found in two groups of PE patients, with and without severe signs, we suggest that women diagnosed with PE with severe signs have a higher risk of developing cardiovascular problems forthwith and henceforth.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

Background

Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings.

Objective

This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor.

Methods

We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices.

Results

Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters.

Conclusions

The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

Heart rate variability in hypertensive pregnancy disorders: A systematic review

BACKGROUND

Hypertensive pregnancy disorders (HPD) are associated with dysfunction of the autonomic nervous system. Cardiac autonomic functions can be assessed by heart rate variability (HRV) measurements.

OBJECTIVE

To study whether HRV detects differences in the function of the autonomic nervous system between pregnant women with HPD compared to normotensive pregnant women and between women with a history of a pregnancy complicated by HPD compared to women with a history of an uncomplicated pregnancy.

METHODS

A systematic search was performed in Medline, EMBASE, and CENTRAL to identify studies comparing HRV between pregnant women with HPD or women with a history of HPD to women with (a history of) normotensive pregnancies.

RESULTS

The search identified 523 articles of which 24 were included in this review, including 850 women with (a history of) HPD and 1205 normotensive controls. The included studies showed a large heterogenicity. A decrease in overall HRV was found in preeclampsia (PE), compared to normotensive pregnant controls. A trend is seen towards increased low frequency/high frequency-ratio in women with PE compared to normotensive pregnant controls.

CONCLUSION

Our systematic review supports the hypothesis a sympathetic overdrive is found in HPD which is associated with a parasympathetic withdrawal. However, the included studies in our review showed a large diversity in the methods applied and their results.

Can Photoplethysmography Replace Arterial Blood Pressure in the Assessment of Blood Pressure?

Arterial Blood Pressure (ABP) and photoplethysmography (PPG) are both useful techniques to monitor cardiovascular status. Though ABP monitoring is more widely employed, this procedure of signal acquisition whether done invasively or non-invasively may cause inconvenience and discomfort to the patients. PPG, however, is simple, noninvasive, and can be used for continuous measurement. This paper focuses on analyzing the similarities in time and frequency domains between ABP and PPG signals for normotensive, prehypertensive and hypertensive subjects and the feasibility of the classification of subjects considering the results of the analysis performed. From a database with 120 records of ABP and PPG, each 120 s in length, the records where separated into epochs taking into account 10 heartbeats, and the following statistical measures were performed: Correlation (r), Coherence (COH), Partial Coherence (pCOH), Partial Directed Coherence (PDC), Directed Transfer Function (DTF), Full Frequency Directed Transfer Function (ffDTF) and Direct Directed Transfer Function (dDTF). The correlation coefficient was r > 0.9 on average for all groups, indicating a strong morphology similarity. For COH and pCOH, coherence (linear correlation in frequency domain) was found with significance (p < 0.01) in differentiating between normotensive and hypertensive subjects using PPG signals. For the dataset at hand, only two synchrony measures are able to convincingly distinguish hypertensive subjects from normotensive control subjects, i.e., ffDTF and dDTF. From PDC, DTF, ffDTF, and dDTF, a consistent, a strong significant causality from ABP→PPG was found. When all synchrony measures were combined, an 87.5 % accuracy was achieved to detect hypertension using a Neural Network classifier, suggesting that PPG holds most informative features that exist in ABP.