Non-Invasive Detection of Mechanical Alternans Utilizing Photoplethysmography

Blood pressure fragmentation as a new measure of blood pressure variability: association with predictors of cardiac surgery outcomes

Background: Fluctuations in beat-to-beat blood pressure variability (BPV) encode untapped information of clinical utility. A need exists for developing new methods to quantify the dynamical properties of these fluctuations beyond their mean and variance. Objectives: Introduction of a new beat-to-beat BPV measure, termed blood pressure fragmentation (BPF), and testing of whether increased preoperative BPF is associated with (i) older age; (ii) higher cardiac surgical risk, assessed using the Society of Thoracic Surgeons' (STS) Risk of Morbidity and Mortality index and the European System for Cardiac Operative Risk Evaluation Score (EuroSCORE II); and (iii) longer ICU length of stay (LOS) following cardiac surgery. The secondary objective was to use standard BPV measures, specifically, mean, SD, coefficient of variation (CV), average real variability (ARV), as well a short-term scaling index, the detrended fluctuation analysis (DFA) ⍺1 exponent, in the same type of analyses to compare the results with those obtained using BPF. Methods: Consecutive sample of 497 adult patients (72% male; age, median [inter-quartile range]: 67 [59-75] years) undergoing cardiac surgery with cardiopulmonary bypass. Fragmentation, standard BPV and DFA ⍺1 measures were derived from preoperative systolic blood pressure (SBP) time series obtained from radial artery recordings. Results: Increased preoperative systolic BPF was associated with older age, higher STS Risk of Morbidity and Mortality and EuroSCORE II values, and longer ICU LOS in all models. Specifically, a one-SD increase in systolic BPF (9%) was associated with a 26% (13%-40%) higher likelihood of longer ICU LOS (>2 days). Among the other measures, only ARV and DFA ⍺1 tended to be associated with longer ICU LOS. However, the associations did not reach significance in the most adjusted models. Conclusion: Preoperative BPF was significantly associated with preoperative predictors of cardiac surgical outcomes as well as with ICU LOS. Our findings encourage future studies of preoperative BPF for assessment of health status and risk stratification of surgical and non-surgical patients.

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.

A review of wearable and unobtrusive sensing technologies for chronic disease management

With the rapidly increasing number of patients with chronic disease, numerous recent studies have put great efforts into achieving long-term health monitoring and patient management. Specifically, chronic diseases including cardiovascular disease, chronic respiratory disease and brain disease can threaten patients' health conditions over a long period of time, thus effecting their daily lives. Vital health parameters, such as heart rate, respiratory rate, SpO2 and blood pressure, are closely associated with patients’ conditions. Wearable devices and unobtrusive sensing technologies can detect such parameters in a convenient way and provide timely predictions on health condition deterioration by tracking these biomedical signals and health parameters. In this paper, we review current advancements in wearable devices and unobtrusive sensing technologies that can provides possible tools and technological supports for chronic disease management. Current challenges and future directions of related techniques are addressed accordingly.

Pulse Arrival Time and Pulse Interval as Accurate Markers to Detect Mechanical Alternans

Mechanical alternans (MA) is a powerful predictor of adverse prognosis in patients with heart failure and cardiomyopathy, but its use remains limited due to the need of invasive continuous arterial pressure recordings. This study aims to assess novel cardiovascular correlates of MA in the intact human heart to facilitate affordable and non-invasive detection of MA and advance our understanding of the underlying pathophysiology. Arterial pressure, respiration, and ECG were recorded in 12 subjects with healthy ventricles during voluntarily controlled breathing at different respiratory rate, before and after administration of beta-blockers. MA was induced by ventricular pacing. A total of 67 recordings lasting approximately 90 s each were analyzed. Mechanical alternans (MA) was measured in the systolic blood pressure. We studied cardiovascular correlates of MA, including maximum pressure rise during systole (dPdtmax), pulse arrival time (PAT), pulse wave interval (PI), RR interval (RRI), ECG QRS complexes and T-waves. MA was detected in 30% of the analyzed recordings. Beta-blockade significantly reduced MA prevalence (from 50 to 11%, p < 0.05). Binary classification showed that MA was detected by alternans in dPdtmax (100% sens, 96% spec), PAT (100% sens, 81% spec) and PI (80% sens, 81% spec). Alternans in PAT and in PI also showed high degree of temporal synchronization with MA (80 ± 33 and 73 ± 40%, respectively). These data suggest that cardiac contractility is a primary factor in the establishment of MA. Our findings show that MA was highly correlated with invasive measurements of PAT and PI. Since PAT and PI can be estimated using non-invasive technologies, these markers could potentially enable affordable MA detection for risk-prediction.