A Pulse Wave Velocity Based Method to Assess the Mean Arterial Blood Pressure Limits of Autoregulation in Peripheral Arteries

Minimum detectable change estimates of heart-to-finger arterial pulse wave conduction time in cardiovascular-healthy young adults

BACKGROUND

Pulse wave velocity (PWV) measurements are the gold standard for assessing arterial stiffness and estimating time or treatment-related changes in cardiovascular status. What constitutes a statistically significant change is an important clinical consideration. This study aimed to describe the variability of heart-to-finger pulse wave conduction time (PWCT) to provide estimates of the minimum detectable change (MDC) dependent on the number of PWCT samples used.

MATERIALS AND METHODS

Heart-to-finger PWCT was measured based on the time delay between the peak of the EKG R-wave and arterial pulse arrival at the left hand index finger as measured by a photoplethysmographic sensor. Measurements were done in 10 young adults (25.7 ± 1.2 years) while supine for 45 min. Depending on the subject's heart rate, these measurements yielded 2430 to 3750 contiguous PWCT for analysis. The variability in these PWCTs was used to determine the minimal detectable percentage change for specified p-values of 0.05, 0.01, and 0.001.

RESULTS

Sample sizes of 10, 30, 50, or 300 contiguous PWCTs yield similar MDC estimates for a given targeted p-value. The MDC% depended on the chosen p-value, with values of MDC% for p-values of 0.05, 0.01, and 0.001 being 7.8%, 10.5%, and 13.6%.

CONCLUSIONS

The estimates may help plan experiments when changes or differences in PWCT or PWV are of interest. Also, these MDC estimates may help assess the validity of clinical study outcomes if PWV changes are outcome measures. The main limitation of the estimates is that they are based on 10 healthy subjects.

A method for blood pressure hydrostatic pressure correction using wearable inertial sensors and deep learning

Cuffless noninvasive blood pressure (BP) measurement could enable early unobtrusive detection of abnormal BP patterns, but when the sensor is placed on a location away from heart level (such as the arm), its accuracy is compromised by variations in the position of the sensor relative to heart level; such positional variations produce hydrostatic pressure changes that can cause swings in tens of mmHg in the measured BP if uncorrected. A standard method to correct for changes in hydrostatic pressure makes use of a bulky fluid-filled tube connecting heart level to the sensor. Here, we present an alternative method to correct for variations in hydrostatic pressure using unobtrusive wearable inertial sensors. This method, called IMU-Track, analyzes motion information with a deep learning model; for sensors placed on the arm, IMU-Track calculates parameterized arm-pose coordinates, which are then used to correct the measured BP. We demonstrated IMU-Track for BP measurements derived from pulse transit time, acquired using electrocardiography and finger photoplethysmography, with validation data collected across 20 participants. Across these participants, for the hand heights of 25 cm below or above the heart, mean absolute errors were reduced for systolic BP from 13.5 ± 1.1 and 9.6 ± 1.1 to 5.9 ± 0.7 and 5.9 ± 0.5 mmHg, respectively, and were reduced for diastolic BP from 15.0 ± 1.0 and 11.5 ± 1.5 to 6.8 ± 0.5 and 7.8 ± 0.8, respectively. On a commercial smartphone, the arm-tracking inference time was ~134 ms, sufficiently fast for real-time hydrostatic pressure correction. This method for correcting hydrostatic pressure may enable accurate passive cuffless BP monitors placed at positions away from heart level that accommodate everyday movements.

It is All in the Wrist: Wearable Sleep Staging in a Clinical Population versus Reference Polysomnography

PURPOSE

There is great interest in unobtrusive long-term sleep measurements using wearable devices based on reflective photoplethysmography (PPG). Unfortunately, consumer devices are not validated in patient populations and therefore not suitable for clinical use. Several sleep staging algorithms have been developed and validated based on ECG-signals. However, translation from these techniques to data derived by wearable PPG is not trivial, and requires the differences between sensing modalities to be integrated in the algorithm, or having the model trained directly with data obtained with the target sensor. Either way, validation of PPG-based sleep staging algorithms requires a large dataset containing both gold standard measurements and PPG-sensor in the applicable clinical population. Here, we take these important steps towards unobtrusive, long-term sleep monitoring.

METHODS

We developed and trained an algorithm based on wrist-worn PPG and accelerometry. The method was validated against reference polysomnography in an independent clinical population comprising 244 adults and 48 children (age: 3 to 82 years) with a wide variety of sleep disorders.

RESULTS

The classifier achieved substantial agreement on four-class sleep staging with an average Cohen's kappa of 0.62 and accuracy of 76.4%. For children/adolescents, it achieved even higher agreement with an average kappa of 0.66 and accuracy of 77.9%. Performance was significantly higher in non-REM parasomnias (kappa = 0.69, accuracy = 80.1%) and significantly lower in REM parasomnias (kappa = 0.55, accuracy = 72.3%). A weak correlation was found between age and kappa (ρ = -0.30, p<0.001) and age and accuracy (ρ = -0.22, p<0.001).

CONCLUSION

This study shows the feasibility of automatic wearable sleep staging in patients with a broad variety of sleep disorders and a wide age range. Results demonstrate the potential for ambulatory long-term monitoring of clinical populations, which may improve diagnosis, estimation of severity and follow up in both sleep medicine and research.

Direct application of an ECG-based sleep staging algorithm on reflective photoplethysmography data decreases performance

OBJECTIVE

The maturation of neural network-based techniques in combination with the availability of large sleep datasets has increased the interest in alternative methods of sleep monitoring. For unobtrusive sleep staging, the most promising algorithms are based on heart rate variability computed from inter-beat intervals (IBIs) derived from ECG-data. The practical application of these algorithms is even more promising when alternative ways of obtaining IBIs, such as wrist-worn photoplethysmography (PPG) can be used. However, studies validating sleep staging algorithms directly on PPG-based data are limited.

RESULTS

We applied an automatic sleep staging algorithm trained and validated on ECG-data directly on inter-beat intervals derived from a wrist-worn PPG sensor, in 389 polysomnographic recordings of patients with a variety of sleep disorders. While the algorithm reached moderate agreement with gold standard polysomnography, the performance was significantly lower when applied on PPG- versus ECG-derived heart rate variability data (kappa 0.56 versus 0.60, p < 0.001; accuracy 73.0% versus 75.9% p < 0.001). These results show that direct application of an algorithm on a different source of data may negatively affect performance. Algorithms need to be validated using each data source and re-training should be considered whenever possible.

Cuffless Blood Pressure Monitoring: Promises and Challenges

Current BP measurements are on the basis of traditional BP cuff approaches. Ambulatory BP monitoring, at 15- to 30-minute intervals usually over 24 hours, provides sufficiently continuous readings that are superior to the office-based snapshot, but this system is not suitable for frequent repeated use. A true continuous BP measurement that could collect BP passively and frequently would require a cuffless method that could be worn by the patient, with the data stored electronically much the same way that heart rate and heart rhythm are already done routinely. Ideally, BP should be measured continuously and frequently during diverse activities during both daytime and nighttime in the same subject by means of novel devices. There is increasing excitement for newer methods to measure BP on the basis of sensors and algorithm development. As new devices are refined and their accuracy is improved, it will be possible to better assess masked hypertension, nocturnal hypertension, and the severity and variability of BP. In this review, we discuss the progression in the field, particularly in the last 5 years, ending with sensor-based approaches that incorporate machine learning algorithms to personalized medicine.

Local Pulse Wave Velocity: Theory, Methods, Advancements, and Clinical Applications

Local pulse wave velocity (PWV) is evolving as one of the important determinants of arterial hemodynamics, localized vessel stiffening associated with several pathologies, and a host of other cardiovascular events. Although PWV was introduced over a century ago, only in recent decades, due to various technological advancements, has emphasis been directed toward its measurement from a single arterial section or from piecewise segments of a target arterial section. This emerging worldwide trend in the exploration of instrumental solutions for local PWV measurement has produced several invasive and noninvasive methods. As of yet, however, a univocal opinion on the ideal measurement method has not emerged. Neither have there been extensive comparative studies on the accuracy of the available methods. Recognizing this reality, makes apparent the need to establish guideline-recommended standards for the measurement methods and reference values, without which clinical application cannot be pursued. This paper enumerates all major local PWV measurement methods while pinpointing their salient methodological considerations and emphasizing the necessity of global standardization. Further, a summary of the advancements in measuring modalities and clinical applications is provided. Additionally, a detailed discussion on the minimally explored concept of incremental local PWV is presented along with suggestions of future research questions.

Alterations of blood pulsations parameters in carotid basin due to body position change

The velocity of the pulse wave (PWV) propagating through the vascular tree is an essential parameter for diagnostic the state of the cardiovascular system especially when it is measured in the pool of carotid arteries. In this research, we showed for the first time that the time of the blood-pressure-wave propagation from the heart to the face is a function of the body position. Significant asymmetry and asynchronicity of blood pulsations in the facial area were found in a recumbent position. Parameters of blood pulsations were measured by an advanced camera-based photoplethysmography system in 73 apparently healthy subjects. Most likely, observed changes of the blood-pulsation parameters are caused by variations of the arterial blood pressure due to hydrostatic pressure changes, and secondary reaction of blood vessels in response to these variations. Demonstrated feasibility of PWV measurements in the pool of carotid arteries provides considerable advantages over other technologies. Moreover, possibilities of the method to estimate physiological regulation of the peripheral blood flow (particularly, as a response to the gravitational changes) have been demonstrated. The proposed concept allows development of non-invasive medical equipment capable of solving a wide range of scientific and practical problems related to vascular physiology.