Induced pain affects auricular and body biosignals: From cold stressor to deep breathing

Pain affects every fifth adult worldwide and is a significant health problem. From a physiological perspective, pain is a protective reaction that restricts physical functions and causes responses in physiological systems. These responses are accessible for evaluation via recorded biosignals and can be favorably used as feedback in active pain therapy via auricular vagus nerve stimulation (aVNS). The aim of this study is to assess the significance of diverse parameters of biosignals with respect to their deflection from cold stressor to deep breathing and their suitability for use as biofeedback in aVNS stimulator. Seventy-eight volunteers participated in two cold pressors and one deep breathing test. Three targeted physiological parameters (RR interval of electrocardiogram, cardiac deflection magnitude ZAC of ear impedance signal, and cardiac deflection magnitude PPGAC of finger photoplethysmogram) and two reference parameters (systolic and diastolic blood pressures BPS and BPD) were derived and monitored. The results show that the cold water decreases the medians of targeted parameters (by 5.6, 9.3%, and 8.0% of RR, ZAC, and PPGAC, respectively) and increases the medians of reference parameters (by 7.1% and 6.1% of BPS and BPD, respectively), with opposite changes in deep breathing. Increasing pain level from relatively mild to moderate/strong with cold stressor varies the medians of targeted and reference parameters in the range from 0.5% to 6.0% (e.g., 2.9% for RR, ZAC and 6.0% for BPD). The physiological footprints of painful cold stressor and relaxing deep breathing were shown for auricular and non-auricular biosignals. The investigated targeted parameters can be used as biofeedback to close the loop in aVNS to personalize the pain therapy and increase its compliance

Difference in pulse arrival time at forehead and at finger as a surrogate of pulse transit time

Pulse transit time (PTT) difference (PTTD) to the forehead and finger dynamics are compared to pulse arrival time towards the finger (PATF) dynamics during a tilt table test. Two frequency bands, where different physiological information is expected, are analyzed: low frequency (LF) influenced by both sympathetic and parasympathetic activity, and high frequency (HF) influenced by parasympathetic activity. As PATF, PTTD is influenced by PTT, but in contrast to PATF, PTTD is not influenced by the pre-ejection period (PEP). This is advantaging in certain applications such as arterial stiffness assessment or blood pressure estimation. Results showed higher correlation between PTTD and PATF during rest stages than during tilt stage, when the PEP dynamics have stronger effect in PATF dynamics. This suggests that PTTD variability can potentially be a surrogate of PTT variability that is not influenced by PEP, which is advantaging in the previously mentioned applications. However, further studies must be elaborated in order to evaluate the potential of PTTD in such specific applications

Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations

Pulse photoplethysmography (PPG) is a simple and economical technique for obtaining cardiovascular information. In fact, PPG has become a very popular technology among wearable devices. However, the PPG signal is well-known to be very vulnerable to artifacts, and a good quality signal cannot be expected for most of the time in daily life. The percentage of time that a given measurement can be estimated (e.g., pulse rate) is denoted coverage (C), and it is highly dependent on the subject activity and on the configuration of the sensor, location, and stability of contact. This work aims to quantify the coverage of PPG sensors, using the simultaneously recorded electrocardiogram as a reference, with the PPG recorded at different places in the body and under different stress conditions. While many previous works analyzed the feasibility of PPG as a surrogate for heart rate variability analysis, there exists no previous work studying coverage to derive other cardiovascular indices. We report the coverage not only for estimating pulse rate (PR) but also for estimating pulse arrival time (PAT) and pulse amplitude variability (PAV). Three different datasets are analyzed for this purpose, consisting of a tilt-table test, an acute emotional stress test, and a heat stress test. The datasets include 19, 120, and 51 subjects, respectively, with PPG at the finger and at the forehead for the first two datasets and at the earlobe, in addition, for the latter. C ranges from 70% to 90% for estimating PR. Regarding the estimation of PAT, C ranges from 50% to 90%, and this is very dependent on the PPG sensor location, PPG quality, and the fiducial point (FP) chosen for the delineation of PPG. In fact, the delineation of the FP is critical in time for estimating derived series such as PAT due to the small dynamic range of these series. For the estimation of PAV, the C rates are between 70% and 90%. In general, lower C rates have been obtained for the PPG at the forehead. No difference in C has been observed between using PPG at the finger or at the earlobe. Then, the benefits of using either will depend on the application. However, different C rates are obtained using the same PPG signal, depending on the FP chosen for delineation. Lower C is reported when using the apex point of the PPG instead of the maximum flow velocity or the basal point, with a difference from 1% to even 10%. For further studies, each setup should first be analyzed and validated, taking the results and guidelines presented in this work into account, to study the feasibility of its recording devices with respect to each specific application

Modeling of the Effect of Alcohol on Episode Patterns in Atrial Fibrillation

Growing evidence shows that alcohol triggers paroxysmal atrial fibrillation (PAF) in some patients. How-ever, there is a lack of methods for assessing the causality between triggers and atrial fibrillation (AF) episodes. Accordingly, this work aims to develop an approach to episode modeling under the influence of alcohol for the purpose of evaluating causality assessment methods. The alternating, bivariate Hawkes model is used to produce episode patterns, where the conditional intensity function λ1(t) defines the transitions from sinus rhythm (SR) to AF. The effect of alcohol consumption is characterized by a body reactivity function, defined by the base intensity μ1(t), which alters λ1(t). Different AF episode patterns were modeled for alcohol units ranging from 0 to 15. The mean AF burden without alcohol was 17.2%, which doubled with 9 alcohol units; the number of AF episodes doubled from 12.9 with 8 alcohol units. The aggregation of AF episodes tended to decrease after 6 alcohol units. The proposed model of alcohol-affected PAF patterns may be useful for assessing the methods for evaluation of causality between triggers and PAF occurrence

Estimation of Heart Rate Recovery after Stair Climbing Using a Wrist-Worn Device

Heart rate recovery (HRR) after physical exercise is a convenient method to assess cardiovascular autonomic function. Since stair climbing is a common daily activity, usually followed by a slow walking or rest, this type of activity can be considered as an alternative HRR test. The present study explores the feasibility to estimate HRR parameters after stair climbing using a wrist-worn device with embedded photoplethysmography and barometric pressure sensors. A custom-made wrist-worn device, capable of acquiring heart rate and altitude, was used to estimate the time-constant of exponential decay τ , the short-term time constant S , and the decay of heart rate in 1 min D . Fifty-four healthy volunteers were instructed to climb the stairs at three different climbing rates. When compared to the reference electrocardiogram, the absolute and percentage errors were found to be ≤ 21.0 s (≤ 52.7%) for τ , ≤ 0.14 (≤ 19.2%) for S , and ≤ 7.16 bpm (≤ 20.7%) for D in 75% of recovery phases available for analysis. The proposed approach to monitoring HRR parameters in an unobtrusive way may complement information provided by personal health monitoring devices (e.g., weight loss, physical activity), as well as have clinical relevance when evaluating the efficiency of cardiac rehabilitation program outside the clinical setting

Diabetic vascular damage: review of pathogenesis and possible evaluation technologies

Diabetes mellitus (DM) is a rapidly increasing problem in health care worldwide: recent forecast indicates that the number of DM patients will rise to 640 million by 2040. Vascular damage is associated with severe complications, including cardiac neuropathy and limb amputation. Therefore, early prediction of diabetic vascular damage using advanced technologies is an important challenge because timely preventive and therapeutic measures could diminish the risk of development and burden of complications. The aim of the article is to provide a review of the initial stages of vascular damage and main mechanisms for development, as well as appropriate modern technologies for prediction and diagnosis. The manuscript provides an overview of interrelated vascular damage mechanisms influenced by diabetes, along with a review of possible technologies for early prediction and diagnosis. A comparative analysis of technologies appropriate for particular issues of prediction is summarised in the discussion

Geriatric care management system powered by the IoT and computer vision techniques

The digitalisation of geriatric care refers to the use of emerging technologies to manage and provide person-centered care to the elderly by collecting patients’ data electronically and using them to streamline the care process, which improves the overall quality, accuracy, and efficiency of healthcare. In many countries, healthcare providers still rely on the manual measurement of bioparameters, inconsistent monitoring, and paper-based care plans to manage and deliver care to elderly patients. This can lead to a number of problems, including incomplete and inaccurate record-keeping, errors, and delays in identifying and resolving health problems. The purpose of this study is to develop a geriatric care management system that combines signals from various wearable sensors, noncontact measurement devices, and image recognition techniques to monitor and detect changes in the health status of a person. The system relies on deep learning algorithms and the Internet of Things (IoT) to identify the patient and their six most pertinent poses. In addition, the algorithm has been developed to monitor changes in the patient’s position over a longer period of time, which could be important for detecting health problems in a timely manner and taking appropriate measures. Finally, based on expert knowledge and a priori rules integrated in a decision tree-based model, the automated final decision on the status of nursing care plan is generated to support nursing staff

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF

Background: Consumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases. Objective: DoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats. Methods and Results: We performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as “AF,” “SR” or “Cannot be concluded.” In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPGbased cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012). Conclusions: This is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contraction