Opportunistic screening for atrial fibrillation by clinical pharmacists in UK general practice during the influenza vaccination season: A cross-sectional feasibility study.

BACKGROUND: Growing prevalence of atrial fibrillation (AF) in the ageing population and its associated life-changing health and resource implications have led to a need to improve its early detection. Primary care is an ideal place to screen for AF; however, this is limited by shortages in general practitioner (GP) resources. Recent increases in the number of clinical pharmacists within primary care makes them ideally placed to conduct AF screening. This study aimed to determine the feasibility of GP practice-based clinical pharmacists to screen the over-65s for AF, using digital technology and pulse palpation during the influenza vaccination season. METHODS AND FINDINGS: Screening was conducted over two influenza vaccination seasons, 2017-2018 and 2018-2019, in four GP practices in Kent, United Kingdom. Pharmacists were trained by a cardiologist to pulse palpate, record, and interpret a single-lead ECG (SLECG). Eligible persons aged ≥65 years (y) attending an influenza vaccination clinic were offered a free heart rhythm check. Six hundred four participants were screened (median age 73 y, 42.7% male). Total prevalence of AF was 4.3%. All participants with AF qualified for anticoagulation and were more likely to be male (57.7%); be older; have an increased body mass index (BMI); and have a CHA2DS2-VASc (Congestive heart failure, Hypertension, Age ≥ 75 years, Diabetes, previous Stroke, Vascular disease, Age 65-74 years, Sex category) score ≥ 3. The sensitivity and specificity of clinical pharmacists diagnosing AF using pulse palpation was 76.9% (95% confidence interval [CI] 56.4-91.0) and 92.2% (95% CI 89.7-94.3), respectively. This rose to 88.5% (95% CI 69.9-97.6) and 97.2% (95% CI 95.5-98.4) with an SLECG. At follow-up, four participants (0.7%) were diagnosed with new AF and three (0.5%) were initiated on anticoagulation. Screening with SLECG also helped identify new non-AF cardiovascular diagnoses, such as left ventricular hypertrophy, in 28 participants (4.6%). The screening strategy was cost-effective in 71.8% and 64.3% of the estimates for SLECG or pulse palpation, respectively. Feedback from participants (422/604) was generally positive. Key limitations of the study were that the intervention did not reach individuals who did not attend the practice for an influenza vaccination and there was a limited representation of UK ethnic minority groups in the study cohort. CONCLUSIONS: This study demonstrates that AF screening performed by GP practice-based pharmacists was feasible, economically viable, and positively endorsed by participants. Furthermore, diagnosis of AF by the clinical pharmacist using an SLECG was more sensitive and more specific than the use of pulse palpation alone. Future research should explore the key barriers preventing the adoption of national screening programmes.

Estimating carotid pulse and breathing rate from near-infrared video of the neck.

OBJECTIVE: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. APPROACH: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, principal component analysis for feature extraction, and hidden Markov models for data smoothing. MAIN RESULTS: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. SIGNIFICANCE: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.

Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables.

Gyrocardiography (GCG) is a new non-invasive technique for assessing heart motions by using a sensor of angular motion - gyroscope - attached to the skin of the chest. In this study, we conducted simultaneous recordings of electrocardiography (ECG), GCG, and echocardiography in a group of subjects consisting of nine healthy volunteer men. Annotation of underlying fiducial points in GCG is presented and compared to opening and closing points of heart valves measured by a pulse wave Doppler. Comparison between GCG and synchronized tissue Doppler imaging (TDI) data shows that the GCG signal is also capable of providing temporal information on the systolic and early diastolic peak velocities of the myocardium. Furthermore, time intervals from the ECG Q-wave to the maximum of the integrated GCG (angular displacement) signal and maximal myocardial strain curves obtained by 3D speckle tracking are correlated. We see GCG as a promising mechanical cardiac monitoring tool that enables quantification of beat-by-beat dynamics of systolic time intervals (STI) related to hemodynamic variables and myocardial contractility.

Multiscale ordinal network analysis of human cardiac dynamics.

In this study, we propose a new information theoretic measure to quantify the complexity of biological systems based on time-series data. We demonstrate the potential of our method using two distinct applications to human cardiac dynamics. Firstly, we show that the method clearly discriminates between segments of electrocardiogram records characterized by normal sinus rhythm, ventricular tachycardia and ventricular fibrillation. Secondly, we investigate the multiscale complexity of cardiac dynamics with respect to age in healthy individuals using interbeat interval time series and compare our findings with a previous study which established a link between age and fractal-like long-range correlations. The method we use is an extension of the symbolic mapping procedure originally proposed for permutation entropy. We build a Markov chain of the dynamics based on order patterns in the time series which we call an ordinal network, and from this model compute an intuitive entropic measure of transitional complexity. A discussion of the model parameter space in terms of traditional time delay embedding provides a theoretical basis for our multiscale approach. As an ancillary discussion, we address the practical issue of node aliasing and how this effects ordinal network models of continuous systems from discrete time sampled data, such as interbeat interval time series.This article is part of the themed issue 'Mathematical methods in medicine: neuroscience, cardiology and pathology'.

Assessment of blind source separation techniques for video-based cardiac pulse extraction.

Blind source separation (BSS) aims at separating useful signal content from distortions. In the contactless acquisition of vital signs by means of the camera-based photoplethysmogram (cbPPG), BSS has evolved the most widely used approach to extract the cardiac pulse. Despite its frequent application, there is no consensus about the optimal usage of BSS and its general benefit. This contribution investigates the performance of BSS to enhance the cardiac pulse from cbPPGs in dependency to varying input data characteristics. The BSS input conditions are controlled by an automated spatial preselection routine of regions of interest. Input data of different characteristics (wavelength, dominant frequency, and signal quality) from 18 postoperative cardiovascular patients are processed with standard BSS techniques, namely principal component analysis (PCA) and independent component analysis (ICA). The effect of BSS is assessed by the spectral signal-to-noise ratio (SNR) of the cardiac pulse. The preselection of cbPPGs, appears beneficial providing higher SNR compared to standard cbPPGs. Both, PCA and ICA yielded better outcomes by using monochrome inputs (green wavelength) instead of inputs of different wavelengths. PCA outperforms ICA for more homogeneous input signals. Moreover, for high input SNR, the application of ICA using standard contrast is likely to decrease the SNR.

A Hidden Markov Model for Seismocardiography.

We propose a hidden Markov model approach for processing seismocardiograms. The seismocardiogram morphology is learned using the expectation-maximization algorithm, and the state of the heart at a given time instant is estimated by the Viterbi algorithm. From the obtained Viterbi sequence, it is then straightforward to estimate instantaneous heart rate, heart rate variability measures, and cardiac time intervals (the latter requiring a small number of manual annotations). As is shown in the conducted experimental study, the presented algorithm outperforms the state-of-the-art in seismocardiogram-based heart rate and heart rate variability estimation. Moreover, the isovolumic contraction time and the left ventricular ejection time are estimated with mean absolute errors of about 5 [ms] and [Formula: see text], respectively. The proposed algorithm can be applied to any set of inertial sensors; does not require access to any additional sensor modalities; does not make any assumptions on the seismocardiogram morphology; and explicitly models sensor noise and beat-to-beat variations (both in amplitude and temporal scaling) in the seismocardiogram morphology. As such, it is well suited for low-cost implementations using off-the-shelf inertial sensors and targeting, e.g., at-home medical services.

Are Nonlinear Model-Free Conditional Entropy Approaches for the Assessment of Cardiac Control Complexity Superior to the Linear Model-Based One?

OBJECTIVE: We test the hypothesis that the linear model-based (MB) approach for the estimation of conditional entropy (CE) can be utilized to assess the complexity of the cardiac control in healthy individuals. METHODS: An MB estimate of CE was tested in an experimental protocol (i.e., the graded head-up tilt) known to produce a gradual decrease of cardiac control complexity as a result of the progressive vagal withdrawal and concomitant sympathetic activation. The MB approach was compared with traditionally exploited nonlinear model-free (MF) techniques such as corrected approximate entropy, sample entropy, corrected CE, two k -nearest-neighbor CE procedures and permutation CE. Electrocardiogram was recorded in 17 healthy subjects at rest in supine position and during head-up tilt with table angles of 15°, 30°, 45°, 60°, and 75°. Heart period (HP) was derived as the temporal distance between two consecutive R-wave peaks and analysis was carried out over stationary sequences of 256 successive HPs. RESULTS: The performance of the MB method in following the progressive decrease of HP complexity with tilt table angles was in line with those of MF approaches and the MB index was remarkably correlated with the MF ones. CONCLUSION: The MB approach can be utilized to monitor the changes of the complexity of the cardiac control, thus speeding up dramatically the CE calculation. SIGNIFICANCE: The remarkable performance of the MB approach challenges the notion, generally assumed in cardiac control complexity analysis based on CE, about the need of MF techniques and could allow real-time applications.

[Options and limitations of heart rate measurement and analysis of heart rate variability by mobile devices: A systematic review]. / Möglichkeiten und Einschränkungen der Herzfrequenzmessung und der Analyse der Herzfrequenzvariabilität mittels mobiler Messgeräte: Eine systematische Literaturübersicht.

BACKGROUND: Heart rate (HR) and heart rate variability (HRV) have been established in the last few years as a non-invasive method for recording the demands on the cardiovascular system. The development enables us today to measure the interbeat intervals with different technologies for calculating HR and HRV. MATERIALS AND METHODS: This review is based on a systematic literature search in PubMed for validity of different measurement techniques and their pros and cons for the measurement of HR and the analysis of HRV. RESULTS: Measurement equipment for recording of interbeat intervals should have a high storage capacity and a sampling rate of 1000 Hz ideally. The quality criteria of freedom of feedback (small, little disruptive), robustness and a non-invasive measurement (e. g. freeze-electrodes or sensors) have to be fulfilled. In addition to the Holter ECG, several portable heart rate watch and chest belt systems provide adequate validity and good applicability. DISCUSSION: The Holter ECG is still the gold standard for the measurement of NN intervals and for the analysis of HRV. Modern heart rate watches show a good correlation with the conformance of freedom from discomfort, robustness and non-invasive measurement and are a good alternative due to the lower disturbance of test persons.

[Pulse oximetry versus electrocardiogram for heart rate assessment during resuscitation of the preterm infant]. / Pulsioximetría frente al monitor de electrocardiograma para la determinación de la frecuencia cardíaca durante la reanimación del recién nacido pretérmino.

BACKGROUND: Heart rate (HR) assessment is essential during neonatal resuscitation, and it is usually done by auscultation or pulse oximetry (PO). The aim of the present study was to determine whether HR assessment with ECG is as fast and reliable as PO during preterm resuscitation. MATERIAL AND METHODS: Thirty-nine preterm (<32 weeks of gestational age and/or<1.500g of birth weight) newborn resuscitations were video-recorded. Simultaneous determinations of HR using ECG and PO were registered every 5s for the first 10min after birth. Time needed to place both devices and to obtain reliable readings, as well as total time of signal loss was registered. The proportion of reliable HR readings available at the beginning of different resuscitation manoeuvres was also determined. RESULTS: Time needed to connect the ECG was shorter compared with the PO (26.64±3.01 vs. 17.10±1.28 s, for PO and ECG, respectively, P<.05). Similarly, time to obtain reliable readings was shorter for the ECG (87.28±12.11 vs. 26.38±3.41 s, for PO and ECG, respectively, P<.05). Availability of reliable HR readings at initiation of different resuscitation manoeuvres was lower with the PO (PO vs. ECG for positive pressure ventilation: 10.52 vs. 57.89% P<.05; intubation: 33.33 vs. 91.66%, P<.05). PO displayed lower HR values during the first 6min after birth (P<.05, between 150 and 300s). CONCLUSIONS: Reliable HR is obtained later with the PO than with the ECG during preterm resuscitation. PO underestimates HR in the first minutes of resuscitation.