Opportunistic detection of atrial fibrillation using blood pressure monitors: a systematic review

Background: Atrial Fibrillation (AF) affects around 2% of the population and early detection is beneficial, allowing patients to begin potentially life-saving anticoagulant therapies. Blood pressure (BP) monitors may offer an opportunity to screen for AF. Aim: To identify and appraise studies which report the diagnostic accuracy of automated BP monitors used for opportunistic AF detection. Methods: A systematic search was performed of the Medline, Medline-in-process and Embase literature databases. Papers were eligible if they described primary studies of the evaluation of a BP device for AF detection, were published in a peer reviewed journal and reported values for the sensitivity and specificity. Included studies were appraised using the QUADAS-2 tool to assess their risk of bias and applicability to opportunistic AF detection. Values for the sensitivity and specificity of AF detection were extracted from each paper and compared. Results and Conclusion: We identified seven papers evaluating six devices from two manufacturers. Only one study scored low risk in all of the QUADAS-2 domains. All studies reported specificity greater than 85% and six reported sensitivity greater than 90%. The studies showed that blood pressure devices with embedded algorithms for detecting arrhythmias show promise as screening tools for AF, comparing favourably with manual pulse palpation. But the studies used different methodologies and many were subject to potential bias. More studies are needed to more precisely define the sensitivity and specificity of opportunistic screening for AF during blood pressure measurement before its clinical utility in the population of interest can be assessed fully

Accuracy of pulse interval timing in ambulatory blood pressure measurement

Blood pressure (BP) monitors rely on pulse detection. Some blood pressure monitors use pulse timings to analyse pulse interval variability for arrhythmia screening, but this assumes that the pulse interval timings detected from BP cuffs are accurate compared with RR intervals derived from ECG. In this study we compared the accuracy of pulse intervals detected using an ambulatory blood pressure monitor (ABPM) with single lead ECG. Twenty participants wore an ABPM for three hours and a data logger which synchronously measured cuff pressure and ECG. RR intervals were compared with corresponding intervals derived from the cuff pressure tracings using three different pulse landmarks. Linear mixed effects models were used to assess differences between ECG and cuff pressure timings and to investigate the effect of potential covariates. In addition, the maximum number of successive oscillometric beats detectable in a measurement was assessed. From 243 BP measurements, the foot landmark of the oscillometric pulse was found to be associated with fewest covariates and had a random error of 9.5 ms. 99% of the cuff pressure recordings had more than 10 successive detectable oscillometric beats. RR intervals can be accurately estimated using an ABPM

Recurring patterns in stationary intervals of abdominal uterine electromyograms during gestation

Abdominal uterine electromyograms (uEMG) studies have focused on uterine contractions to describe the evolution of uterine activity and preterm birth (PTB) prediction. Stationary, non-contracting uEMG has not been studied. The aim of the study was to investigate the recurring patterns in stationary uEMG, their relationship with gestation age and PTB, and PTB predictivity. A public database of 300 (38 PTB) three-channel (S1-S3) uEMG recordings of 30 min, collected between 22 and 35 weeks' gestation, was used. Motion and labour contraction-free intervals in uEMG were identified as 5-min weak-sense stationarity intervals in 268 (34 PTB) recordings. Sample entropy (SampEn), percentage recurrence (PR), percentage determinism (PD), entropy (ER), and maximum length (L MAX) of recurrence were calculated and analysed according to the time to delivery and PTB. Random time series were generated by random shuffle (RS) of actual data. Recurrence was present in actual data (p<0.001) but not RS. In S3, PR (p<0.005), PD (p<0.01), ER (p<0.005), and L MAX (p<0.05) were higher, and SampEn lower (p<0.005) in PTB. Recurrence indices increased (all p<0.001) and SampEn decreased (p<0.01) with decreasing time to delivery, suggesting increasingly regular and recurring patterns with gestation progression. All indices predicted PTB with AUC≥0.62 (p<0.05). Recurring patterns in stationary non-contracting uEMG were associated with time to delivery but were relatively poor predictors of PTB

Effects of dissolved gases on partial anodic passivation phenomena at copper microelectrodes immersed in aqueous NaCl

Anodic passivation for copper exposed to aqueous NaCl (model seawater) is rate limited by diffusion of a poorly soluble Cu(I) chloro species. As a result, a protective layer of CuCl forms on copper metal (with approx. 1 μm thickness) that is then put under strain at more positive applied potentials with explosive events causing current spikes and particulate product expulsion. In this report, the mechanism for this explosive film rupture and particle expulsion process is shown to occur (i) in the absence of underlying anodic gas evolution, and (ii) linked to the presence/nature of gaseous solutes. The film rupture event is proposed to be fundamentally dependent on gas bubble nucleation (triggered by the release of interfacial stress) with surface tension effects by dissolved gases affecting the current spike pattern. Oxygen O2, hydrogen H2, and helium He suppress current spikes and behave differently to argon Ar, nitrogen N2, and carbon dioxide CO2, which considerably enhance current spikes. Vacuum-degassing the electrolyte solution results in behaviour very similar to that observed in the presence of helium. The overall corrosion rate for copper microelectrodes is compared and parameters linked to passivation and corrosion processes are discussed.</p

Application of Deep Neural Network Models for Blood Pressure Classification based on Photoplethysmograpic Recordings

[EN] The measurement of blood pressure (BP) in an uninterrupted and comfortable way for the subject is essential for early diagnosis and monitoring of cardiovascular diseases (CVD). In fact, hypertension is the main risk factor for CVD because, being a hidden health problem with no symptoms until late stages of the disease are reached. This work investigates whether deep neural network models are able to discriminate between healthy and hypertensive subjects based on photoplethysmographic (PPG) recordings, without the need of electrocardiographic (ECG) recordings as well as avoiding manual morphological feature extraction, as has been popularly used in many previous studies. Recordings analyzed consisted of 635 simultaneous PPG and arterial blood pressure (ABP) signals from 50 different patients. The classification was performed with GoogLeNet, ResNet-18 and ResNet-50 pretrained convolutional neural networks (CNN) using as input images the scalogram of PPG segments obtained by continuous wavelet transformation (CWT). Additionally, Adam and SGDM training solvers were used to compare classification performance. After applying early stopping to avoid overfitting, training was performed with more than half of the epochs using Adam optimizer. ResNet-18 CNN provided the highest classification performance with sensitivity of 95.68%, specificity of 93.65%, F1-score of 95.61% an Area under the Roc area of 98.77%. Hence, the application of deep neural network classification models using time frequency transformation of PPG recordings has been able to provide outstanding results in blood pressure classification without requiring neither morphological feature extraction nor ECG features.Research supported by grants DPI2017-83952-C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Cano, J.; Fácila, L.; Langley, P.; Zangróniz, R.; Alcaraz, R.; Rieta, JJ. (2021). Application of Deep Neural Network Models for Blood Pressure Classification based on Photoplethysmograpic Recordings. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.96576581

A new algorithm to diagnose atrial ectopic origin from multi lead ECG systems - insights from 3D virtual human atria and torso

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms

Catheter Ablation Outcome Prediction With Advanced Time-Frequency Features of the Fibrillatory Waves From Patients in Persistent Atrial Fibrillation

[EN] Although catheter ablation (CA) is still the first-line treatment for persistent atrial fibrillation (AF) patients, its limited long-term success rate has motivated clinical interest in preoperative prediction on the procedure¿s outcome to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the ECG have provided promising results. Hence this work explores the ability of a novel set of frequency and amplitud f-waves features, such as spectral entropy (SE), spectral flatness measure (SFM), and amplitud spectrum area (AMSA), along with DF and normalized f-wave amplitude (NFWA), to improve CA outcome prediction. Despite all single indices reported statistically significant differences between patients who relapsed to AF and those who maintained sinus rhythm after a follow-up of 9 months for 204 6 s-length ECG intervals extracted from 51 persistent AF patients, they obtained a limited discriminant ability ranging between 55 and 62%, which was overcome by 15¿23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the fwaves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction.This research has been supported by grants DPI201783952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-la Mancha and AICO/2019/036 from Generalitat Valenciana. Moreover, Pilar Escribano holds a graduate research scholarship from University of Castilla-La ManchaEscribano, P.; Ródenas, J.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Catheter Ablation Outcome Prediction With Advanced Time-Frequency Features of the Fibrillatory Waves From Patients in Persistent Atrial Fibrillation. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.396S1

Are Coronary Sinus Features Reflecting the Effect of Catheter Ablation of Atrial Fibrillation as P-waves Do?

[EN] Atrial substrate alteration due to catheter ablation (CA) of atrial fibrillation (AF) is primarily assessed from P-waves. Nonetheless, how CA affects critical structures is ignored. The aim of the current study is to investigate if CA effect on CS, the principal CA reference, is related to that observed from P-waves analysis. Five-minute lead II and bipolar CS recordings of 29 paroxysmal AF patients were obtained before, during and after CA. Duration, amplitude, area and heart-rate (HR) variability (HRV) features were calculated for P-waves and local activation waves (LAWs). Normalization mitigated the effect of HR fluctuations. Linear correlations between each P-wave and LAW were tested with linear regression (LR) and Pearson correlation (PC) and nonlinear correlations with cross-quadratic sample entropy (CQSE). Correlation between the CA effect on P-waves and LAWs was investigated with PC. Negligent statistical correlations were found by PC and LR for amplitude and area (¿3.30% 90%, p < 0.0297). Apart from HRV, no significant correlations between CS LAWs and P-waves have been found. HR fluctuations mask any possible tuning and normalization should be applied prior to the analysis.Research supported by grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER UE, SBPLY/17/180501/000411 from JCCLM and AICO/2021/286 from GVA.Vraka, A.; Bertomeu-González, V.; Hornenro, F.; Langley, P.; Alcaraz, R.; Rieta, JJ. (2021). Are Coronary Sinus Features Reflecting the Effect of Catheter Ablation of Atrial Fibrillation as P-waves Do?. IEEE. 1-4. https://doi.org/10.1109/EHB52898.2021.96576391

Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings

[EN] In the last years, atrial fibrillation (AF) has become one of the most remarkable health problems in the developed world. This arrhythmia is associated with an increased risk of cardiovascular events, being its early detection an unresolved challenge. To palliate this issue, long-term wearable electrocardiogram (ECG) recording systems are used, because most of AF episodes are asymptomatic and very short in their initial stages. Unfortunately, portable equipments are very susceptible to be contaminated with different kind of noises, since they work in highly dynamics and ever-changing environments. Within this scenario, the correct identification of free-noise ECG segments results critical for an accurate and robust AF detection. Hence, this work presents a deep learning-based algorithm to identify high-quality intervals in single-lead ECG recordings obtained from patients with paroxysmal AF. The obtained results have provided a remarkable ability to classify between high- and low-quality ECG segments about 92%, only misclassifying around 7% of clean AF intervals as noisy segments. These outcomes have overcome most previous ECG quality assessment algorithms also dealing with AF signals by more than 20%.This research has been supported by the grants DPI2017-83952-C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744.Huerta, A.; Martinez-Rodrigo, A.; Arias, MA.; Langley, P.; Rieta, JJ.; Alcaraz, R. (2020). Application of Deep Learning for Quality Assessment of Atrial Fibrillation ECG Recordings. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.367S1