Ambulatory ECG Monitoring in Atrial Fibrillation Management

Curcumin improves atrial fibrillation susceptibility by regulating tsRNA expression in aging mouse atrium

Age is an independent risk factor for atrial fibrillation (AF), and curcumin can delay aging related disease through reducing oxidative stress and inflammation. However, its target in aging-related AF remains unclear. Transfer RNA-derived small RNA (tsRNA) is a novel short non-coding RNA (sncRNA), and exerts a potential regulatory function in aging. This study was to explore the therapeutic targets of curcumin in atrium of aged mice by PANDORA-seq. Aged mice (18 month) were treated with curcumin (100 mg/kg). Rapid transjugular atrial pacing was performed to observe AF inducibility. SA-β-gal staining, reactive oxygen species (ROS) detection and qRT-PCR were used to assess the degree of aging and oxidative stress/inflammation levels. PANDORA-seq was performed to reveal the differentially expressed sncRNAs in the atrium of mice. The results showed that curcumin reduced the susceptibility AF of aged mice by improving aging-related atrial fibrosis. Compared to young mice (5 month) group, aged mice yielded 473 significantly altered tsRNA sequences, while 947 tsRNA sequences were significantly altered after treated with curcumin. Enrichment analysis revealed that the target genes were mainly related to DNA damage and protein modification. Compared with the 5 month group, the expression levels of mature-mt_tRNA-Val-TAC_CCA_end, mature-mt_tRNA-Glu-TTC_CCA_end, and mature-tRNA-Asp-GTC_CCA_end were up-regulated in the 18 month group, while the expression of mature-mt_tRNA-Thr-TGT_5_end was down-regulated. This trend was reversed in the 18 month + curcumin group. Increased cellular ROS levels, inflammation expression and senescence in aged mice atrium were improved by the down-regulation of mature-mt_tRNA-Val-TAC_CCA_end. In conclusion, our findings identified mature-mt_tRNA-Val-TAC_CCA_end participated in the mechanism of aging-related atrial fibrosis, providing new intervention target of aging-related AF.

Enhanced premature ventricular contraction pulse detection and classification using deep convolutional neural network

Access to accurate and precise monitoring systems for cardiac arrhythmia could contribute significantly to preventing damage and subsequent heart disorders. The present research concentrates on using photoplethysmography (PPG) and arterial blood pressure (ABP) with deep convolutional neural networks (CNN) for the classification and detection of fetal cardiac arrhythmia or premature ventricular contractions (PMVCs). The framework for the study entails (Icentia 11k) a public dataset of ECG signals consisting of different cardiac abnormalities. Following this, the weights obtained from the Icentia 11k dataset are transferred to the proposed CNN. Finally, fine-tuning was carried out to improve the accuracy of classification. Results obtained showcase the capacity of the proposed method to detect and classify PMVCs into three types: Normal, P1, and P2 with an accuracy of 99.9%, 99.8%, and 99.5%.

Non-invasive mid-term electrocardiogram patch monitoring is effective in detecting atrial fibrillation

BACKGROUND

Atrial fibrillation (AF) is a cause of serious morbidity such as stroke. Early detection and treatment of AF is important. Current guidelines recommend screening via opportunistic pulse taking or 12‑lead electrocardiogram. Mid-term ECG patch monitors increases the sensitivity of AF detection.

METHODS

The Singapore Atrial Fibrillation Study is a prospective multi-centre study aiming to study the incidence of AF in patients with no prior AF and a CHA2DS2-VASc score of at least 1, with the use of a mid-term continuous ECG monitoring device (Spyder ECG). Consecutive patients from both inpatient and outpatient settings were recruited from 3 major hospitals from May 2016 to December 2019.

RESULTS

Three hundred and fifty-five patients were monitored. 6 patients (1.7%) were diagnosed with AF. There were no significant differences in total duration of monitoring between the AF and non-AF group (6.39 ± 3.19 vs 5.42 ± 2.46 days, p = 0.340). Patients with newly detected AF were more likely to have palpitations (50.0% vs 11.8%, p = 0.027). Half of the patients (n = 3, 50.0%) were diagnosed on the first day of monitoring and the rest were diagnosed after 24 h. On univariate analysis, only hyperlipidemia was associated with reduced odds of being diagnosed with AF (OR HR 0.08 CI 0.01-0.74, p = 0.025). In a group of 128 patients who underwent coronary artery bypass grafting and had post-operative ECG monitoring, 9 patients (7.0%) were diagnosed with post-operative AF.

CONCLUSIONS

The use of non-invasive mid-term patch-based ECG monitoring is an effective modality for AF screening.

Atrioventricular Synchronization for Detection of Atrial Fibrillation and Flutter in One to Twelve ECG Leads Using a Dense Neural Network Classifier

This study investigates the use of atrioventricular (AV) synchronization as an important diagnostic criterion for atrial fibrillation and flutter (AF) using one to twelve ECG leads. Heart rate, lead-specific AV conduction time, and P-/f-wave amplitude were evaluated by three representative ECG metrics (mean value, standard deviation), namely RR-interval (RRi-mean, RRi-std), PQ-interval (PQi-mean, PQI-std), and PQ-amplitude (PQa-mean, PQa-std), in 71,545 standard 12-lead ECG records from the six largest PhysioNet CinC Challenge 2021 databases. Two rhythm classes were considered (AF, non-AF), randomly assigning records into training (70%), validation (20%), and test (10%) datasets. In a grid search of 19, 55, and 83 dense neural network (DenseNet) architectures and five independent training runs, we optimized models for one-lead, six-lead (chest or limb), and twelve-lead input features. Lead-set performance and SHapley Additive exPlanations (SHAP) input feature importance were evaluated on the test set. Optimal DenseNet architectures with the number of neurons in sequential [1st, 2nd, 3rd] hidden layers were assessed for sensitivity and specificity: DenseNet [16,16,0] with primary leads (I or II) had 87.9-88.3 and 90.5-91.5%; DenseNet [32,32,32] with six limb leads had 90.7 and 94.2%; DenseNet [32,32,4] with six chest leads had 92.1 and 93.2%; and DenseNet [128,8,8] with all 12 leads had 91.8 and 95.8%, indicating sensitivity and specificity values, respectively. Mean SHAP values on the entire test set highlighted the importance of RRi-mean (100%), RR-std (84%), and atrial synchronization (40-60%) for the PQa-mean (aVR, I), PQi-std (V2, aVF, II), and PQi-mean (aVL, aVR). Our focus on finding the strongest AV synchronization predictors of AF in 12-lead ECGs would lead to a comprehensive understanding of the decision-making process in advanced neural network classifiers. DenseNet self-learned to rely on a few ECG behavioral characteristics: first, characteristics usually associated with AF conduction such as rapid heart rate, enhanced heart rate variability, and large PQ-interval deviation in V2 and inferior leads (aVF, II); second, characteristics related to a typical P-wave pattern in sinus rhythm, which is best distinguished from AF by the earliest negative P-peak deflection of the right atrium in the lead (aVR) and late positive left atrial deflection in lateral leads (I, aVL). Our results on lead-selection and feature-selection practices for AF detection should be considered for one- to twelve-lead ECG signal processing settings, particularly those measuring heart rate, AV conduction times, and P-/f-wave amplitudes. Performances are limited to the AF diagnostic potential of these three metrics. SHAP value importance can be used in combination with a human expert's ECG interpretation to change the focus from a broad observation of 12-lead ECG morphology to focusing on the few AV synchronization findings strongly predictive of AF or non-AF arrhythmias. Our results are representative of AV synchronization findings across a broad taxonomy of cardiac arrhythmias in large 12-lead ECG databases.

Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network

Objective. We propose a model that can perform multi-label classification on 26 cardiac abnormalities from reduced lead Electrocardiograms (ECGs) and interpret the model.Approach. PhysioNet/Computing in Cardiology (CinC) challenge 2021 datasets are used to train the model. All recordings shorter than 20 seconds are preprocessed by normalizing, resampling, and zero-padding. The frequency domains of the recordings are obtained by applying Fast Fourier Transform. The time domain and frequency domain of the signals are fed into two separate deep convolutional neural networks. The outputs of these networks are then concatenated and passed through a fully connected layer that outputs the probabilities of 26 classes. Data imbalance is addressed by using a threshold of 0.13 to the sigmoid output. The 2-lead model is tested under noise contamination based on the quality of the signal and interpreted using SHapley Additive exPlanations (SHAP).Main results. The proposed method obtained a challenge score of 0.55, 0.51, 0.56, 0.55, and 0.56, ranking 2nd, 5th, 3rd, 3rd, and 3rd out of 39 officially ranked teams on 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead hidden test datasets, respectively, in the PhysioNet/CinC challenge 2021. The model performs well under noise contamination with mean F1 scores of 0.53, 0.56 and 0.56 for the excellent, barely acceptable and unacceptable signals respectively. Analysis of the SHAP values of the 2-lead model verifies the performance of the model while providing insight into labeling inconsistencies and reasons for the poor performance of the model in some classes.Significance. We have proposed a model that can accurately identify 26 cardiac abnormalities using reduced lead ECGs that performs comparably with 12-lead ECGs and interpreted the model behavior. We demonstrate that the proposed model using only the limb leads performs with accuracy comparable to that using all 12 leads.

Artificial intelligence-electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode

Aims

Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF.

Methods and results

We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA₂DS₂-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2-5 years prior to AF, 23.6% 1-2 years prior to AF, 34.0% 0-3 months prior to AF, 40.9% 0-3 months after AF, 35.2% 1-2 years after AF, and 42.2% 2-5 years after AF (P < 0.001). The rate of increase prior to AF was higher for age >50 years CHA₂DS₂-VASc score ≥4.

Conclusion

The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1-2 years following AF and continues to increase thereafter.

Low-Cost Wearable Fluidic Sweat Collection Patch for Continuous Analyte Monitoring and Offline Analysis

Sweat sensors allow for new unobtrusive ways to continuously monitor an athlete's performance and health status. Significant advances have been made in the optimization of sensitivity, selectivity, and durability of electrochemical sweat sensors. However, comparing the in situ performance of these sensors in detail remains challenging because standardized sweat measurement methods to validate sweat sensors in a physiological setting do not yet exist. Current collection methods, such as the absorbent patch technique, are prone to contamination and are labor-intensive, which limits the number of samples that can be collected over time for offline reference measurements. We present an easy-to-fabricate sweat collection system that allows for continuous electrochemical monitoring, as well as chronological sampling of sweat for offline analysis. The patch consists of an analysis chamber hosting a conductivity sensor and a sequence of 5 to 10 reservoirs that contain level indicators that monitor the filling speed. After testing the performance of the patch in the laboratory, elaborate physiological validation experiments (3 patch locations, 6 participants) were executed. The continuous sweat conductivity measurements were compared with laboratory [Na⁺] and [Cl^-] measurements of the samples, and a strong linear relationship (R² = 0.97) was found. Furthermore, sweat rate derived from ventilated capsule measurement at the three locations was compared with patch filling speed and continuous conductivity readings. As expected from the literature, sweat conductivity was linearly related to sweat rate as well. In short, a successfully validated sweat collection patch is presented that enables sensor developers to systematically validate novel sweat sensors in a physiological setting.

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

Application of Dense Neural Networks for Detection of Atrial Fibrillation and Ranking of Augmented ECG Feature Set

Considering the significant burden to patients and healthcare systems globally related to atrial fibrillation (AF) complications, the early AF diagnosis is of crucial importance. In the view of prominent perspectives for fast and accurate point-of-care arrhythmia detection, our study optimizes an artificial neural network (NN) classifier and ranks the importance of enhanced 137 diagnostic ECG features computed from time and frequency ECG signal representations of short single-lead strips available in 2017 Physionet/CinC Challenge database. Based on hyperparameters' grid search of densely connected NN layers, we derive the optimal topology with three layers and 128, 32, 4 neurons per layer (DenseNet-3@128-32-4), which presents maximal F1-scores for classification of Normal rhythms (0.883, 5076 strips), AF (0.825, 758 strips), Other rhythms (0.705, 2415 strips), Noise (0.618, 279 strips) and total F1 relevant to the CinC Challenge of 0.804, derived by five-fold cross-validation. DenseNet-3@128-32-4 performs equally well with 137 to 32 features and presents tolerable reduction by about 0.03 to 0.06 points for limited input sets, including 8 and 16 features, respectively. The feature reduction is linked to effective application of a comprehensive method for computation of the feature map importance based on the weights of the activated neurons through the total path from input to specific output in DenseNet. The detailed analysis of 20 top-ranked ECG features with greatest importance to the detection of each rhythm and overall of all rhythms reveals DenseNet decision-making process, noticeably corresponding to the cardiologists' diagnostic point of view.

AFCNNet: Automated detection of AF using chirplet transform and deep convolutional bidirectional long short term memory network with ECG signals

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia and is characterized by the heart's beating in an uncoordinated manner. In clinical studies, patients often do not have visible symptoms during AF, and hence it is harder to detect this cardiac ailment. Therefore, automated detection of AF using the electrocardiogram (ECG) signals can reduce the risk of stroke, coronary artery disease, and other cardiovascular complications. In this paper, a novel time-frequency domain deep learning-based approach is proposed to detect AF and classify terminating and non-terminating AF episodes using ECG signals. This approach involves evaluating the time-frequency representation (TFR) of ECG signals using the chirplet transform. The two-dimensional (2D) deep convolutional bidirectional long short-term memory (BLSTM) neural network model is used to detect and classify AF episodes using the time-frequency images of ECG signals. The proposed TFR based 2D deep learning approach is evaluated using the ECG signals from three public databases. Our developed approach has obtained an accuracy, sensitivity, and specificity of 99.18% (Confidence interval (CI) as [98.86, 99.49]), 99.17% (CI as [98.85 99.49]), and 99.18% (CI as [98.86 99.49]), respectively, with 10-fold cross-validation (CV) technique to detect AF automatically. The proposed approach also classified terminating and non-terminating AF episodes with an average accuracy of 75.86%. The average accuracy value obtained using the proposed approach is higher than the short-time Fourier transform (STFT), discrete-time continuous wavelet transform (DT-CWT), and Stockwell transform (ST) based time-frequency analysis methods with deep convolutional BLSTM models to detect AF. The proposed approach has better AF detection performance than the existing deep learning-based techniques using ECG signals from the MIT-BIH database.

A Batteryless Motion-Adaptive Heartbeat Detection System-on-Chip Powered by Human Body Heat

This paper presents a batteryless heartbeat detection system-on-chip (SoC) powered by human body heat. An adaptive threshold generation architecture using pulse-width locked loop (PWLL) is developed to detect heartbeats from electrocardiogram (ECG) in the presence of motion artifacts. The sensing system is autonomously powered by harvesting thermal energy from human body heat using a thermoelectric generator (TEG) coupled to a low-voltage, self-starting boost converter and integrated power management system. The SoC was implemented in a 0.18 μm CMOS process and is fully functional with a minimum input power of 20 μW, provided by a portable TEG at 20 mV with a ~0.5 °C temperature gradient. The complete system demonstrates motion-adaptive, power-autonomous heartbeat detection for sustainable healthcare using wearable devices.

Continuous ECG Monitoring Trial for Outpatient - Patient Receptiveness and Signal Accuracy

We have conducted a clinical trial to investigate the receptiveness and signal accuracy of our in-house light-weight single lead wearable wireless ECG device with 20 outpatients, who were suspected of cardiac rhythm issues. The receptiveness was measured via a survey score sheet while the signal accuracy was evaluated by comparing the Holter's hourly heart rate report (the gold-standard) against the ones from our device. In terms of receptiveness, a score of 8.6 indicates good patient compliance. In terms of accuracy, the mean absolute error is 1.4 bpm (beats per minute) with precision of ±1.6 bpm. In addition, measurements from both devices were found to be linearly related with coefficient of determination, r², of 0.97. Furthermore, the limits of agreement were calculated to be +3.54 and -4.71 based on the Altman and Bland technique, which indicated good agreement for most of our measurements against the Holter device. In addition, this paper also discusses the unexpected challenges of conducting a trial on actual outpatients which can be used as a reference for similar subsequent studies.

The Accuracy of Atrial Fibrillation Detection from Wrist Photoplethysmography. A Study on Post-Operative Patients

Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Although not life-threatening itself, AF significantly increases the risk of stroke and myocardial infarction. Current tools available for screening and monitoring of AF are inadequate and an unobtrusive alternative, suitable for long-term use, is needed. This paper evaluates an atrial fibrillation detection algorithm based on wrist photoplethysmographic (PPG) signals. 29 patients recovering from surgery in the post-anesthesia care unit were monitored. 15 patients had sinus rhythm (SR, 67.5± 10.7 years old, 7 female) and 14 patients had AF (74.8± 8.3 years old, 8 female) during the recordings. Inter-beat intervals (IBI) were estimated from PPG signals. As IBI estimation is highly sensitive to motion or other types of noise, acceleration signals and PPG waveforms were used to automatically detect and discard unreliable IBI. AF was detected from windows of 20 consecutive IBI with 98.45±6.89% sensitivity and 99.13±1.79% specificity for 76.34±19.54% of the time. For the remaining time, no decision was taken due to the lack of reliable IBI. The results show that wrist PPG is suitable for long term monitoring and AF screening. In addition, this technique provides a more comfortable alternative to ECG devices.

The multiple arrhythmia dataset evaluation database (M.A.D.A.E.)

The convergence between wearable and medical device technologies is a natural progression. Miniaturization has allowed the design of small, compact monitoring systems that can record physiological signals over longer periods of time. Thus, the potential for these devices to expand the understanding of disease progression and patients' clinical status is very high. The accuracy of these devices, however, is dependent upon the computer algorithms utilized in the analysis of the large volume of physiological data monitored and/or recorded by the devices. Automated interpretation of the data by these new technologies, therefore, necessitates closer examination by regulatory organizations. The current requirements for the validation of novel Ambulatory ECG (A-ECG) annotation algorithms are based on the AAMI/ANSI-EC57 and IEC60601-2-47 Standard. These standards are being updated, but they rely on a very limited set of digitized ECG recordings from a couple of ECG databases built in the first half of the 70's. These reference signals are obsolete. We are developing a validation tool for computerized methods designed to detect and monitor cardiac activities based on body-surface ECGs. We will rely on a set of existing digital high-resolution 12‑lead A-ECG recordings acquired in cardiac patients and healthy individuals. These ECG signals include a large and unique set of electrocardiographic events. This tool is being qualified by the Center for Devices and Radiological Health of the United States Food and Drug Administration (FDA) as a Medical Device Development Tool (MDDT). This document provides insights into the design of the M.A.D.A.E. database, its functionalities, and its ultimate role in enabling the next generations of automatic interpretation of ECG signals.

Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions

OBJECTIVE

Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life.

APPROACH

The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement.

MAIN RESULTS

CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa  =  0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa  =  0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%-57.6% depending on the movement threshold compared to 92.1% of ECG.

SIGNIFICANCE

The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.

Atrial Fibrillation Detection Using a Novel Cardiac Ambulatory Monitor Based on Photo-Plethysmography at the Wrist

Background Long-term continuous cardiac monitoring would aid in the early diagnosis and management of atrial fibrillation ( AF ). This study examined the accuracy of a novel approach for AF detection using photo-plethysmography signals measured from a wrist-based wearable device. Methods and Results ECG and contemporaneous pulse data were collected from 2 cohorts of AF patients: AF patients (n=20) undergoing electrical cardioversion ( ECV ) and AF patients (n=40) that were prescribed for 24 hours ECG Holter in outpatient settings ( HOL ). Photo-plethysmography and acceleration data were collected at the wrist and processed to determine the inter-pulse interval and discard inter-pulse intervals in presence of motion artifacts. A Markov model was deployed to assess the probability of AF given irregular pattern in inter-pulse interval sequences. The AF detection algorithm was evaluated against clinical rhythm annotations of AF based on ECG interpretation. Photo-plethysmography recordings from apparently healthy volunteers (n=120) were used to establish the false positive AF detection rate of the algorithm. A total of 42 and 855 hours (AF: 21 and 323 hours) of photo-plethysmography data were recorded in the ECV and HOL cohorts, respectively. AF was detected with >96% accuracy ( ECV, sensitivity=97%; HOL , sensitivity=93%; both with specificity=100%). Because of motion artifacts, the algorithm did not provide AF classification for 44±16% of the monitoring period in the HOL group. In healthy controls, the algorithm demonstrated a <0.2% false positive AF detection rate. Conclusions A novel AF detection algorithm using pulse data from a wrist-wearable device can accurately discriminate rhythm irregularities caused by AF from normal rhythm.

Assessment of the clinical efficacy of the heart spectrum blood pressure monitor for diagnosis of atrial fibrillation: An unblinded clinical trial

Atrial fibrillation (AF) is the most common arrhythmia. The most common diagnostic method, 12-lead electrocardiogram (ECG), can record episodes of arrhythmia from which the type and severity can be determined. The Heart Spectrum Blood Pressure Monitor (P2; OSTAR Meditech Corp., New Taipei City, Taiwan) is used to measure cardiovascular pressure change with fast Fourier transform (FFT) analysis to obtain heart rate frequency variability and accurate blood pressure data. We compared the diagnostic efficacy of the Heart Spectrum Blood Pressure Monitor to a 12-lead ECG (gold standard) for patients with AF. Three measurement methods were used in this study to analyze the heart index and compare the results with simultaneous 12-lead ECG: blood pressure; mean arterial pressure, which was calculated from individual blood pressure as a constant pressure; and a constant pressure of 60 mmHg. The physician used a 12-lead ECG and the Heart Spectrum Blood Pressure Monitor simultaneously. The Heart Spectrum Blood Pressure Monitor used FFT analysis to diagnose AF, and the findings were compared to the 12-lead ECG readings. This unblinded clinical trial was conducted in the emergency department of Taipei Medical University Hospital. Twenty-nine subjects with AF and 33 without AF aged 25 to 97 y (mean, 63.5 y) were included. Subjects who were exposed to high-frequency surgical equipment during testing, those with cardiac pacemakers or implantable defibrillators, and pregnant women were excluded. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 97%, 97%, 97%, and 97%, respectively, for method 1; 90%, 100%, 100%, and 91%, respectively, for method 2; and 100%, 94%, 94%, and 100%, respectively, for method 3. The sensitivity, specificity, PPV, and NPV for both methods ranged between 90% and 100%, indicating that the Heart Spectrum Blood Pressure Monitor can be effectively applied for AF detection.

Portable out-of-hospital electrocardiography: A review of current technologies

Background

Availability of portable and home‐based electrocardiography (ECG) is an important medical innovation, which has a potential to transform medical care. We performed this review to understand the current state of out‐of‐hospital portable ECG technologies with respect to their scope, ease of use, data transmission capabilities, and diagnostic accuracy.

Methods

We conducted PubMed and Internet searches for “handheld” or “wearable” or “patch” electrocardiography devices to enlist available technologies. We also searched PubMed with names of individual devices to obtain additional citations. We classified available devices as a “single limb lead ECG recording devices” and chest‐lead “ECG recording devices.” If a device used more than three electrodes, it was defined as a conventional electrocardiography or Holter machine and was excluded from this review.

Results

We identified a total of 15 devices. Overall, only six of these devices (five single lead and one chest lead) featured in published medical literature as identified from PubMed search. A total of 13 citations were available for the single limb lead ECG recording devices and 6 citations for the chest‐lead ECG recording devices.

Conclusions

Despite the increase in number of such devices, published biomedical literature regarding their diagnostic accuracy, reproducibility, or utility is scant.

[Implantable loop recorder in atrial fibrillation and after catheter ablation]

BACKGROUND

Implantable loop recorders (ILR) are an established diagnostic method for detection of cardiac arrhythmias including atrial fibrillation.

OBJECTIVE

The aim of this work is to provide an overview of available data and indications of ILR in atrial fibrillation, especially after catheter ablation, in order to illustrate practice-oriented recommendations.

MATERIALS AND METHODS

We conducted a selective PubMed literature search.

RESULTS AND DISCUSSION

ILR can record asymptomatic/rare atrial fibrillation episodes and prevent thromboembolic events by allowing timely initiation of oral anticoagulation. They can be used to assess therapeutic success after percutaneous or surgical ablation, if despite increased thromboembolic risk, no oral anticoagulation is desired. ILR equipped with remote monitoring function and special P wave detection algorithms may improve diagnostic confidence.

The Evolution and Application of Cardiac Monitoring for Occult Atrial Fibrillation in Cryptogenic Stroke and TIA

OPINION STATEMENT

The evaluation of the stroke and transient ischemic attack (TIA) patient has been historically predominated by the initial evaluation in the hospital setting. As the etiology of stroke has eluded us in approximately one third of all acute events, the medical community has been eager to seek the answer to this mystery. In recent years, we have seen an explosion of innovations and trends allowing for a more detailed post stroke assessment strategy aimed at the identification of occult atrial fibrillation as the etiologic cause for the cryptogenic event. This has been achieved through the evolution and aggressive application and study of prolonged and advanced cardiac monitoring. This review is aimed to clarify and elucidate the standard and novel cardiac monitoring methods that have become available for use by the medical community and expected in the higher level care of cryptogenic stroke and TIA patients. These cardiac monitoring methods and devices are as heterogeneous as our patient population and have their own advantages and disadvantages. Many factors may be taken into consideration in choosing the appropriate cardiac monitoring method and are highlighted for consideration in this review. With a judicious approach to investigating the cryptogenic stroke population, and applying a wealth of novel treatment options, we may move forward into a new era of stroke prevention.

Photoplethysmography-Based Method for Automatic Detection of Premature Ventricular Contractions

This work introduces a method for detection of premature ventricular contractions (PVCs) in photoplethysmogram (PPG). The method relies on 6 features, characterising PPG pulse power, and peak-to-peak intervals. A sliding window approach is applied to extract the features, which are later normalized with respect to an estimated heart rate. Artificial neural network with either linear and non-linear outputs was investigated as a feature classifier. PhysioNet databases, namely, the MIMIC II and the MIMIC, were used for training and testing, respectively. After annotating the PPGs with respect to synchronously recorded electrocardiogram, two main types of PVCs were distinguished: with and without the observable PPG pulse. The obtained sensitivity and specificity values for both considered PVC types were 92.4/99.9% and 93.2/99.9%, respectively. The achieved high classification results form a basis for a reliable PVC detection using a less obtrusive approach than the electrocardiography-based detection methods.

Early detection of occult atrial fibrillation and stroke prevention

Atrial fibrillation (AF) is a very common arrhythmia and significantly increases stroke risk. This risk can be mitigated with oral anticoagulation, but AF is often asymptomatic, or occult, preventing timely detection and treatment. Accordingly, occult AF may cause stroke before it is clinically diagnosed. Currently, guidelines for the early detection and treatment of occult AF are limited. This review addresses recent advancements in occult AF detection methods, identification of populations at high risk for occult AF, the treatment of occult AF with oral anticoagulation, as well as ongoing trials that may answer critically important questions regarding occult AF screening.

Cost Analysis and Clinical Outcomes of Ambulatory Care Monitoring in Medicare Patients: Describing the Diagnostic Odyssey

Objectives: The diagnostic sequence and costs for arrhythmia detection utilizing Holter ambulatory ECG monitoring have not been well studied. The objective of the current study was to characterize the number of patients and associated costs incurred in the diagnosis, additional monitoring, clinical events and sequelae after an initial Holter monitor in Medicare patients with arrhythmia-the diagnostic odyssey. Methods: We performed a retrospective, longitudinal claims analysis using a 5% random sample of Medicare beneficiaries' claims from the Fee-for-Service (FFS) Standard Analytic Files. The analysis was limited to patients with full benefits for 1 year prior and 2 years post the index 24- or 48-hour Holter event, no prior arrhythmia or Holter. Results: The group of greatest interest was the "No results" category, since these 1,976 patients (11.1% of the total 17,887 patients evaluated) reflected the failure of repeat Holter monitoring to either detect clinical events or diagnose disease. In spite of this failure, there was a total allowed charge of more than $45 million or slightly more than $23,000 per involved patient. When extrapolated over the entire Medicare FFS population, this category was estimated to cost more than $900 million over the 2-year study period. Conclusions: Additional diagnostic paradigms need to be explored to improve upon these patient and system outcomes, where repeat monitoring frequently did not yield a diagnosis and patients continued to experience clinical events.

Arrhythmia Burden in Elderly Patients With Severe Aortic Stenosis as Determined by Continuous Electrocardiographic Recording

Background—

This study sought to evaluate the prevalence of previously undiagnosed arrhythmias in candidates for transcatheter aortic valve replacement (TAVR) and to determine the impact on therapy changes and arrhythmic events after the procedure.

Methods and Results—

A total of 435 candidates for TAVR underwent 24-hour continuous ECG monitoring the day before the procedure. Newly diagnosed arrhythmias were observed in 70 patients (16.1%) before TAVR: paroxysmal atrial fibrillation (AF)/atrial tachycardia (AT) in 28, advanced atrioventricular block or severe bradycardia in 24, nonsustained ventricular tachycardia in 26, and intermittent left bundle-branch block in 3 patients. All arrhythmic events but one were asymptomatic and led to a therapy change in 43% of patients. In patients without known AF/AT, the occurrence of AF/AT during 24-hour ECG recording was associated with a higher rate of 30-day cerebrovascular events (7.1% versus 0.4%; P =0.030). Among the 53 patients with new-onset AF/AT after TAVR, 30.2% had newly diagnosed paroxysmal AF/AT before the procedure. In patients who needed permanent pacemaker implantation after the procedure (n=35), 31.4% had newly diagnosed advanced atrioventricular block or severe bradycardia before TAVR. New-onset persistent left bundle-branch block after TAVR occurred in 37 patients, 8.1% of whom had intermittent left bundle-branch block before the procedure.

Conclusions—

Newly diagnosed arrhythmias were observed in approximately a fifth of TAVR candidates, led to a higher rate of cerebrovascular events, and accounted for a third of arrhythmic events after the procedure. This high arrhythmia burden highlights the importance of an early diagnosis of arrhythmic events in such patients to implement the appropriate therapeutic measures earlier.

Cryptogenic Stroke And Role Of Loop Recorder

Ischemic stroke is an important cause of morbidity and mortality when untreated. Identifying atrial fibrillation is important because atrial fibrillation ischemic related strokes are associated with an increased risk of disability and death compared with strokes of other etiologies and tend to recur without anticoagulation. However, atrial fibrillation detection can be difficult when it is asymptomatic and paroxistic and may be the underlying cause of some cryptogenic strokes or strokes of unknown origin. In this review, the different methods of cardiac monitoring to detect atrial fibrillation in patients with cryptogenic stroke are summarized, with a focus on loop recorder monitoring.

Vagus-preserving technique during minimally invasive esophagectomy: the effects on cardiac parameters in a swine model

BACKGROUND

Cardiac complications are an important cause of morbidity and mortality observed after esophageal resections. We examined whether an high intrathoracic vagotomy during abdominothoracic esophagectomy would have an effect on intraoperative and early postoperative cardiac function in the setting of a minimally invasive resection. Two hypotheses were generated for this study: (1) Vagotomy would cause cardiac changes, and (2) vagus-preserving esophagectomy would prevent cardiac problems during resection and in the early postoperative phase.

METHODS AND RESULTS

Thirty male pigs were operated on while cardiac parameters (heart rate [HR], cardiac index [CI], preload recruitable stroke work [PRSW], contractility speed [dp/dtmax], relaxation speed [dp/dtmin], and relaxation time [tau]) were monitored using a conductance catheter and the thermodilution method. Animals were randomized into 4 groups (each n = 7): (1) control, thoracoscopy only, (2) thoracoscopy with vagotomy, (3) esophageal resection with vagotomy, and (4) esophageal resection with vagus nerve preservation. To evaluate the first hypothesis, we compared groups 1 and 2; to evaluate the second hypothesis, we compared groups 3 and 4. HR, CI, PRSW, dp/dtmax, and tau were different in the 2 groups without resection (area under the curve; each P < .05). Vagotomy with esophagectomy resulted in nonsignificant differences between groups 3 and 4. The requirement for metoprolol administration to avoid severe tachycardia was greater in the groups that underwent vagotomy (P < .05; Fisher's exact test).

CONCLUSION

An high intrathoracic vagotomy results in loss of vagal tone and a greater rate of tachycardia during thoracoscopy and esophagectomy. There were no differences, however, in cardiac dynamics between the esophagectomy groups. Thus, vagal injury is not the sole reason for cardiac dysfunction after esophagectomy.