Atrial Signal Research Articles

An improved deep learning approach based on exponential moving average algorithm for atrial fibrillation signals identification

Atrial fibrillation (AF) is one of the leading causes of heart diseases worldwide. An accurate AF detection method for timely treatment is attracting great attention by widespread scientific and clinical research in recent years. However, routine AF detection using the visual examination of electrocardiogram data is strenuous and relatively subjective. In this study, an improved Elman neural network (IENN) model is specially designed for automated AF signals identification. Inspired by the exponential moving average (EMA) algorithm, a weight strategy is installed into the existing ENN network that provides more effective and comprehensive discrimination ability to the model for ECG signals recognition. Besides, our IENN network is embedded into a convolutional network architecture and the existing multi-layer perceptron and ENN networks are considered as control subjects to evaluate the validity on two public databases. The results exhibit that the proposed model yields the accuracy, specificity and sensitivity of 97.9%, 97.8% and 98.0% on the MIT-BIH AF database and 97.1%, 97.1% and 97.2% on the MIT-BIH arrhythmia database, respectively. These superior performances enable the proposed IENN model to have considerable potential as an effective tool to aid cardiologists in detecting AF signals accurately.

Inflammatory signalling in atrial cardiomyocytes: a novel unifying principle in atrial fibrillation pathophysiology.

Inflammation has been implicated in atrial fibrillation (AF), a very common and clinically significant cardiac rhythm disturbance, but its precise role remains poorly understood. Work performed over the past 5 years suggests that atrial cardiomyocytes have inflammatory signalling machinery — in particular, components of the NLRP3 (NACHT-, LRR- and pyrin domain-containing 3) inflammasome — that is activated in animal models and patients with AF. Furthermore, work in animal models suggests that NLRP3 inflammasome activation in atrial cardiomyocytes might be a sufficient and necessary condition for AF occurrence. In this Review, we evaluate the evidence for the role and pathophysiological significance of cardiomyocyte NLRP3 signalling in AF. We first summarize the evidence for a role of inflammation in AF and review the biochemical properties of the NLRP3 inflammasome, as defined primarily in studies of classic inflammation. We then briefly consider the broader evidence for a role of inflammatory signalling in heart disease, particularly conditions that predispose individuals to develop AF. We provide a detailed discussion of the available information about atrial cardiomyocyte NLRP3 inflammasome signalling in AF and related conditions and evaluate the possibility that similar signalling might be important in non-myocyte cardiac cells. We then review the evidence on the role of active resolution of inflammation and its potential importance in suppressing AF-related inflammatory signalling. Finally, we consider the therapeutic potential and broader implications of this new knowledge and highlight crucial questions to be addressed in future research.

Correlation between conduction velocity and frequency analysis in patients with atrial fibrillation using high-density charge mapping.

Spectral analysis of atrial signals has been used to identify regions of interest in atrial fibrillation (AF). However, the relationship to the atrial substrate is unclear. In this study, we compare regions with dominant frequency (DF), simultaneously determined in the left atrium (LA) by a novel noncontact mapping system using unipolar charge density signals, to the zones of slow conduction (SZ) during AF.In 19 AF patients the conduction during AF was assessed by a validated algorithm and SZ compared to the DF and the DF ratio between the DF peak and the area under the total spectrum (DFR). The results were compared in five different regions of the LA. The reproducibility of SZ location at different time measurements was higher than for DF or DFR. The SZs are mainly confined at the anterior and posterior wall of the LA. There was no statistically significant correlation between SZ and DF or DFR across the atrium.

Inhibition of adenylyl cyclase 1 by ST034307 inhibits IP3-evoked changes in sino-atrial node beat rate.

Atrial arrhythmias, such as atrial fibrillation (AF), are a major mortality risk and a leading cause of stroke. The IP3 signalling pathway has been proposed as an atrial-specific target for AF therapy, and atrial IP3 signalling has been linked to the activation of calcium sensitive adenylyl cyclases AC1 and AC8. We investigated the involvement of AC1 in the response of intact mouse atrial tissue and isolated guinea pig atrial and sino-atrial node (SAN) cells to the α-adrenoceptor agonist phenylephrine (PE) using the selective AC1 inhibitor ST034307. The maximum rate change of spontaneously beating mouse right atrial tissue exposed to PE was reduced from 14.5% to 8.2% (p = 0.005) in the presence of 1 μM ST034307, whereas the increase in tension generated in paced left atrial tissue in the presence of PE was not inhibited by ST034307 (Control = 14.2%, ST034307 = 16.3%; p > 0.05). Experiments were performed using isolated guinea pig atrial and SAN cells loaded with Fluo-5F-AM to record changes in calcium transients (CaT) generated by 10 μM PE in the presence and absence of 1 μM ST034307. ST034307 significantly reduced the beating rate of SAN cells (0.34-fold decrease; p = 0.003) but did not inhibit changes in CaT amplitude in response to PE in atrial cells. The results presented here demonstrate pharmacologically the involvement of AC1 in the downstream response of atrial pacemaker activity to α-adrenoreceptor stimulation and IP3R calcium release.

A deep learning refinement strategy based on efficient channel attention for atrial fibrillation and atrial flutter signals identification

Atrial fibrillation (AF) and atrial flutter (AFL) are the most frequent arrhythmias recently. However, given both similar physiological features, visually evaluating electrocardiogram as the most traditional diagnosis scheme is taxing and error-prone. In this work, we specifically design two network modules based on bidirectional long short term memory (BiLSTM) and gate recurrent unit (BiGRU) for automatic AF and AFL detection. Motivated from Efficient Channel Attention network, we aim to reformulate BiLSTM and BiGRU with a feature recalibration approach that enables the model to adaptively focus more on the relevant feature representations and suppress irrelevant parts while appropriately capturing cross-channel interaction for learning effective channel attention. The results lead to consistent performance gains than several published researches with an accuracy of 99.2% and 99.3% across the two publicly available data sets while demonstrating the effectiveness of both modules. In particular, various derivative gradient values of sample ECG segments are visualized to improve interpretability. To our knowledge, this work offers the first empirical investigation of existing BiLSTM and BiGRU refinements for a better performance gain, showing great potential for many computer vision tasks.

Icariin attenuates excessive alcohol consumption-induced susceptibility to atrial fibrillation through SIRT3 signaling

Excessive alcohol consumption has long been identified as a risk factor for adverse atrial remodeling and atrial fibrillation (AF). Icariin is a principal active component from traditional Chinese medicine Herba Epimedii and has been demonstrated to exert potential antiarrhythmic effect. The present study was designed to evaluate the effect of icariin against alcohol-induced atrial remodeling and disruption of mitochondrial dynamics and furthermore, to elucidate the underlying mechanisms. Excessive alcohol-treated C57BL/6 J mice were infected with serotype 9 adeno-associated virus (AAV9) carrying mouse SIRT3 gene or negative control virus. Meanwhile, icariin (50 mg/kg/d) was administered to the animals in the presence or absence of AAV9 carrying SIRT3 shRNA. We noted that 8 weeks of icariin treatment effectively attenuated alcohol consumption-induced atrial structural and electrical remodeling as evidenced by reduced AF inducibility and reversed atrial electrical conduction pattern as well as atrial enlargement. Furthermore, icariin-treated group exhibited significantly enhanced atrial SIRT3-AMPK signaling, decreased atrial mitoSOX fluorescence and mitochondrial fission markers, elevated mitochondrial fusion markers (MFN1, MFN2) as well as NRF-1-Tfam-mediated mitochondrial biogenesis. Importantly, these beneficial effects were mimicked by SIRT3 overexpression while abolished by SIRT3 knockdown. These data revealed that targeting atrial SIRT3-AMPK signaling and preserving mitochondrial dynamics might serve as the novel therapeutic strategy against alcohol-induced AF genesis. Additionally, icariin ameliorated atrial remodeling and mitochondrial dysfunction by activating SIRT3-AMPK signaling, highlighting the use of icariin as a promising antiarrhythmic agent in this circumstance.

Real-world experience on implantation and atrial signal detection of a SC ICD with atrial sensing capability: The MATRIX study

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): BIOTRONIK, Berlin, Germany Introduction A single-lead implantable cardioverter-defibrillator (ICD) with atrial sensing dipole (the DX ICD system) can potentially give additive information concerning atrial diagnostics in patients requiring only a single-chamber ICD. We therefore report the real-world experience from large DX registry on implantation, atrial signal quality and detection and the long-term stability of the atrial signal. Methods The prospective, single-arm MATRIX (Management and Detection of Atrial Tachyarrhythmias in Patients Implanted With BIOTRONIK DX Systems, NCT01774357) registry study effectively enrolled 2041 patients at 119 sites in 24 countries. All patients had a DX system implanted for a single-chamber ICD indication for primary or secondary prevention of sudden cardiac death. Patients were followed for 24 months including remote monitoring. Implantation and follow-up data are reported for the whole patient set. For the analyses on atrial sensing amplitude values, remotely transmitted device measurements of patients without history of long-standing persistent or permanent AF at baseline were used. Results The mean follow-up period was 677±173 days. Implantation took place at 15±22 days before enrollment. Baseline and implantation data are shown in the table. Implantation procedure and lead insertion were rated as "easy" or "very easy" in 91.0% and 96.3% of assessments, respectively. At implantation, the investigators rated the quality of the atrial sensing amplitude as "sufficient" in 97% of the assessed cases. At enrollment (12-month/24-month follow-up), the atrial signal quality and detection were rated as "good" or "excellent" in 92.3% (89.8%/89.9%) and 92.4% (90.1%/91.3%) of assessments, respectively. For 1841 patients (90.2%), remotely transmitted device information was received. The median (mean ± SD, IQR) transmission rate was 92.5% (85.4±18.2%, 81.4-97.3%). 1746 patients (85.5%) matched the inclusion criteria for the quantitative analyses on atrial sensing. 95.6% of available RA sensing amplitude values were ≥1 mV. Based on each patient’s overall median value, the median (mean ± SD, IQR) RA sensing amplitude was 4.6 mV (4.4±2.0 mV, 2.8-6.2 mV). The time course of patient median values stratified by month is shown in the figure. Conclusion The study followed 2041 patients implanted with the DX ICD system for two years. In the vast majority of cases, investigators rated implantation as (very) easy and the atrial signal over 24 months as good/excellent. According to daily, automatic Home Monitoring data, the overall mean P-wave amplitude remained stable throughout the whole follow-up. The MATRIX study demonstrated functionality and clinical utility of the DX concept in an unselected, real-life setting.

Amplitude of fibrillatory wave correlates with long-term maintenance of sinus rhythm after ablation in persistent atrial fibrillation

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Commission for Technology and Innovation (CTI), Switzerland Background Amplitude of fibrillatory wave (fWA) on surface ECG is regarded as a predictor of ablation outcome for atrial fibrillation (AF). Purpose We sought to investigate whether fWA and its changes during ablation predicts long-term maintenance of sinus rhythm (SR) after wide circumferential isolation of pulmonary veins (WPVI) in persistent AF (peAF). Methods 41 patients (63±10 y, sustained AF 11±7 months) underwent a de-novo WPVI. A second WPVI was performed in patients with recurrent AF in order to provide complete PV disconnection. We defined "success" as patients who remained in SR after one or two procedures, and "failure" otherwise. 60-sec ECG signals devoid of QRST waves were recorded during the index ablation at baseline and at the end of ablation (end_WPVI, before cardioversion or conversion of AF into SR). fWA was computed on leads V1 and V6b (placed on the pts’ back) as the average difference between the upper and lower envelope of atrial ECG signals. Results Over a mean follow-up of 33±9 months, 30 patients remained free from AF (success group), while 11 patients had AF recurrence after 2 WPVIs (failure group). The clinical characteristics (e.g. age, body mass index, left atrial volume or duration in sustained AF) were similar between groups (p > 0.05). The success group displayed significantly higher fWA values at baseline and end_WPVI than that of the failure group (p<0.05, Panel A and B). No significant difference was found between baseline fWA values and those measured at end_WPVI (p > 0.05). Conclusion As fWA is independent of PVs contribution, it is a marker of atrial body remodelling. Low fWA values identify patients with peAF unresponsive to WPVI.

Early experience with the second generation of leadless pacemakers and correlation with ecg parameters

Abstract Funding Acknowledgements Type of funding sources: None. Background Leadless pacing has evolved as a safe and effective treatment option in selected patients. With the updated generation that allows sensing of atrial contraction, atrioventricular synchronized pacing is now possible in a VDD mode. Previous retrospective analyses have demonstrated that echocardiographic parameters may be helpful in selecting patients with a higher chance of good atrioventricular synchronous pacing behaviour. Purpose Analysis of the early experience with the second generation of leadless pacemaker and the role of ECG parameters to predict a good atrial contraction signal (so-called A4 amplitude) in patients who underwent leadless pacemaker implantation in four tertiary centres. Methods and Results In this retrospective analysis, a total of 136 patients were included. Mean age was 78.0 (64.7 - 84.2 years) years with 48.9 % being male. Coronary artery disease was the leading underlying heart disease with 27.1 % affected patients. 61.7 % of the population suffered from sinus rhythm with complete or intermittent atrioventricular block. The majority of devices were implanted at the mid-septal (61.2 %) or high-septal (25.6 %) right ventricle, respectively. Electrical parameters were optimal at implant (Table 1) and remained stable over time (Table 1). In addition, A4 signal amplitude remained stable too during follow-up compared to the value early after implantation (Table 1). From this entire cohort, patients with an ECG available at implant and those in which the device was working predominantly in the VDD mode were selected for further analyses (62 patients). PR interval measured from the ECG prior to implantation did not correlate with the A4 signal amplitude (Figure 1A; P = NS). Next, P wave amplitudes were measured in all 12 ECG leads. There was a correlation between P wave amplitude from lead V2 with the A4 amplitude (Figure 1B; P = 0.034, R2 = 0.09), whereas the other right-sided ECG leads (V1/aVR), either alone or in combination, did not correlate with the A4 signal amplitude (P = NS). Conclusions In our cohort of patients with the second generation of leadless pacemakers, offering VDD pacing, good electrical parameters can be achieved as it has been observed with the first generation. Also the A4 signal amplitude as a marker for atrial contraction remains stable over time. In regard to ECG parameters measured prior to device implantation, only the P wave amplitude in lead V2 correlated with a amplitude of the A4 signal.

PO-672-08 SINGLE LEAD IMPLANTABLE CARDIOVERTER-DEFIBRILLATOR SYSTEM WITH A FLOATING ATRIAL SENSING DIPOLE: LONG TERM FOLLOW UP WITH LIMITATIONS OF THE SMART ALGORITHM

Biotronik Dx ICD system is a single lead pacing system with ability to sense atrial signals via floating atrial dipole, developed to provide advantages of atrial electrograms without risks & cost of additional atrial lead using SMART algorithm. We herein present long term follow up data of the largest single centre experience with Dx ICD system.

Blind Source Separation in Persistent Atrial Fibrillation Electrocardiograms Using Block-Term Tensor Decomposition With Löwner Constraints.

The estimation of the atrial activity (AA) signal from electrocardiogram (ECG) recordings is an important step in the noninvasive analysis of atrial fibrillation (AF), the most common sustained arrhythmia encountered in clinical practice. This problem admits a blind source separation (BSS) formulation that has been recently posed as a tensor factorization, using the Hankel-based block term decomposition (BTD), which is particularly well-suited to the estimation of exponential models like AA during AF. However, persistent forms of AF are characterized by short R-R intervals and very disorganized (or weak) AA, making it difficult to model AA directly and perform its successful extraction through Hankel-BTD. To overcome this drawback, the present work proposes a tensor approach to estimate QRS complexes and subtract them from the ECG, resulting in a signal that, ideally, only contains the AA component. Such an approach tackles the problem of blind separation of rational functions, which models QRS complexes explicitly. The data tensor admitting a BTD is built from Löwner matrices generated from each lead of the observed ECG. To this end, this paper formulates a variant of the recently proposed constrained alternating group lasso (CAGL) algorithm that imposes Löwner structure on the decomposition blocks. This is done by performing an orthogonal projection, which we explicitly derive, at each iteration of CAGL. Results from experiments with synthetic data show the consistency of the proposed Löwner-constrained AGL (LCAGL) in extracting the desired sources. Experimental results obtained on a population of 20 patients suffering from persistent AF show that the proposed variant outperforms other tensor-based methods in terms of atrial signal estimation quality from ECG records as short as a single heartbeat.

A novel bidirectional LSTM network based on scale factor for atrial fibrillation signals classification

Atrial fibrillation (AF) is a common arrhythmia worldwide. The visual examination for electrocardiogram is the major diagnosis method, which is generally burdensome and inefficient. In this work, an improved bidirectional long short term memory (IB-LSTM) frame is specially designed for intelligence-based AF signals classification. Based on the existing B-LSTM architecture, a scale factor is installed into IB-LSTM network that enables the model system to effectively reallocate information representation and therefore learn better representation. After several pre-processing steps such as denoising, these results demonstrate consistent performance improvements with the accuracy of 98.2% and 97.5% against multiple existing approaches and lower computation costs than B-LSTM with the two public MIT-BIH AF and arrhythmia databases. The proposed IB-LSTM network is capable of trading off between model accuracy and computing resources. In particular, this research provides the first empirical exploration of redesign architecture of B-LSTM to alleviate high computation cost and information redundancy, which shows great prospects as efficient auxiliary tools for medical diagnosis.

STRANGE DUAL CHAMBER STIMULATION

Implantable cardiac devices (pacemaker, ICD, resynchronisation) are increasingly indicated in recent years. Complications secondary to these implants are encountered more frequently.We report the case of a 60 year old patient with a double chamber pacing device, implanted 6 months before her consultation date for a complete AVB.She was referred by her attending physician for exertional dyspnea and lipothymic discomfort following the implantation of the device.Clinical examination revealed no abnormalities. The ECG showed a spontaneous sinus rhythm at 60cpm.The interrogation of the device showed a DDDR pacing mode with the most frequent pacing mode in ASVS found at 80%.The EGMs listed were not consistent with the programmed dual chamber mode. The atrial endocavitary signals are labelled VS and the ventricular ones AP. The change of mode to VVI which should generate a ventricular drive showed us a spontaneous atrial signal coded VS. The AAI mode produces ventricular pacing with AP coded ventriculograms.A reversal of the lead connection at the box level was evoked.The patient has undergone a correct reconnection of the leads to the box with a rhythm at the end of the procedure in DDD.Follow-up telemetry 1 month after reconnection showed atrial and ventricular EGM signals appropriate to the paced and detected chamber.

Atrial fibrillatory rate as predictor of recurrence of atrial fibrillation in horses treated medically or with electrical cardioversion.

The recurrence rate of atrial fibrillation (AF) in horses after cardioversion to sinus rhythm (SR) is relatively high. Atrial fibrillatory rate (AFR) derived from surface ECG is considered a biomarker for electrical remodelling and could potentially be used for the prediction of successful AF cardioversion and AF recurrence. Evaluate if AFR was associated with successful treatment and could predict AF recurrence in horses. Retrospective multicentre study. Electrocardiograms (ECG) from horses with persistent AF admitted for cardioversion with either medical treatment (quinidine) or transvenous electrical cardioversion (TVEC) were included. Bipolar surface ECG recordings were analysed by spatiotemporal cancellation of QRST complexes and calculation of AFR from the remaining atrial signal. Kaplan-Meier survival curve and Cox regression analyses were performed to assess the relationship between AFR and the risk of AF recurrence. Of the 195 horses included, 74 received quinidine treatment and 121 were treated with TVEC. Ten horses did not cardiovert to SR after quinidine treatment and AFR was higher in these, compared with the horses that successfully cardioverted to SR (median [interquartile range]), (383 [367-422] vs 351 [332-389] fibrillations per minute (fpm), P<.01). Within the first 180days following AF cardioversion, 12% of the quinidine and 34% of TVEC horses had AF recurrence. For the horses successfully cardioverted with TVEC, AFR above 380 fpm was significantly associated with AF recurrence (hazard ratio=2.4, 95% confidence interval 1.2-4.8, P=.01). The treatment groups were different and not randomly allocated, therefore the two treatments cannot be compared. Medical records and the follow-up strategy varied between the centres. High AFR is associated with failure of quinidine cardioversion and AF recurrence after successful TVEC. As a noninvasive marker that can be retrieved from surface ECG, AFR can be clinically useful in predicting the probability of responding to quinidine treatment as well as maintaining SR after electrical cardioversion.

A Classification Scheme for ECG Signals Based on Bidirectional LSTM Model

The application of ECG to diagnose cardiovascular diseases is a common method in clinical medicine, so the use of deep learning tools to achieve automatic analysis and classification of ECG has been a research direction for a wide range of researchers. This paper proposes a classification model for ECG signals based on a bidirectional LSTM model which is trained and tested using the dataset used for the PhysioNet 2017 computational cardiology challenge. The data are normalized and then processed by feature extraction. After passing a bidirectional LSTM layer, a fully connected layer, a softmax layer, and a classification layer in the model, and finally achieve the binary classification of normal signals and atrial fibrillation signals. In this process, the feature of bidirectional LSTM that can integrate contextual information is fully utilized. The experiments show that the classification accuracy of the model reaches 94.1%, demonstrating a good classification result.

A Novel Bidirectional LSTM Network Based on Scale Factor for Atrial Fibrillation Signals Classification

A Novel Bidirectional LSTM Network Based on Scale Factor for Atrial Fibrillation Signals Classification

Design and implementation of FPGA-based arrhythmic ECG signals using VHDL for biomedical calibration applications

Biomedical applications are one of the important research areas of recent years. One of these fields of study is biomedical signals. In this study, the Normal Sinus Rhythm and three arrhythmic ECG signals (Ventricular Tachycardia, Ventricular Paced and Atrial Flutter), one of the vital sign signals, were designed and implemented to work on FPGA chips using the Xilinx-Vivado program with VHDL. Matlab-based ECG signals were taken as a reference and compared with the results obtained from the FPGA-based ECG signals design. Then, the structure used in the design and the test results obtained from the study have been presented. The designed ECG signals were synthesized for the Zynq-7000 XC7Z020 FPGA and observed from the oscilloscope using the 14-channel AN9767 DA module. FPGA chip resource consumption values obtained after the Place-Route process are presented. According to the results, the maximum operating frequency of Normal Sinus Rhythm and Ventricular tachycardia signals on the FPGA was 657.614 MHz and the maximum operating frequency of the Ventricular Paced and Atrial Flutter signals on the FPGA was 651.827 MHz. The maximum MSE value obtained from FPGA-based ECG signal design is 1.2319E-02. In this study, it has been shown that the FPGA-based ECG signal generation system, which is implemented as hardware, can be designed using FPGA chips and can be safely used in biomedical calibration applications. Other arrhythmic ECG signals can be designed and implemented using similar methods in future studies.

ECG classification using multifractal detrended moving average cross-correlation analysis

Accurate detection of arrhythmia signal types is of great significance for the early detection of heart disease and its subsequent treatment. The primary purpose of this study is to explore an electrocardiogram (ECG) classification system to improve its performance and achieve excellent computing performance, especially for large sample datasets. We classified ECG signals using the Hurst exponent, which is an ECG feature extracted by multifractal detrended moving average cross-correlation analysis (MF-XDMA). In addition, we used multifractal methods such as multifractal detrended fluctuation analysis (MF-DFA), multifractal detrended cross-correlation analysis (MF-DCCA) and multifractal detrended moving average (MF-DMA) to extract the features of ECG signals, and we used a support vector machine (SVM) to classify the four types of feature data. The experimental results show that MF-XDMA-SVM has the best classification performance for atrial premature beat (APB) and bigeminy signals, which indicates that MF-XDMA-SVM is the most effective for the extraction of ECG signal sequence features among the four multifractal models.

Quantification of left atrial fibrosis by 3D late gadolinium-enhanced cardiac magnetic resonance imaging in patients with atrial fibrillation: impact of different analysis methods.

AimsVarious methods and post-processing software packages have been developed to quantify left atrial (LA) fibrosis using 3D late gadolinium-enhancement cardiac magnetic resonance (LGE-CMR) images. Currently, it remains unclear how the results of these methods and software packages interrelate.Methods and resultsForty-seven atrial fibrillation (AF) patients underwent 3D-LGE-CMR imaging prior to their AF ablation. LA fibrotic burden was derived from the images using open-source CEMRG software and commercially available ADAS 3D-LA software. Both packages were used to calculate fibrosis based on the image intensity ratio (IIR)-method. Additionally, CEMRG was used to quantify LA fibrosis using three standard deviations (3SD) above the mean blood pool signal intensity. Intraclass correlation coefficients were calculated to compare LA fibrosis quantification methods and different post-processing software outputs. The percentage of LA fibrosis assessed using IIR threshold 1.2 was significantly different from the 3SD-method (29.80 ± 14.15% vs. 8.43 ± 5.42%; P < 0.001). Correlation between the IIR-and SD-method was good (r = 0.85, P < 0.001) although agreement was poor [intraclass correlation coefficient (ICC) = 0.19; P < 0.001]. One-third of the patients were allocated to a different fibrosis category dependent on the used quantification method. Fibrosis assessment using CEMRG and ADAS 3D-LA showed good agreement for the IIR-method (ICC = 0.93; P < 0.001).ConclusionsBoth, the IIR1.2 and 3SD-method quantify atrial fibrotic burden based on atrial wall signal intensity differences. The discrepancy in the amount of LA fibrosis between these methods may have clinical implications when patients are classified according to their fibrotic burden. There was no difference in results between post-processing software packages to quantify LA fibrosis if an identical quantification method including the threshold was used.

Enhanced detection of atrial tachyarrhythmias with pacing devices by using more accurate atrial sensing

PurposeCardiac pacing devices can detect and monitor atrial tachyarrhythmias (ATA) which increase the risk of thromboembolic complications. The aim of this study was to compare (1) two different atrial leads and (2) standard and optimized settings to detect ATA and reject far-field R-wave signal (FFRW).MethodsThis was a prospective, randomized multi-center trial comparing St. Jude Medical OptiSense lead (tip-to-ring spacing 1.1 mm) and Tendril lead (tip-to-ring spacing 10.0 mm), having programmed atrial sensitivity at 0.2 mV and post-ventricular atrial blanking at 60 ms. We measured intra-atrial amplitudes of FFRW, intrinsic atrial signals, the amount of FFRW oversensing, and other inappropriate mode switching.ResultsOne hundred and ten patients were enrolled. The mean amplitude of sensed and paced FFRW bipolar signal was 0.13 mV vs. 0.21 mV (p < 0.001) and 0.13 mV vs. 0.26 mV (p < 0.001) with OptiSense and Tendril lead, respectively. The mean amplitude of the atrial bipolar signal was 2.84 mV with OptiSense and 3.48 mV with Tendril lead, p = 0.014. With the optimized settings with OptiSense lead, one patient out of 20 (5%) had FFRW oversensing, none had undersensing of ATAs due to 2:1-blanking of atrial depolarizations, and the concordance of the ATAs by Holter and pacemaker memory was high (Spearman’s rank correlation coefficient = 0.90). In the Tendril group, 12 out of 25 patients (48%) had oversensing and 4 had atrial undersensing (p < 0.001).ConclusionsThe technique with an atrial lead with short tip-to-ring spacing combined with optimized pacemaker programming resulted in reliable and accurate atrial arrhythmia detection.Trial registrationClinicalTrials.gov number NCT01074749.