Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation

Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Frequency Tracking of Atrial Fibrillation using Hidden Markov Models

A Hidden Markov Model (HMM) is used to improve the robustness to noise when tracking the atrial fibrillation (AF) frequency in the ECG. Each frequency interval corresponds to a state in the HMM. Following QRST cancellation, a sequence of observed states is obtained from the residual ECG, using the short time Fourier transform. Based on the observed state sequence, the Viterbi algorithm, which uses a state transition matrix, an observation matrix and an initial state vector, is employed to obtain the optimal state sequence. The state transition matrix incorporates knowledge of intrinsic AF characteristics, e.g., frequency variability, while the observation matrix incorporates knowledge of the frequency estimation method and SNRs. An evaluation is performed using simulated AF signals where noise obtained from ECG recordings have been added at different SNR. The results show that the use of HMM considerably reduces the average RMS error associated with the frequency tracking: at 5 dB SNR the RMS error drops from 1.2 Hz to 0.2 Hz

ECG modeling for simulation of arrhythmias in time-varying conditions

The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance

Human herpesvirus 6A and axonal injury before the clinical onset of multiple sclerosis

Recent research indicates that multiple sclerosis is preceded by a prodromal phase with elevated levels of serum neurofilament light chain (sNfL), a marker of axonal injury. The effect of environmental risk factors on the extent of axonal injury during this prodrome is unknown. Human herpesvirus 6A (HHV-6A) is associated with an increased risk of developing multiple sclerosis. The objective of this study was to determine if HHV-6A serostatus is associated with the level of sNfL in the multiple sclerosis prodrome, which would support a causative role of HHV-6A. A nested case-control study was performed by crosslinking multiple sclerosis registries with Swedish biobanks. Individuals with biobank samples collected before the clinical onset of multiple sclerosis were included as cases. Controls without multiple sclerosis were randomly selected, matched for biobank, sex, sampling date and age. Serostatus of HHV-6A and Epstein-Barr virus (EBV) was analysed with a bead-based multiplex assay. The concentration of sNfL was analysed with Single molecule array technology. The association between HHV-6A serology and sNfL was assessed by stratified t-tests and linear regressions, adjusted for EBV serostatus and sampling age. Within-pair ratios of HHV-6A seroreactivity and sNfL were calculated for each case and its matched control. To assess the temporal relationship between HHV-6A antibodies and sNfL, these ratios were plotted against the time to the clinical onset of multiple sclerosis and compared using locally estimated scatterplot smoothing regressions with 95% confidence intervals (CI). Samples from 519 matched case-control pairs were included. In cases, seropositivity of HHV-6A was significantly associated with the level of sNfL (+11%, 95% CI 0.2-24%, P = 0.045), and most pronounced in the younger half of the cases (+24%, 95% CI 6-45%, P = 0.007). No such associations were observed among the controls. Increasing seroreactivity against HHV-6A was detectable before the rise of sNfL (significant within-pair ratios from 13.6 years vs 6.6 years before the clinical onset of multiple sclerosis). In this study, we describe the association between HHV-6A antibodies and the degree of axonal injury in the multiple sclerosis prodrome. The findings indicate that elevated HHV-6A antibodies both precede and are associated with a higher degree of axonal injury, supporting the hypothesis that HHV-6A infection may contribute to multiple sclerosis development in a proportion of cases

Prediction of sinus rhythm maintenance following DC-cardioversion of persistent atrial fibrillation – the role of atrial cycle length

BACKGROUND: Atrial electrical remodeling has been shown to influence the outcome the outcome following cardioversion of atrial fibrillation (AF) in experimental studies. The aim of the present study was to find out whether a non-invasively measured atrial fibrillatory cycle length, alone or in combination with other non-invasive parameters, could predict sinus rhythm maintenance after cardioversion of AF. METHODS: Dominant atrial cycle length (DACL), a previously validated non-invasive index of atrial refractoriness, was measured from lead V1 and a unipolar oesophageal lead prior to cardioversion in 37 patients with persistent AF undergoing their first cardioversion. RESULTS: 32 patients were successfully cardioverted to sinus rhythm. The mean DACL in the 22 patients who suffered recurrence of AF within 6 weeks was 152 ± 15 ms (V1) and 147 ± 14 ms (oesophagus) compared to 155 ± 17 ms (V1) and 151 ± 18 ms (oesophagus) in those maintaining sinus rhythm (NS). Left atrial diameter was 48 ± 4 mm and 44 ± 7 mm respectively (NS). The optimal parameter predicting maintenance of sinus rhythm after 6 weeks appeared to be the ratio of the lowest dominant atrial cycle length (oesophageal lead or V1) to left atrial diameter. This ratio was significantly higher in patients remaining in sinus rhythm (3.4 ± 0.6 vs. 3.1 ± 0.4 ms/mm respectively, p = 0.04). CONCLUSION: In this study neither an index of atrial refractory period nor left atrial diameter alone were predictors of AF recurrence within the 6 weeks of follow-up. The ratio of the two (combining electrophysiological and anatomical measurements) only slightly improve the identification of patients at high risk of recurrence of persistent AF. Consequently, other ways to asses electrical remodeling and / or other variables besides electrical remodeling are involved in determining the outcome following cardioversion