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

Signal quality assessment of a novel ecg electrode for motion artifact reduction

Background: The presence of noise is problematic in the analysis and interpretation of the ECG, especially in ambulatory monitoring. Restricting the analysis to high-quality signal segments only comes with the risk of excluding significant arrhythmia episodes. Therefore, the development of novel electrode technology, robust to noise, continues to be warranted. Methods: The signal quality of a novel wet ECG electrode (Piotrode) is assessed and compared to a commercially available, commonly used electrode (Ambu). The assessment involves indices of QRS detection and atrial fibrillation detection performance, as well as signal quality indices (ensemble standard deviation and time–frequency repeatability), computed from ECGs recorded simultaneously from 20 healthy subjects performing everyday activities. Results: The QRS detection performance using the Piotrode was considerably better than when using the Ambu, especially for running but also for lighter activities. The two signal quality indices demonstrated similar trends: the gap in quality became increasingly larger as the subjects became increasingly more active. Conclusions: The novel wet ECG electrode produces signals with less motion artifacts, thereby offering the potential to reduce the review burden, and accordingly the cost, associated with ambulatory monitoring

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

False Alarm Reduction in Atrial Fibrillation Screening

Early detection of AF is essential and emphasizes the significance of AF screening. However, AF detection in screening ECGs, usually recorded by handheld and portable devices, is limited because of their high susceptibility to noise. In this study, the feasibility of applying a machine learning-based quality control stage, inserted between the QRS detector and AF detector blocks, is investigated with the aim to improve AF detection. A convolutional neural network was trained to classify the detections into either true or false. False detections were excluded and an updated series of QRS complexes was fed to the AF detector. The results show that the convolutional neural network-based quality control reduces the number of false alarms by 24.8% at the cost of 1.9% decrease in sensitivity compared to AF detection without any quality control

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

Prediction of combination therapies based on topological modeling of the immune signaling network in multiple sclerosis

Signal transduction deregulation is a hallmark of many complex diseases, including Multiple Sclerosis (MS). Here, we performed ex vivo multiplexed phosphoproteomic assays upon perturbations with multiple drugs and ligands in primary immune cells from 169 MS patients and matched healthy controls. Patients were either untreated or treated with fingolimod, natalizumab, interferon-β, glatiramer acetate or the experimental therapy epigallocatechin gallate (EGCG). We generated for each donor a dynamic logic model by fitting a bespoke literature-derived network of MS-related pathways to the perturbation data. Analysis of the models uncovered features of healthy-, disease- and drug-specific signaling networks. We developed an approach based on network topology to identify deregulated interactions whose activity could be reverted to a “healthy-like” status by combination therapy. We predicted several combinations with approved MS drugs. Specifically, TAK1 kinase, involved in TGF-β, Toll-like receptor, B-cell receptor and response to inflammation pathways were found to be highly deregulated and co-druggable with all MS drugs studied. One of these predicted combinations, fingolimod with a TAK1 inhibitor, was validated in an animal model of MS. Our approach based on donor-specific signaling networks enables prediction of targets for combination therapy for MS and other complex diseases. Significance statement: Multiple Sclerosis (MS) is a major health problem, leading to significant disability and patient suffering. Although chronic activation of the immune system is a hallmark of the disease, its pathogenesis is poorly understood. Further, current treatments only ameliorate the disease and may produce severe side effects. Here, we applied a network-based modeling approach based on phosphoproteomic data upon perturbation with ligands and drugs of healthy donors and MS patients to create donor-specific models. The models uncover the differential activation in signaling wiring between healthy donors, untreated patients and those under different treatments. Further, based in the patient-specific networks, a new approach identifies drug combinations to revert signaling to a healthy-like state. One sentence summary: A new approach to predict combination therapies based on modeling signaling networks using phosphoproteomics from Multiple Sclerosis patients identifies deregulated pathways and new drug combinations

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