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

Detection of Non-Sustained Supraventricular Tachycardia in Atrial Fibrillation Screening

OBJECTIVE

Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT.

METHODS

The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable.

RESULTS

The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively.

CONCLUSION

The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.

ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions

The present article 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.

Systematic Screening for Atrial Fibrillation in Patients at Moderate-to-High Risk of Stroke - Potential to Increase the Atrial Fibrillation Detection Rate (SCAN-AF)

BACKGROUND

To determine the rate of undiagnosed atrial fibrillation (AF) we screened for AF using an oscillometric blood pressure (BP) monitor device followed by a single-lead handheld electrocardiogram (ECG), with confirmation by 12-lead ECG as the reference standard.

METHODS AND RESULTS

From October 2017 to August 2019, 1,148 patients were enrolled without known AF, who were aged ≥65 years with moderate-to-high stroke risk, at 71 centers in Japan. After exclusion of 7 patients with confirmed AF at the index visit, 1,141 patients were asked to use an oscillometric BP monitor twice daily for 2 weeks (max: 4 weeks) to detect an irregular pulse. The BP monitor detected an irregular pulse in 481 patients, of which 1 patient had confirmed AF. Thereafter, 480 patients were instructed to acquire ECGs twice daily for an additional 2 weeks (max: 4 weeks) using a single-lead handheld ECG device. The handheld ECG device detected irregular rhythm in 41 patients, of which 1 patient had confirmed AF. In total, undiagnosed AF was confirmed in 9 (0.8%) patients of the overall study cohort during the 24-week follow-up period.

CONCLUSIONS

Sequential use of a BP monitor and handheld ECG for 4 weeks is a practical strategy for identifying undiagnosed AF in Japanese people at heightened risk of stroke.

Prioritising electrocardiograms for manual review to improve the efficiency of atrial fibrillation screening

Screening for atrial fibrillation (AF) could reduce the incidence of stroke by identifying undiagnosed AF and prompting anticoagulation. However, screening may involve recording many electrocardiograms (ECGs) from each participant, several of which require manual review which is costly and time-consuming. The aim of this study was to investigate whether the number of ECG reviews could be reduced by using a model to prioritise ECGs for review, whilst still accurately diagnosing AF. A multiple logistic regression model was created to estimate the likelihood of an ECG exhibiting AF based on the mean RR-interval and variability in RR-intervals. It was trained on 1,428 manually labelled ECGs from 144 AF screening programme participants, and evaluated using 11,443 ECGs from 1,521 participants. When using the model to order ECGs for review, the number of reviews for AF participants was reduced by 74% since no further reviews are required after an AF ECG is identified; however, it did not impact the number of reviews in non-AF participants (the vast majority of participants), so the overall number of reviews was reduced by 3% with no missed AF diagnoses. When using the model to also exclude ECGs from review, the overall number of reviews was reduced by 28% with no missed AF diagnoses, and by 53% with only 4% of AF diagnoses missed. In conclusion, the workload can be reduced by using a model to prioritise ECGs for review. Ordering ECGs alone only provides only a moderate reduction in workload. The additional use of a threshold to exclude ECGs from review provides a much greater reduction in workload at the expense of some missed AF diagnoses. Clinical Relevance-This shows the potential benefit of using a model to prioritise electrocardiograms for review in order to reduce the manual workload of AF screening.

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.