Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review: Digital Biomarkers for AF in Surface ECGs

Aims: Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into these available classification methods by discussing previously used digital biomarkers and algorithms and make recommendations for future research. Methods: On the 14th of September 2020, the PubMed database was searched for articles focusing on algorithms for AF detection in ECGs using the MeSH terms Atrial Fibrillation, Electrocardiography and Algorithms. Articles which solely focused on differentiation of types of rhythm disorders or prediction of AF termination were excluded. Results: The search resulted in 451 articles, of which 130 remained after full-text screening. Not only did the amount of research on methods for AF detection increase over the past years, but a trend towards more complex classification methods is observed. Furthermore, three different types of features can be distinguished: atrial features, ventricular features, and signal features. Although AF is an atrial disease, only 22% of the described methods use atrial features. Conclusion: More and more studies focus on improving accuracy of classification methods for AF in ECGs. As a result, algorithms become increasingly complex and less well interpretable. Only a few studies focus on detecting atrial activity in the ECG. Developing innovative methods focusing on detection of atrial activity might provide accurate classifiers without compromising on transparency.Biomechanical EngineeringCircuits and System

An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions

Background: An increasing number of wearables are capable of measuring electrocardiograms (ECGs), which may help in early detection of atrial fibrillation (AF). Therefore, many studies focus on automated detection of AF in ECGs. A major obstacle is the required amount of manually labelled data. This study aimed to provide an efficient and reliable method to train a classifier for AF detection using large datasets of real-life ECGs. Method: Human-controlled semi-supervised learning was applied, consisting of two phases: the pre-training phase and the semi-automated training phase. During pre-training, an initial classifier was trained, which was used to predict the classes of new ECG segments in the semi-automated training phase. Based on the degree of certainty, segments were added to the training dataset automatically or after human validation. Thereafter, the classifier was retrained and this procedure was repeated. To test the model performance, a real-life telemetry dataset containing 3,846,564 30-s ECG segments of hospitalized patients (n = 476) and the CinC Challenge 2017 database were used. Results: After pre-training, the average F1-score on a hidden testing dataset was 89.0%. Furthermore, after the pre-training phase 68.0% of all segments in the hidden test set could be classified with an estimated probability of successful classification of 99%, providing an F1-score of 97.9% for these segments. During the semi-automated training phase, this F1-score showed little variation (97.3%–97.9% in the hidden test set), whilst the number of segments which could be automatically classified increased from 68.0% to 75.8% due to the enhanced training dataset. At the same time, the overall F1-score increased from 89.0% to 91.4%. Conclusions: Human-validated semi-supervised learning makes training a classifier more time efficient without compromising on accuracy, hence this method might be valuable in the automated detection of AF in real-life ECGs.Circuits and System

Gene-environment interaction with smoking for increased non-muscle-invasive bladder cancer tumor size

Background: Urinary bladder cancer (UBC) is one of few cancers with an established gene-environment interaction (GxE) with smoking. However, it is unknown whether the interaction with tobacco use is present non-muscle invasive bladder cancer (NMIBC) and characteristics of prognostic relevance. We aimed to investigate if smoking status and/or smoking intensity interact with the effect of discovered variants on key NMIBC characteristics of tumor grade, stage, size, and patient age within the Bladder Cancer Prognosis Programme (BCPP) cohort.Methods: Analyzed sample consisted of 546 NMIBC patients with valid smoking data from the BCPP. In a previous genome-wide association study (GWAS), we have identified 61 single nucleotide polymorphisms (SNPs) potentially associated with the NMIBC characteristics of tumor stage, grade, size, and patient age. In the current analysis, we have tested these SNPs for GxE with smoking.Results: Out of 61 SNPs, 10 have showed suggestion (statistical significance level of P<0.05) for GxE with NMIBC tumor size rs35225990, rs188958632, rs180910528, rs74603364, rs187040828, rs144383242, rs117587674, rs113705641, rs2937268, and chromosome 14:38247577. All SNPs were located across loci of 1p31.3, 3p26.1, 6814.1, 14821.1, and 13814.13. In addition, two of the tested polymorphisms were suggestive for interaction with smoking intensity (chromosome 14:38247577 and rs2937268).Conclusions: Our study suggests interaction between genetic variance and smoking behavior for increased NMIBC tumor size at the time of diagnosis. Further replication is required to validate these findings

Development of a patient-oriented hololens application to illustrate the function of medication after myocardial infarction

Aims: Statin treatment is one of the hallmarks of secondary prevention after myocardial infarction. Adherence to statins tends to be difficult and can be improved by patient education. Novel technologies such as mixed reality (MR) expand the possibilities to support this process. To assess if an MR medication-application supports patient education focused on function of statins after myocardial infarction. Methods and results: A human-centred design-approach was used to develop an MR statin tool for Microsoft HoloLens™. Twenty-two myocardial infarction patients were enrolled; 12 tested the application, 10 patients were controls. Clinical, demographic, and qualitative data were obtained. All patients performed a test on statin knowledge. To test if patients with a higher tendency to become involved in virtual environments affected test outcome in the intervention group, validated Presence- and Immersive Tendency Questionnaires (PQ and ITQ) were used. Twenty-two myocardial infarction patients (ST-elevation myocardial infarction, 18/22, 82%) completed the study. Ten out of 12 (83%) patients in the intervention group improved their statin knowledge by using the MR application (median 8 points, IQR 8). Test improvement was mainly the result of increased understanding of statin mechanisms in the body and secondary preventive effects. A high tendency to get involved and focused in virtual environments was moderately positive correlated with better test improvement (r = 0.57, P &lt; 0.05). The median post-test score in the control group was poor (median 6 points, IQR 4). Conclusions: An MR statin education application can be applied effectively in myocardial infarction patients to explain statin function and importance.Applied Ergonomics and Desig