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Wearing Your Heart on Your Sleeve: the Future of Cardiac Rhythm Monitoring
Purpose of ReviewThis review describes the novel category of wearable ECG monitors and identifies where patients, healthcare providers, and device...
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Mobile Health for Arrhythmia Diagnosis and Management
Palpitations are a common symptom managed by general practitioners and cardiologists; atrial fibrillation (AF) is the most common arrhythmia in...
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Daily ECG transmission versus serial 6-day Holter ECG for the assessment of efficacy of ablation for atrial fibrillation — the AGNES-ECG study
PurposeTo compare daily ECG transmissions using trans-telephonic monitoring (TTM) with repeated 6-day Holter ECG in detecting atrial fibrillation...
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Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review
Purpose of ReviewArtificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human...
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Watch for tachycardia
BackgroundWearable devices capable of measuring health metrics are becoming increasingly prevalent. Most work has investigated the potential for...
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Digital Cardiology: Opportunities for Disease Prevention
Purpose of ReviewCardiovascular diseases (CVD) are the world’s leading cause of mortality, responsible for 18 million deaths each year. Disease...
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Use of Digital Health Technology in Heart Failure and Diabetes: a Sco** Review
Use of digital health technologies (DHT) in chronic disease management is rising. We aim to evaluate the impact of DHT on clinical outcomes from...
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The in-ear region as a novel anatomical site for ECG signal detection: validation study on healthy volunteers
PurposeEarly detection of cardiac arrhythmias is a major opportunity for mobile health, as wearable devices nowadays available can detect single-lead...
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Digitale Kompetenzen in der Rhythmologie
BackgroundThe digital transformation in medicine, particularly in technology-orientated areas such as rhythmology, is leading to a rapid change in...
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Impact of COVID-19 on Arrhythmia Care Delivery
Purpose of reviewThe goal of this review is to provide the reader with an overview of how the coronavirus disease 2019 (COVID-19) pandemic affected...
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Prospective blinded evaluation of smartphone-based ECG for differentiation of supraventricular tachycardia from inappropriate sinus tachycardia
IntroductionSupraventricular tachycardias (SVT) are often difficult to document due to their intermittent, short-lasting nature. Smartphone-based...
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Validation of the SCALE-CryoAF risk model to predict very late return of atrial fibrillation after cryoballoon ablation
BackgroundTo date, few risk models have been validated to predict recurrent atrial fibrillation (AF) >1 year after ablation. The SCALE-CryoAF score...
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Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management
Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the...
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Digital health solutions in the screening of subclinical atrial fibrillation
Atrial fibrillation (AF) will become one of the biggest challenges in cardiovascular medicine in the near future. Attempting an improvement in future...
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Smart wearable devices in cardiovascular care: where we are and how to move forward
Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage...
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Validation of smartwatch electrocardiogram intervals in children compared to standard 12 lead electrocardiograms
Lay people are now able to obtain one-lead electrocardiograms (ECG) using smartwatches, which facilitates documentation of arrhythmias. The accuracy...
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Accuracy of mobile 6-lead electrocardiogram device for assessment of QT interval: a prospective validation study
IntroductionAmbulatory assessment of the heart rate–corrected QT interval (QTc) can be of diagnostic value, for example in patients on QTc-prolonging...
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Machine learning in the detection and management of atrial fibrillation
Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated...
