Innovations in cardiology:Towards patient centered care

The thesis consists of three parts: In Part 1, the effect of telemonitoring on patients with congenital heart defects or genetic cardiomyopathy was investigated. Telemonitoring does not lead to a reduction in the number of unplanned hospital visits. Part 2 researched the effect of patient education through virtual reality. Particularly, patients with no prior experience in the operating room or the hospital benefit from this comprehensive education. In patients with experience, we did not observe a decreased anxiety about the procedure (even if it was a new procedure to them). In part 3, it was explored whether artificial intelligence can contribute to precise data point localization of the R-wave on the electrocardiogram. Our technique was data-point precise and outperformed current techniques. Expanding this technique in the future could assist cardiologists in automatically detecting heart conditions

Innovations in cardiology:Towards patient centered care

The thesis consists of three parts: In Part 1, the effect of telemonitoring on patients with congenital heart defects or genetic cardiomyopathy was investigated. Telemonitoring does not lead to a reduction in the number of unplanned hospital visits. Part 2 researched the effect of patient education through virtual reality. Particularly, patients with no prior experience in the operating room or the hospital benefit from this comprehensive education. In patients with experience, we did not observe a decreased anxiety about the procedure (even if it was a new procedure to them). In part 3, it was explored whether artificial intelligence can contribute to precise data point localization of the R-wave on the electrocardiogram. Our technique was data-point precise and outperformed current techniques. Expanding this technique in the future could assist cardiologists in automatically detecting heart conditions

Innovations in cardiology:Towards patient centered care

The thesis consists of three parts: In Part 1, the effect of telemonitoring on patients with congenital heart defects or genetic cardiomyopathy was investigated. Telemonitoring does not lead to a reduction in the number of unplanned hospital visits. Part 2 researched the effect of patient education through virtual reality. Particularly, patients with no prior experience in the operating room or the hospital benefit from this comprehensive education. In patients with experience, we did not observe a decreased anxiety about the procedure (even if it was a new procedure to them). In part 3, it was explored whether artificial intelligence can contribute to precise data point localization of the R-wave on the electrocardiogram. Our technique was data-point precise and outperformed current techniques. Expanding this technique in the future could assist cardiologists in automatically detecting heart conditions

Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms

Low-cost wearables with capability to record electrocardiograms (ECG) are becoming increasingly available. These wearables typically acquire single-lead ECGs that are mainly used for screening of cardiac arrhythmias such as atrial fibrillation. Most arrhythmias are characteruzed by changes in the RR-interval, hence automatic methods to diagnose arrythmia may utilize R-peak detection. Existing R-peak detection methods are fairly accurate but have limited precision. To enable data-point precise detection of R-peaks, we propose a method that uses a fully convolutional dilated neural network. The network is trained and evaluated with manually annotated R-peaks in a heterogeneous set of ECGs that contain a wide range of cardiac rhythms and acquisition noise. 700 randomly chosen ECGs from the PhysioNet/CinC challenge 2017 were used for training (n=500), validation (n=100) and testing (n=100). The network achieves a precision of 0.910, recall of 0.926, and an F1-score of 0.918 on the test set. Our data-point precise R-peak detector may be important step towards fully automatic cardiac arrhythmia detection.Clinical relevance- This method enables data-point precise detection of R-peaks that provides a basis for detection and characterization of arrhythmias