Signal Extraction from Scans of Electrocardiograms

Sammanfattning: In this thesis, we propose a Deep Learning method for fully automated digitization of ECG (Electrocardiogram) sheets. We perform the digitization of ECG sheets in three steps: layout detection, column-wise signal segmentation, and finally signal retrieval - each of them performed by a Convolutional Neural Network. These steps leverage advances in the fields of object detection and pixel-wise segmentation due to the rise of CNNs in image processing. We train each network on synthetic images that reect the challenges of real-world data. The use of these realistic synthetic images aims at making our models robust to the variability of real-world ECG sheets. Compared with computer vision benchmarks, our networks show promising results. Our signal retrieval network significantly outperforms our implementation of the benchmark. Our column segmentation model shows robustness to overlapping signals, an issue of signal segmentation that computer vision methods are not equipped to deal with. Overall, this fully automated pipeline provides a gain in time and precision for physicians willing to digitize their ECG database.