VinDigitizer: An Image Processing Approach to Digitize Paper ECG Records

The dominance of traditional paper-based ECG records in clinical settings poses a barrier to integrating AI for cardiovascular disease detection.The shift towards digital ECG is imperative yet hindered by the lack of reliable digitization tools.For the PhysioNet Challenge 2024, our Vi-nUniTeam proposes a hybrid digitization approach merging Deep Learning and image processing algorithms.Our methodology comprises three key phases: lead segmentation, signal extraction, and scale conversion.Initially, we employ a pre-trained YOLOv8 single-shot detector to capture snapshots of 12 ECG leads.Then, each snapshot undergoes Otsu's technique for RGB to binary conversion, Hough transform for grid detection, and Viterbi's algorithm for sampling and extracting signal values in pixels.Finally, we convert these pixel values to voltage values by estimating grid resolution within the snapshot.To address uneven lengths in the extracted lead signals, we use cubic spline interpolation to horizontally scale the signals to align with a predetermined target length while preserving their waveform.Our method ranked 9/16 in the official phase, with SNR of -0.066.