PTB-Image: A Scanned Paper ECG Dataset for Digitization and Image-based Diagnosis

Electrocardiograms (ECGs) recorded on paper remain prevalent in clinical practice, yet their use presents challenges for automated analysis and digital storage. To address this issue, we introduce PTB-Image, a dataset comprising scanned paper ECGs with corresponding digital signals, enabling research on ECG digitization. We also provide VinDigitizer, a digitization baseline to convert paper-based ECGs into digital time-series signals. The method involves detecting signal rows, extracting waveforms from the background, and reconstructing numerical values from the digitized traces. We applied VinDigitizer to 549 scanned ECGs and evaluated its performance against the original PTB dataset (modified to match the printed signals). The results achieved a mean signal-to-noise ratio (SNR) of 0.01 dB, highlighting both the feasibility and challenges of ECG digitization, particularly in mitigating distortions from printing and scanning processes. By providing PTB-Image and baseline digitization methods, this work aims to facilitate advancements in ECG digitization, enhancing access to historical ECG data and supporting applications in telemedicine and automated cardiac diagnostics.

MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Previous studies have investigated several machine and deep learning models for electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, conventional feature extractions derived from ECG signals, such as R-peaks and RR intervals, may fail to capture crucial information encompassed within the complete PQRST segments. In this study, we propose an innovative approach to address this diagnostic gap by delving deeper into the comprehensive segments of the ECG signal. The proposed methodology draws inspiration from Matrix Profile algorithms, which generate an Euclidean distance profile from fixed-length signal subsequences. From this, we derived the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean Distance Profile (MeanDP) based on the minimum, maximum, and mean of the profile distances, respectively. To validate the effectiveness of our approach, we use the modified LeNet-5 architecture as the primary CNN model, along with two existing lightweight models, BAFNet and SE-MSCNN, for ECG classification tasks. Our extensive experimental results on the PhysioNet Apnea-ECG dataset revealed that with the new feature extraction method, we achieved a per-segment accuracy up to 92.11 \% and a per-recording accuracy of 100\%. Moreover, it yielded the highest correlation compared to state-of-the-art methods, with a correlation coefficient of 0.989. By introducing a new feature extraction method based on distance relationships, we enhanced the performance of certain lightweight models, showing potential for home sleep apnea test (HSAT) and SA detection in IoT devices. The source code for this work is made publicly available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea.