Analysis on Conversion Process from Paper Record ECG to Computer based ECG

Abstract

The capacity of electrocardiograms (ECG) recorded in hospital is increasing. Although modern electrocardiographs have digital output, most of ECG's are still recorded on grid papers. Recently, exchanging patients' clinical information between healthcare facilities has become very important. It will be very helpful if paper type of ECG could be converted to digital form. In this study, a morphological method is developed to digitize ECG signal from the paper charts. The results show that the method can erase the background noise and provide the digital ECG signal from ECG paper charts correctly. ECG signal digitization is practically used in ECG data exchanging between healthcare providers. Experimental results on sample ECG paper records are very encouraging and show the promise of efficiency in ECG data storage and retrieval and easy manipulation for clinical uses.

P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images

“…We build upon this approach to automatically detect P-waves from ECG images, taking into consideration that an ECG is always printed on a grid paper. The grid background can be removed by the algorithm proposed in [2], as a pre-processing stage. Our proposed algorithm is then used to detect P-waves in the image-based ECG.…”

“…In developed countries, modern electrocardiograms (ECGs) that produce digital output are the most commonly used diagnosis technology. On the other hand, in developing countries, paper-based ECGs are still dominant [2,3]. This study is concerned with the medical environment in developing countries where paper-based ECGs are used.…”

“…These paper-based ECG images require digitization so that they can be transformed into 1D signals in order to apply the state-of-the-art algorithms that have been well studied in the literature. Working on a digitized version of paper-based ECGs (1D version) has important advantages in terms of storage space and the ease of exchanging patients' clinical information between healthcare facilities [2,4,5]. Yet, certain limitations remain, such as dealing with alignment issues, the loss of relative timing between leads, and inaccuracy in high-frequency regions, to name of few.…”

“…This should help clinicians in diagnosing several cardiac arrhythmias. Furthermore, the proposed algorithm works on printed ECG signals, which are the most commonly used in developing countries, without the need for digitalization, as in [2]. The proposed automated algorithm uses U-Net deep fully convolutional networks.…”

Combining Optical Character Recognition With Paper ECG Digitization

“…This overlap interferes with accurate digitization. Existing methods utilize morphological processing and connected component analysis (CCA) [13] for removal of non-overlapping characters [14] , but the extent to which overlapping ECG characters affects the digitization process has not been addressed and thoroughly investigated. While CCA does detect and categorize isolated characters, overlapping characters are often still categorized to be a part of the original wave, making it harder to remove them.…”