Digitizing ECG Signal using 2D Signal Convolution Approach

Abstract

ECG signal printed on a graph paper has been widely used by medical examiners to analyze diseases related to the heart. Medical practitioners rely on historical records to perform diagnosis. Constantly accessing the ECG printed graph paper manually could be time consuming as there are bulk of graph papers for examination. The proposed work aims to convert the printed ECG graph paper into digitized ECG for remote diagnosis. The ECG printed graph paper undergoes conversion into ECG artifact before transforming as digitized ECG. In the initial phase, patient information in the ECG artifact is preserved by encoding into a QR Code. In phase two, preliminary processing is done on ECG artifact for removal of gridline in the background. Image convolution method is proposed as the process for background gridline removal. Then, morphological image processing is implemented to enhance the ECG artifact. In phase three, segmentation process takes place, in which the ECG artifact is divided into segments for separating the waveforms. In the final phase of ECG digitization, the location of the signal is traced for reshaping the ECG artifact as digitized ECG. The accuracy of the ECG digitization is measured through the heart rate that is calculated using our approach and compared with the one on ECG printed graph paper. The average sum of squared error of the heart rate between the ECG printed graph paper and digitized ECG is 0.005618. The digitized ECG can be useful for medical examiners and practitioners in telemedicine where remote diagnosis may be needed.