The first automated ECG programs were developed in the 1970s, when digital ECG machines became possible by third generation digital signal processing boards. During the 1980s and 1990s, extensive research was carried out by companies and by university labs in order to improve the accuracy rate, which was not very high in the first models. AI-based programs, such abnormal ecg interpretation pdf the peak amplitude, area under the curve, displacement in relation to baseline, etc.
A reporting program is activated and produces a proper display of original and calculated data, as well as the results of automated interpretation. ECG recordings with 3 or more leads. ECG analysis, essentially to detect abnormalities. The automated ECG interpretation is a useful tool when access to a specialist is not possible. Wellens phenomenon, Left ventricular hypertrophy, left bundle branch block or in presence of a pacemaker. Automated monitoring of ST-segment during patient transport is increasingly used and improves STEMI detection sensitivity, as ST elevation is a dynamical phenomenon. Combined wavelet transformation and radial basis neural networks for classifying life threatening cardiac arrhythmias, Med.
HES EKG expert-an expert system for comprehensive ECG analysis and teaching. ECG classification with neural networks and cluster analysis. Applications of artificial neural networks in biological signal processing. Difficult ECGs in STEMI: lessons learned from serial sampling of pre- and in-hospital ECGs, Ayer et al. Computers in the processing of biological signals. Translated and reproduced by permission of the author.