The first automated ECG programs were developed in the 1970s, when digital ECG machines became possible by third-generation digital signal processing boards. Commercial models, such as those developed by Hewlett-Packard, incorporated these programs into clinically used devices.
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. For this purpose, several signal databases with normal and abnormal ECGs were built by institutions such as MIT and used to test the algorithms and their accuracy.
Phases
Basic signal features of time and amplitude which are measured and form the basis for automated ECG analysis
The resulting digital signal is processed by a series of specialized algorithms, which start by conditioning it, e.g., removal of noise, baselevel variation, etc.
Feature extraction: mathematical analysis is now performed on the clean signal of all channels, to identify and measure a number of features which are important for interpretation and diagnosis, this will constitute the input to AI-based programs, such as the peak amplitude, area under the curve, displacement in relation to baseline, etc., of the P, Q, R, S and T waves,[1] the time delay between these peaks and valleys, heart rate frequency (instantaneous and average), and many others. Some sort of secondary processing such as Fourier analysis and wavelet analysis[2] may also be performed in order to provide input to pattern recognition-based programs.
The manufacturing industries of ECG machines is now entirely digital, and many models incorporate embedded software for analysis and interpretation of ECG recordings with 3 or more leads. Consumer products, such as home ECG recorders for simple, 1-channel heart arrhythmia detection, also use basic ECG analysis, essentially to detect abnormalities. Some application areas are:
Incorporation into automatic defibrillators, so that autonomous decision can be reached whether there is a cause for administering the electrical shock on basis of an atrial or ventricular arrhythmia;
The automated ECG interpretation is a useful tool when access to a specialist is not possible. Although considerable effort has been made to improve automated ECG algorithms, the sensitivity of the automated ECG interpretation is of limited value in the case of STEMI equivalent[6][7] as for example with "hyperacute T waves",[8] de Winter ST-T complex,[9] 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.
↑ Al-Fahoum, AS; Howitt, I. Combined wavelet transformation and radial basis neural networks for classifying life threatening cardiac arrhythmias, Med. Biol. Eng. Comput. 37 (1999), pp. 566–573.
↑ Mautgreve, W., et al. HES EKG expert-an expert system for comprehensive ECG analysis and teaching. Proc. Computers in Cardiology: Jerusalem, Israel 19–22 September 1989. (USA: IEEE Comput. Soc. Press, 1990. p. 77–80).
↑ Bortolan, G., et al. ECG classification with neural networks and cluster analysis. Proc. Computers in Cardiology. Venice, Italy, 23–26 September 1991. (USA: IEEE Comput. Soc. Press, 1991. p. 177-80).
↑ Sabbatini, R.M.E. Applications of artificial neural networks in biological signal processing. MD Computing, 3(2), 165-172 March 1996.
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