Automated ECG interpretation is the use of artificial intelligence and pattern recognition software and knowledge bases to carry out automatically the interpretation, test reporting, and computer-aided diagnosis of electrocardiogram tracings obtained usually from a patient. History 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 A digital representation of each recorded ECG channel is obtained, by means of an analog-to-digital converter and a special data acquisition software or a digital signal processing (DSP) chip. 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, 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 may also be performed in order to provide input to pattern recognition-based programs. Logical processing and pattern recognition, using rule-based expert systems, probabilistic Bayesian analysis or fuzzy logics algorithms, cluster analysis, artificial neural networks, genetic algorithms and others techniques are used to derive conclusions, interpretation and diagnosis. A reporting program is activated and produces a proper display of original and calculated data, as well as the results of automated interpretation. In some applications, such as automatic defibrillators, an action of some sort may be triggered by results of the analysis, such as the occurrence of an atrial fibrillation or a cardiac arrest, the sounding of alarms in a medical monitor in intensive-care unit applications, and so on. Applications The manufacturing industries of ECG machines