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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Automated ECG interpretation</span></span>

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<p><b>Automated ECG interpretation</b> is the use of <a href="Artificial_intelligence" title="Artificial intelligence">artificial intelligence</a> and <a href="Pattern_recognition" title="Pattern recognition">pattern recognition</a> <a href="Software" title="Software">software</a> and <a href="Knowledge_base" title="Knowledge base">knowledge bases</a> to carry out automatically the interpretation, test reporting, and <a href="Computer-aided_diagnosis" title="Computer-aided diagnosis">computer-aided diagnosis</a> of <a href="Electrocardiogram" class="mw-redirect" title="Electrocardiogram">electrocardiogram</a> tracings obtained usually from a <a href="Patient" title="Patient">patient</a>.

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<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

<p>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 Marquette Electronics—<a href="General_Electric" title="General Electric">General Electric</a> (GE), <a href="Hewlett-Packard" title="Hewlett-Packard">Hewlett-Packard</a>-<a href="Philips" title="Philips">Philips</a> and <a href="Mortara_Instrument" title="Mortara Instrument">Mortara</a> incorporated these programs into clinically used devices.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>

</p><p>Further, automated interpretation of ECGs was driven by advancements in microprocessor technology. In 1979, the introduction of the <a href="Motorola_68000" title="Motorola 68000">Motorola 68000</a> <a href="32-bit_microprocessor" class="mw-redirect" title="32-bit microprocessor">32-bit microprocessor</a> enabled a leap forward in ECG device capabilities.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup>

</p><p>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 <a href="MIT" class="mw-redirect" title="MIT">MIT</a> and used to test the algorithms and their accuracy.

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<div class="mw-heading mw-heading2"><h2 id="Phases">Phases</h2></div>

<ol><li>A digital representation of each recorded ECG channel is obtained, by means of an <a href="Analog-to-digital_converter" title="Analog-to-digital converter">analog-to-digital converter</a> and a special <a href="Data_acquisition" title="Data acquisition">data acquisition</a> software or a <a href="Digital_signal_processing" title="Digital signal processing">digital signal processing</a> (DSP) <a href="Integrated_circuit" title="Integrated circuit">chip</a>.</li>

<li>The resulting digital signal is processed by a series of specialized <a href="Algorithm" title="Algorithm">algorithms</a>, which start by <a href="Signal_conditioning" title="Signal conditioning">conditioning</a> it, e.g., removal of <a href="Noise_(signal)" class="mw-redirect" title="Noise (signal)">noise</a>, <a href="Baselevel" class="mw-redirect" title="Baselevel">baselevel</a> variation, etc.</li>

<li><a href="Feature_extraction" class="mw-redirect" title="Feature extraction">Feature extraction</a>: 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,<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup> the time delay between these peaks and valleys, heart rate frequency (instantaneous and average), and many others. Some sort of secondary processing such as <a href="Fourier_analysis" title="Fourier analysis">Fourier analysis</a> and <a href="Wavelet_analysis" class="mw-redirect" title="Wavelet analysis">wavelet analysis</a><sup id="cite_ref-4" class="reference"><a href="#cite_note-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> may also be performed in order to provide input to pattern recognition-based programs.</li>

<li>Logical processing and pattern recognition, using rule-based <a href="Expert_system" title="Expert system">expert systems</a>,<sup id="cite_ref-5" class="reference"><a href="#cite_note-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup> probabilistic <a href="Bayesian_analysis" class="mw-redirect" title="Bayesian analysis">Bayesian analysis</a> or <a href="Fuzzy_logics" class="mw-redirect" title="Fuzzy logics">fuzzy logics</a> algorithms, <a href="Cluster_analysis" title="Cluster analysis">cluster analysis</a>,<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup> <a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">artificial neural networks</a>,<sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup> <a href="Genetic_algorithm" title="Genetic algorithm">genetic algorithms</a> and others techniques are used to derive conclusions, interpretation and diagnosis.</li>

<li>A reporting program is activated and produces a proper display of original and calculated data, as well as the results of automated interpretation.</li>

<li>In some applications, such as automatic <a href="Defibrillation" title="Defibrillation">defibrillators</a>, an action of some sort may be triggered by results of the analysis, such as the occurrence of an <a href="Atrial_fibrillation" title="Atrial fibrillation">atrial fibrillation</a> or a <a href="Cardiac_arrest" title="Cardiac arrest">cardiac arrest</a>, the sounding of alarms in a <a href="Medical_monitor" class="mw-redirect" title="Medical monitor">medical monitor</a> in <a href="Intensive-care_unit" class="mw-redirect" title="Intensive-care unit">intensive-care unit</a> applications, and so on.</li></ol>

<div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2></div>

<p>The manufacturing industries of ECG machines is now entirely digital, and many models incorporate <a href="Embedded_software" title="Embedded software">embedded software</a> for analysis and interpretation of ECG recordings with 3 or more leads. Consumer products, such as home ECG recorders for simple, 1-channel <a href="Heart_arrhythmia" class="mw-redirect" title="Heart arrhythmia">heart arrhythmia</a> detection, also use basic ECG analysis, essentially to detect abnormalities. Some application areas are:

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<ul><li>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;</li>

<li>Portable ECG used in <a href="Telemedicine" class="mw-redirect" title="Telemedicine">telemedicine</a>. These machines are used to send ECG recordings via a telecommunications link, such as <a href="Telephone" title="Telephone">telephone</a>, cellular <a href="Data_communication" title="Data communication">data communication</a> or <a href="Internet" title="Internet">Internet</a></li>

<li>Conventional ECG machines to be used in <a href="Primary_healthcare" class="mw-redirect" title="Primary healthcare">primary healthcare</a> settings where a trained <a href="Cardiologist" class="mw-redirect" title="Cardiologist">cardiologist</a> is not available</li></ul>

<div class="mw-heading mw-heading2"><h2 id="Implications_and_limitations">Implications and limitations</h2></div>

<p>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 <a href="Myocardial_infarction" title="Myocardial infarction">STEMI</a> equivalent<sup id="cite_ref-8" class="reference"><a href="#cite_note-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup> as for example with "hyperacute T waves",<sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> de Winter ST-T complex,<sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup> 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.

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<ul><li><a href="Medical_monitor" class="mw-redirect" title="Medical monitor">Medical monitor</a></li>

<li><a href="Holter_monitor" title="Holter monitor">Holter monitor</a></li>

<li>Open ECG project</li>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

/* start https://en.wikipedia.org/ */

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</style><cite id="CITEREFMacfarlaneKennedy2021" class="citation journal cs1">Macfarlane, Peter W.; Kennedy, Julie (23 September 2021). <a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Fhearts2040034">"Automated ECG Interpretation—A Brief History from High Expectations to Deepest Networks"</a>. <i>Hearts</i>. <b>2</b> (4): <span class="nowrap">433–</span>448. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://doi.org/10.3390%2Fhearts2040034">10.3390/hearts2040034</a></span>. <a href="ISSN_(identifier)" class="mw-redirect" title="ISSN (identifier)">ISSN</a> <a rel="nofollow" class="external text" href="https://search.worldcat.org/issn/2673-3846">2673-3846</a>.</cite></span>

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<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><cite class="citation web cs1"><a rel="nofollow" class="external text" href="https://www.amps-llc.com/uploads/2025-1-31/Heartline_dec2024-Biosignal%20cover%20story.pdf">"HeartLine - News from the Division of Cardiology at the University of California, San Francisco - Center for Biosignal Research Decoding Music of the Heart"</a> <span class="cs1-format">(PDF)</span>. <q>The 1979 release of the Motorola 68000 32-bit microprocessor chip was a gamechanger. "This chip allowed us to design a circuit board with the horsepower to do everything," said Dr. Mortara, who invented the hardware and software for the new interpretive ECG device. "It no longer had to be connected to an outside computer, and the interpretation was rendered immediately [by the machine], right at the bedside.</q></cite></span>

</li>

<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.biopac.com/Manuals/app_pdf/app148.pdf">BioPac Systems. Application Note: Automated ECG Analysis</a></span>

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<li id="cite_note-4"><span class="mw-cite-backlink"><b><a href="#cite_ref-4">^</a></b></span> <span class="reference-text">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.</span>

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<li id="cite_note-5"><span class="mw-cite-backlink"><b><a href="#cite_ref-5">^</a></b></span> <span class="reference-text">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).</span>

</li>

<li id="cite_note-6"><span class="mw-cite-backlink"><b><a href="#cite_ref-6">^</a></b></span> <span class="reference-text">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).</span>

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<li id="cite_note-7"><span class="mw-cite-backlink"><b><a href="#cite_ref-7">^</a></b></span> <span class="reference-text">Sabbatini, R.M.E. Applications of artificial neural networks in biological signal processing. MD Computing, 3(2), 165-172 March 1996.</span>

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<li id="cite_note-8"><span class="mw-cite-backlink"><b><a href="#cite_ref-8">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.jecgonline.com/article/S0022-0736%2814%2900121-6/abstract">Difficult ECGs in STEMI: lessons learned from serial sampling of pre- and in-hospital ECGs, Ayer et al., JECG, 2014</a></span>

</li>

<li id="cite_note-9"><span class="mw-cite-backlink"><b><a href="#cite_ref-9">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.ecg-quiz.com/guidelines/stemi/">ECG Interpretation - STEMI and equivalent, ebook</a></span>

</li>

<li id="cite_note-10"><span class="mw-cite-backlink"><b><a href="#cite_ref-10">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="https://archive.today/20131207033000/http://www.ajemjournal.com/article/S0735-6757(02)92193-5/abstract">The Prominent T wave: Electrocardiographic differential diagnosis</a>, Sommers et al., American Journal of Emergency Medicine</span>

</li>

<li id="cite_note-11"><span class="mw-cite-backlink"><b><a href="#cite_ref-11">^</a></b></span> <span class="reference-text"><a rel="nofollow" class="external text" href="http://www.nejm.org/doi/full/10.1056/NEJMc0804737">A New ECG Sign of Proximal LAD Occlusion, de Winter, NEJM, 2008</a></span>

</li>

</ol></div>

<div class="mw-heading mw-heading2"><h2 id="Sources">Sources</h2></div>

<ul><li><a href="Renato_M.E._Sabbatini" class="mw-redirect" title="Renato M.E. Sabbatini">Sabbatini, RME</a>: <a rel="nofollow" class="external text" href="http://www.informaticamedica.org.br/informed/sinais.htm">O computador no processamento de sinais biológicos</a>. Revista Informédica, 2 (12): 5–9, 1995. Computers in the processing of biological signals. (In Portuguese)</li></ul>

<p><br>Translated and reproduced by permission of the author.

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<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external text" href="https://www.emkatech.com/product/ecgauto-software/">ecgAUTO in-depth ECG analysis software for preclinical research</a></li>

<li><a rel="nofollow" class="external text" href="http://www.clinicalwindow.liitin.net/dl/Art20_1(Kligfield1_6f).pdf">Kligfield, P. Automated Analysis of ECG Rhythm</a></li>

<li><a rel="nofollow" class="external text" href="http://www.physionet.org">Physionet</a></li>

<li><a rel="nofollow" class="external text" href="http://www.ecg-quiz.com">Telemedical ECG Interpretation training module</a></li></ul></div><div><div class="zim-footer">

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