IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED ECG ACQUISITION SYSTEM FOR THE DETECTION OF CARDIAC ABNORMALITIES
Achraf Benba
achraf.benba@um5s.net.maMohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies (Morocco)
https://orcid.org/0000-0001-7939-0790
Fatima Zahra El Attaoui
Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies (Morocco)
http://orcid.org/0009-0001-0196-0500
Sara Sandabad
Ecole Normale Supérieure de l'Enseignement Technique de Mohammadia, Electrical Engineering and Intelligent Systems, Hassan II University of Casablanca (Morocco)
http://orcid.org/0000-0002-0813-6178
Abstract
The electrocardiogram (ECG) is a common test that measures the electrical activity of the heart. On the ECG, several cardiac abnormalities can be seen, including arrhythmias, which are one of the major causes of cardiac mortality worldwide. The objective for the research community is accurate and automated cardiovascular analysis, especially given the maturity of artificial intelligence technology and its contribution to the health area. The goal of this effort is to create an acquisition system and use artificial intelligence to classify ECG readings. This system is designed in two parts: the first is the signal acquisition using the ECG Module AD8232; the obtained signal is a single derivation that has been amplified and filtered. The second section is the classification for heart illness identification; the suggested model is a deep convolutional neural network with 12 layers that was able to categorize five types of heartbeats from the MIT-BIH arrhythmia database. The results were encouraging, and the embedded system was built.
Keywords:
electrocardiogram, arrhythmias, artificial intelligence, convolution neural networkReferences
AD8232 DS. Single-Lead, Heart Rate Monitor Front End. Analog Device, 2013.
Google Scholar
Ahsan M. M., Siddique Z.: Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine 29, 2022, 102289 [http://doi.org/10.1016/j.artmed.2022.102289].
DOI: https://doi.org/10.1016/j.artmed.2022.102289
Google Scholar
Atal D. K., Singh M.: Arrhythmia classification with ECG signals based on the optimization-enabled deep convolutional neural network. Computer Methods and Programs in Biomedicine, 196, 2020, 105607 [http://doi.org/10.1016/j.cmpb.2020.105607].
DOI: https://doi.org/10.1016/j.cmpb.2020.105607
Google Scholar
Day T. G. et al.: Artificial intelligence, fetal echocardiography, and congenital heart disease. Prenatal Diagnosis 41(6), 2021, 733–742 [http://doi.org/10.1002/pd.5892].
DOI: https://doi.org/10.1002/pd.5892
Google Scholar
Farinha J. M. et al.: Frequent premature atrial contractions as a signalling marker of atrial cardiomyopathy, incident atrial fibrillation and stroke. Cardiovascular research, 2022, cvac054 [http://doi.org/10.1093/cvr/cvac054].
DOI: https://doi.org/10.1093/cvr/cvac054
Google Scholar
Giudicessi J. R. et al.: Artificial intelligence–enabled assessment of the heart rate corrected QT interval using a mobile electrocardiogram device. Circulation 143(13), 2021, 1274–1286 [http://doi.org/10.1161/circulationaha.120.050231].
DOI: https://doi.org/10.1161/CIRCULATIONAHA.120.050231
Google Scholar
Han C. et al.: QRS complexes and T waves localization in multi-lead ECG signals based on deep learning and electrophysiology knowledge. Expert Systems with Applications 199, 2022, 117187 [http://doi.org/10.1016/j.eswa.2022.117187].
DOI: https://doi.org/10.1016/j.eswa.2022.117187
Google Scholar
Hassan S. U. et al.: Classification of cardiac arrhythmia using a convolutional neural network and bi-directional long short-term memory. Digital Health 8, 2022 [http://doi.org/10.1177/20552076221102766].
DOI: https://doi.org/10.1177/20552076221102766
Google Scholar
Higuchi K. et al.: How to use bipolar and unipolar electrograms for selecting successful ablation sites of ventricular premature contractions. Heart Rhythm 19(7), 2022, 1067–1073 [http://doi.org/10.1016/j.hrthm.2021.12.035].
DOI: https://doi.org/10.1016/j.hrthm.2021.12.035
Google Scholar
Karri M., Annavarapu C. S.: A real-time embedded system to detect QRS-complex and arrhythmia classification using LSTM through hybridized features. Expert Systems with Applications 214, 2023, 119221 [http://doi.org/10.1016/j.eswa.2022.119221].
DOI: https://doi.org/10.1016/j.eswa.2022.119221
Google Scholar
Kwon J. M. et al.: Artificial intelligence assessment for early detection of heart failure with preserved ejection fraction based on electrocardiographic features. European Heart Journal-Digital Health 2(1), 2021, 106–116 [http://doi.org/10.1093/ehjdh/ztaa015].
DOI: https://doi.org/10.1093/ehjdh/ztaa015
Google Scholar
Li T., Zhou M.: ECG classification using wavelet packet entropy and random forests. Entropy 18(8), 2016, 285 [http://doi.org/10.3390/e18080285].
DOI: https://doi.org/10.3390/e18080285
Google Scholar
Moody G. B., Mark R. G.: The impact of the MIT-BIH arrhythmia database. IEEE engineering in medicine and biology magazine 20(3), 2001, 45–50 [http://doi.org/10.1109/51.932724].
DOI: https://doi.org/10.1109/51.932724
Google Scholar
Rahman M. A. et al.: Remote monitoring of heart rate and ECG signal using ESP32. 4th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), 2021, 604–610 [http://doi.org/10.1109/AEMCSE51986.2021.00127].
DOI: https://doi.org/10.1109/AEMCSE51986.2021.00127
Google Scholar
Reis C. Q., Robar J. L.: Evaluation of the feasibility of cardiac gating for SBRT of ventricular tachycardia based on real‐time ECG signal acquisition. Journal of Applied Clinical Medical Physics, 2022, e13814 [http://doi.org/10.1002/acm2.13814].
DOI: https://doi.org/10.1002/acm2.13814
Google Scholar
Ribeiro J. M. et al.: Artificial intelligence and transcatheter interventions for structural heart disease: a glance at the (near) future. Trends in cardiovascular medicine 32(3), 2022, 153–159 [http://doi.org/10.1016/j.tcm.2021.02.002].
DOI: https://doi.org/10.1016/j.tcm.2021.02.002
Google Scholar
Vamseekrishna A. et al.: Low-Cost ECG-Based Heart Monitoring System with Ubidots Platform. Embracing Machines and Humanity Through Cognitive Computing and IoT, 2023 [http://doi.org/10.1007/978-981-19-4522-9_6].
DOI: https://doi.org/10.1007/978-981-19-4522-9_6
Google Scholar
Vinther M. et al.: A randomized trial of His pacing versus biventricular pacing in symptomatic HF patients with left bundle branch block (His-alternative). Clinical Electrophysiology 7(11), 2021, 1422–1432 [http://doi.org/10.1016/j.jacep.2021.04.003].
DOI: https://doi.org/10.1016/j.jacep.2021.04.003
Google Scholar
Zhu K. et al.: The physiologic mechanisms of paced QRS narrowing during left bundle branch pacing in right bundle branch block patients. Frontiers in Cardiovascular Medicine 9, 2022 [http://doi.org/10.3389/fcvm.2022.835493].
DOI: https://doi.org/10.3389/fcvm.2022.835493
Google Scholar
Authors
Achraf Benbaachraf.benba@um5s.net.ma
Mohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies Morocco
https://orcid.org/0000-0001-7939-0790
Authors
Fatima Zahra El AttaouiMohammed V University in Rabat, Ecole Nationale Supérieure d'Arts et Métiers, Electronic Systems Sensors and Nanobiotechnologies Morocco
http://orcid.org/0009-0001-0196-0500
Authors
Sara SandabadEcole Normale Supérieure de l'Enseignement Technique de Mohammadia, Electrical Engineering and Intelligent Systems, Hassan II University of Casablanca Morocco
http://orcid.org/0000-0002-0813-6178
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