IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED ECG ACQUISITION SYSTEM FOR THE DETECTION OF CARDIAC ABNORMALITIES

Achraf Benba

achraf.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

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 network

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

Download


Published
2023-03-31

Cited by

Benba, A., Zahra El Attaoui, F., & Sandabad, S. (2023). IMPLEMENTATION OF AN ARTIFICIAL INTELLIGENCE-BASED ECG ACQUISITION SYSTEM FOR THE DETECTION OF CARDIAC ABNORMALITIES. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(1), 22–25. https://doi.org/10.35784/iapgos.3387

Authors

Achraf Benba 
achraf.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 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

Authors

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

Statistics

Abstract views: 261
PDF downloads: 279