Detection of atrial septal aneurysm on ECG based on Deep Learning algorithm (ANN)

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Published Nov 9, 2025
https://doi.org/10.62438/tunismed.v103i9.5646
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Vol. 103 No. 9 (2025): Tunis Med sept 2025

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Mohammed Marouane Saim
Mohammadia School of Engineers, Mohammed V University of Rabat, ERSC Research Center, 10080, Rabat, Morocco.
https://orcid.org/0000-0002-4963-3031
Omar Alami
University of Paris Est Créteil, 94100, Paris, France.
Hassan Ammor
Mohammadia School of Engineers, Mohammed V University of Rabat, ERSC Research Center, 10080, Rabat, Morocco.
Mohamed Alami
Ghandi cardiology Center, 20026, Casablanca, Morocco

Abstract

Atrial Septal Aneurysm (ASA) is a real clinical challenge due to its possible association with other relevant conditions. The absence of specific symptoms or electrocardiogram (ECG) criteria explain why its diagnosis is very often qualified as incidental. The aim of this study is to assess ASA detection by Machine Learning (ML) on electrocardiogram (ECG) data. The study is a retrospective analysis of 233 individuals, including 123 with ASA confirmed by trans-thoracic Echocardiography (TTE) and 110 without ASA. Key ECG parameters were examined. An Artificial Neural Network (ANN) algorithm was trained on 80% of the dataset, with the remaining 20% for testing. Results demonstrated a model sensitivity of 73%, specificity of 84%, Positive Predictive Value (PPV) of 80%, Negative Predictive Value (NPV) of 73%, and an F-1 score of 0.79. The Receiver Operating Characteristic (ROC) curve exhibited an Area Under the Curve (AUC) of 0.8, indicative of excellent diagnostic test performance. This study shows that ASA detection by ECG using ML is possible, offering a potential opening for a broader clinical understanding and implications of this cardiac abnormality.


 

Keywords:

Atrial septal aneurysm, Artificial Neural Network, ECG, K-Fold Cross-Validation

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References

  1. MORELLI S, VOCI P, MORABITO G, SGRECCIA A, DE MARZIO P, MARZANO F, GIORDANO M. ATRIAL SEPTAL ANEURYSM AND CARDIAC ARRHYTHMIAS. INT J CARDIOL. 1995 MAY;49(3):257-65. DOI: 10.1016/0167-5273(95)02317-P. PMID: 7649672.
  2. Mügge, A., Daniel, W. G., Angermann, C., Spes, C., Khandheria, B. K., Kronzon, I., Freedberg, R. S., Keren, A., Dennig, K., Engberding, R., Sutherland, G. R., Vered, Z., Erbel, R., Visser, C. A., Lindert, O., Hausmann, D., & Wenzlaff, P. (1995). Atrial Septal Aneurysm in Adult Patients. In Circulation (Vol. 91, Issue 11, pp. 2785–2792). Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.1161/01.cir.91.11.2785
  3. Yetkin E, Atalay H, Ileri M. Atrial septal aneurysm: Prevalence and covariates in adults. Int J Cardiol. 2016 Nov 15;223:656-659. doi: 10.1016/j.ijcard.2016.08.220. Epub 2016 Aug 16. PMID: 27567234.
  4. Kuriki A, Ueno Y, Kamiya Y, Shimizu T, Doijiri R, Tateishi Y, Kikuno M, Shimada Y, Takekawa H, Yamaguchi E, Koga M, Ihara M, Ono K, Tsujino A, Hirata K, Toyoda K, Hasegawa Y, Hattori N, Urabe T. Atrial Septal Aneurysm may Cause In-Hospital Recurrence of Cryptogenic Stroke. J Atheroscler Thromb. 2021 May 1;28(5):514-523. doi: 10.5551/jat.56440. Epub 2020 Jul 17. PMID: 32684557; PMCID: PMC8193779.
  5. Rigatelli G, Zuin M, Bilato C. Atrial septal aneurysm contribution to the risk of cryptogenic stroke in patients with patent foramen ovale: A brief updated systematic review and meta-analysis. Trends Cardiovasc Med. 2023 Aug;33(6):329-333. doi: 10.1016/j.tcm.2022.02.006. Epub 2022 Feb 15. PMID: 35181471.
  6. Yetkin E, Ileri M, Korkmaz A, Ozturk S. Association between atrial septal aneurysm and arrhythmias. Scand Cardiovasc J. 2020 Jun;54(3):169-173. doi: 10.1080/14017431.2019.1667525. Epub 2019 Sep 23. PMID: 31544553.
  7. Rudoy AS, Bova AA, Nekhaichik TA. Anevrizma mezhpredserdnoĭ peregorodki: évoliutsiia diagnosticheskikh i klinicheskikh predstavleniĭ [Atrial septal aneurysm: Evolution of diagnostic and clinical judgements]. Ter Arkh. 2017;89(9):104-108. Russian. doi: 10.17116/terarkh2017899104-108. PMID: 29039838.
  8. RUSSO V, RAGO A, DI MEO F, PAPA AA, CIARDIELLO C, CRISTIANO A, CALABRÒ R, RUSSO MG, NIGRO G. ATRIAL SEPTAL ANEURYSMS AND SUPRAVENTRICULAR Arrhythmias : The Role of Atrial Electromechanical Delay. Echocardiography. 2015 Oct ;32(10):1504-14. Doi : 10.1111/echo.12908. Epub 2015 Mar 3. PMID: 25735318.
  9. Hanley PC, Tajik AJ, Hynes JK, Edwards WD, Reeder GS, Hagler DJ, Seward JB. Diagnosis and classification of atrial septal aneurysm by two-dimensional echocardiography : report of 80 consecutive cases. J Am Coll Cardiol. 1985 Dec ;6(6):1370-82. Doi : 10.1016/s0735-1097(85)80228-x. PMID: 4067118.
  10. Olivares-Reyes A, Chan S, Lazar EJ, Bandlamudi K, Narla V, Ong K. Atrial septal aneurysm : a new classification in two hundred five adults. J Am Soc Echocardiogr. 1997 Jul-Aug ;10(6):644-56. Doi : 10.1016/s0894-7317(97)70027-0. PMID : 9282354.
  11. Razaq M, Parihar RK, Saini G. Atrial septal aneurysm and stroke. Ann Pediatr Cardiol. 2012 Jan ;5(1):98-9. Do i: 10.4103/0974-2069.93726. PMID : 22529616; PMCID: PMC3327030.
  12. Sarma A. Crochetage Sign: An Invaluable Independent ECG Sign in Detecting ASD. Indian J Crit Care Med. 2021 Feb;25(2):234-235. doi: 10.5005/jp-journals-10071-23731. PMID: 33707907; PMCID: PMC7922464.
  13. Xie L, Liu Z, Zhang X, Xu K, Xu Q, Lu L, Hu C, Han S, Li J. Electrocardiography-gated dual-source computed tomography in the detection of atrial septal aneurysm. Exp Ther Med. 2018 Nov;16(5):4260-4264. doi: 10.3892/etm.2018.6717. Epub 2018 Sep 11. PMID: 30402163; PMCID: PMC6200974.
  14. St»hle, L., & Wold, S. (1989). Analysis of variance (ANOVA). In Chemometrics and Intelligent Laboratory Systems (Vol. 6, Issue 4, pp. 259–272). Elsevier BV. https://doi.org/10.1016/0169-7439(89)80095-4
  15. Kucuk, U., Eyuboglu, M., Kucuk, H. O., & Degirmencioglu, G. (2016). Importance of using proper post hoc test with ANOVA. In International Journal of Cardiology (Vol. 209, p. 346). Elsevier BV. https://doi.org/10.1016/j.ijcard.2015.11.061
  16. N. Jiwani, K. Gupta and P. Whig, "Novel HealthCare Framework for Cardiac Arrest with the Application of AI Using ANN," 2021 5th International Conference on Information Systems and Computer Networks (ISCON), Mathura, India, 2021, pp. 1-5, doi: 10.1109/ISCON52037.2021.9702493.
  17. N. Mitra and B. I. Morshed, "Analyzing Clinical 12-Lead ECG Images Using Deep Learning Algorithms for Objective Detection of Cardiac Diseases," 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), NewYork, NY, NY, USA, 2022, pp.0517-0523, doi : 10.1109/UEMCON54665.2022.9965722.
  18. Dongmei Han, Qigang Liu, Weiguo Fan,A new image classification method using CNN transfer learning and web data augmentation,Expert Systems with Applications, Volume95, 2018, Pages43-56, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2017.11.028.
  19. Haibing Wu, Xiaodong Gu,Towards dropout training for convolutional neural networks,Neural Networks,Volume 71, 2015, Pages 1-10,ISSN 0893-6080,https://doi.org/10.1016/j.neunet.2015.07.007.
  20. M. Shaha and M. Pawar, "Transfer Learning for Image Classification," 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 2018, pp. 656-660, doi: 10.1109/ICECA.2018.8474802.
  21. Mahmoud Said ElSayed, Nhien-An Le-Khac, Marwan Ali Albahar, Anca Jurcut,A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique,Journal of Network and Computer Applications,Volume 191, 2021,103160, ISSN 1084-8045,https://doi.org/10.1016/j.jnca.2021.103160.
  22. Al-Issa, Y., Alqudah, A.M. A lightweight hybrid deep learning system for cardiac valvular disease classification. Sci Rep 12, 14297 (2022). https://doi.org/10.1038/s41598-022-18293-7
  23. Singh P, Haimovich J, Reeder C, Khurshid S, Lau E, Cunningham J, Philippakis A, Anderson C, Ho J, Lubitz S, Batra P “One Clinician Is All You Need–Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development”, JMIR Med Inform 2022;10(9): e38178, https://medinform.jmir.org/2022/9/e38178
  24. Zhou, Y., Cao, Y., Fang, Z. et al. Research on the clinical factors of cardiac iron deposition in children with beta-thalassemia major. Eur J Pediatr (2023). https://doi.org/10.1007/s00431-023-05300-w
  25. Bhatt, C.M.; Patel, P.; Ghetia, T.; Mazzeo, P.L. Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms 2023, 16, 88. https://doi.org/10.3390/a16020088