Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

Jintai Chen; Ying Zhang; Qing Chang; Yixiao Zhang; Dantong Li; Jia Qiu; Lianting Hu; Xiaoting Peng; Yunmei Du; Yunfei Gao; DANNY Z. CHEN; Abdelouahab Bellou; Jian Wu; Huiying Liang

doi:10.1038/s41467-024-44930-y

journal article Open Access Feb 01, 2024

Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

Jintai Chen

Ying Zhang

Qing Chang

Yixiao Zhang

Dantong Li Jia Qiu Lianting Hu Xiaoting Peng Yunmei Du Yunfei Gao

DANNY Z. CHEN Abdelouahab Bellou Jian Wu

Huiying Liang

Nature Communications Vol. 15 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/s41467-024-44930-y

Abstract

AbstractEarly detection is critical to achieving improved treatment outcomes for child patients with congenital heart diseases (CHDs). Therefore, developing effective CHD detection techniques using low-cost and non-invasive pediatric electrocardiogram are highly desirable. We propose a deep learning approach for CHD detection, CHDdECG, which automatically extracts features from pediatric electrocardiogram and wavelet transformation characteristics, and integrates them with key human-concept features. Developed on 65,869 cases, CHDdECG achieved ROC-AUC of 0.915 and specificity of 0.881 on a real-world test set covering 12,000 cases. Additionally, on two external test sets with 7137 and 8121 cases, the overall ROC-AUC were 0.917 and 0.907 while specificities were 0.937 and 0.907. Notably, CHDdECG surpassed cardiologists in CHD detection performance comparison, and feature importance scores suggested greater influence of automatically extracted electrocardiogram features on CHD detection compared with human-concept features, implying that CHDdECG may grasp some knowledge beyond human cognition. Our study directly impacts CHD detection with pediatric electrocardiogram and demonstrates the potential of pediatric electrocardiogram for broader benefits.

Topics

No keywords indexed for this article. Browse by subject →

References

69

[1]

Global, regional, and national burden of congenital heart disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Meghan S Zimmerman, Alison Grace Carswell Smith, Craig A Sable et al.

The Lancet Child & Adolescent Health 2020 10.1016/s2352-4642(19)30402-x

[2]

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017

Gregory A Roth, Degu Abate, Kalkidan Hassen Abate et al.

The Lancet 2018 10.1016/s0140-6736(18)32203-7

[3]

Tworetzky, W. et al. Improved surgical outcome after fetal diagnosis of hypoplastic left heart syndrome. Circulation 103, 1269–1273 (2001). 10.1161/01.cir.103.9.1269

[4]

Bonnet, D. et al. Detection of transposition of the great arteries in fetuses reduces neonatal morbidity and mortality. Circulation 99, 916–918 (1999). 10.1161/01.cir.99.7.916

[5]

Van Velzen, C. et al. Prenatal detection of transposition of the great arteries reduces mortality and morbidity. Ultrasound Obstet. Gynecol. 45, 320–325 (2015). 10.1002/uog.14689

[6]

Morris, S. A. et al. Prenatal diagnosis, birth location, surgical center, and neonatal mortality in infants with hypoplastic left heart syndrome. Circulation 129, 285–292 (2014). 10.1161/circulationaha.113.003711

[7]

Corbett, L. et al. A practical guideline for performing a comprehensive transthoracic echocardiogram in the congenital heart disease patient: consensus recommendations from the British Society of Echocardiography. Echo Res. Pract. 9, 1–34 (2022). 10.1186/s44156-022-00006-5

[8]

Massin, M. & Dessy, H. Delayed recognition of congenital heart disease. Postgrad. Med. J. 82, 468–470 (2006). 10.1136/pgmj.2005.044495

[9]

Peterson, C. et al. Late detection of critical congenital heart disease among US infants: estimation of the potential impact of proposed universal screening using pulse oximetry. JAMA Pediatr. 168, 361–370 (2014). 10.1001/jamapediatrics.2013.4779

[10]

Lytzen, R. et al. Live-born major congenital heart disease in Denmark: incidence, detection rate, and termination of pregnancy rate from 1996 to 2013. JAMA Cardiol. 3, 829–837 (2018). 10.1001/jamacardio.2018.2009

[11]

Oster, M. E. et al. A population-based study of the association of prenatal diagnosis with survival rate for infants with congenital heart defects. Am. J. Cardiol. 113, 1036–1040 (2014). 10.1016/j.amjcard.2013.11.066

[12]

Tegnander, E. et al. Prenatal detection of heart defects in a non-selected population of 30 149 fetuses—detection rates and outcome. Ultrasound Obstet. Gynecol. 27, 252–265 (2006). 10.1002/uog.2710

[13]

Bull, C. et al. Current and potential impact of fetal diagnosis on prevalence and spectrum of serious congenital heart disease at term in the UK. The Lancet 354, 1242–1247 (1999). 10.1016/s0140-6736(99)01167-8

[14]

Landis, B. J. et al. Prenatal diagnosis of congenital heart disease and birth outcomes. Pediatr. Cardiol. 34, 597–605 (2013). 10.1007/s00246-012-0504-4

[15]

Tantchou Tchoumi, J. et al. Occurrence and pattern of congenital heart diseases in a rural area of sub-Saharan Africa: cardiovascular topics. Cardiovasc. J. Afr. 22, 63–66 (2011). 10.5830/cvja-2010-046

[16]

Krishnan, A. et al. Impact of socioeconomic status, race and ethnicity, and geography on prenatal detection of hypoplastic left heart syndrome and transposition of the great arteries. Circulation 143, 2049–2060 (2021). 10.1161/circulationaha.120.053062

[17]

Ames, S., Pillsworth, E., Sparman-Shelto, A. & Isaac, D. L. Addressing barriers to health care access of congenital heart disease patients in Guyana. Global Pediatr. Health 8, 2333794X211012977 (2021). 10.1177/2333794x211012977

[18]

Ekure, E. N. & Adeyemo, A. A. Clinical epidemiology and management of congenital heart defects in a developing country. In Congenital Heart Disease (Karger publishers, 2015). 10.1159/000375204

[19]

Bhardwaj, R. et al. Prevalence of congenital heart disease in rural population of Himachal—a population-based study. Indian Heart J. 68, 48–51 (2016). 10.1016/j.ihj.2015.08.022

[20]

Jivanji, S. G., Lubega, S., Reel, B. & Qureshi, S. A. Congenital heart disease in East Africa. Front. Pediatr. 7, 250 (2019). 10.3389/fped.2019.00250

[21]

Rashid, U., Qureshi, A. U., Hyder, S. N. & Sadiq, M. Pattern of congenital heart disease in a developing country tertiary care center: factors associated with delayed diagnosis. Ann. Pediatr. Cardiol. 9, 210 (2016). 10.4103/0974-2069.189125

[22]

Kors, J. et al. Reconstruction of the Frank vectorcardiogram from standard electrocardiographic leads: diagnostic comparison of different methods. Eur. Heart J. 11, 1083–1092 (1990). 10.1093/oxfordjournals.eurheartj.a059647

[23]

Edenbrandt, L. & Pahlm, O. Vectorcardiogram synthesized from a 12-lead ECG: superiority of the inverse Dower matrix. J. Electrocardiol. 21, 361–367 (1988). 10.1016/0022-0736(88)90113-6

[24]

Chen, J. et al. Electrocardio panorama: synthesizing new ECG views with self-supervision. In International Joint Conference on Artificial Intelligence (International Joint Conferences on Artificial Intelligence Organization, 2021). 10.24963/ijcai.2021/495

[25]

Khairy, P. & Marelli, A. J. Clinical use of electrocardiography in adults with congenital heart disease. Circulation 116, 2734–2746 (2007). 10.1161/circulationaha.107.691568

[26]

Rodriguez-Alvarez, A. et al. The vectorcardiographic equivalent of the “crochetage” of the QRS of the electrocardiogram in atrial septal defect of the ostium secundum type. Preliminary report. Am. Heart J. 58, 388–394 (1959). 10.1016/0002-8703(59)90155-3

[27]

Heller, J. et al. "crochetage” (notch) on R wave in inferior limb leads: a new independent electrocardiographic sign of atrial septal defect. J. Am. College Cardiol. 27, 877–882 (1996). 10.1016/0735-1097(95)00554-4

[28]

Cano, Ó. et al. Essential ECG clues in patients with congenital heart disease and arrhythmias. J. Electrocardiol. 50, 243–250 (2017). 10.1016/j.jelectrocard.2016.08.005

[29]

Borkon, A. M. et al. The superior QRS axis in ostium primum ASD: a proposed mechanism. Am. Heart J. 90, 215–221 (1975). 10.1016/0002-8703(75)90122-2

[30]

Rasmussen, K. & Sørland, S. J. Prediction of right ventricular systolic pressure in pulmonary stenosis from combined vectorcardiographic data. Am. Heart J. 86, 318–328 (1973). 10.1016/0002-8703(73)90040-9

[31]

Macruz, R. et al. A method for the electrocardiographic recognition of atrial enlargement. Circulation 17, 882–889 (1958). 10.1161/01.cir.17.5.882

[32]

Abrahams, D. G. & Wood, P. Pulmonary stenosis with normal aortic root. Br. Heart J. 13, 519 (1951). 10.1136/hrt.13.4.519

[33]

Marino, B. S. et al. Neurodevelopmental outcomes in children with congenital heart disease: evaluation and management: a scientific statement from the American Heart Association. Circulation 126, 1143–1172 (2012). 10.1161/cir.0b013e318265ee8a

[34]

Deep learning in ECG diagnosis: A review

Huan Wang, Zongjin Li, Lang Qin

Knowledge-Based Systems 2021 10.1016/j.knosys.2021.107187

[35]

Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi et al.

Nature Medicine 2019 10.1038/s41591-018-0268-3

[36]

Lima, E. M. et al. Deep neural network-estimated electrocardiographic age as a mortality predictor. Nat. Commun. 12, 5117 (2021). 10.1038/s41467-021-25351-7

[37]

Automatic diagnosis of the 12-lead ECG using a deep neural network

Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão et al.

Nature Communications 2020 10.1038/s41467-020-15432-4

[38]

Pasha, S. N. et al. Cardiovascular disease prediction using deep learning techniques. In IOP Conference Series: Materials Science and Engineering (2020). 10.1088/1757-899x/981/2/022006

[39]

Zhang, Z. et al. Heartbeat classification using disease-specific feature selection. Comput. Biol. Med. 46, 79–89 (2014). 10.1016/j.compbiomed.2013.11.019

[40]

Xu, M. et al. Rule-based method for morphological classification of ST segment in ECG signals. J. Med. Biol. Eng. 35, 816–823 (2015). 10.1007/s40846-015-0092-x

[41]

Sannino, G. & De Pietro, G. A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Future Gener. Comput. Syst. 86, 446–455 (2018). 10.1016/j.future.2018.03.057

[42]

Mondéjar-Guerra, V. et al. Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomed. Signal Process. Control 47, 41–48 (2019). 10.1016/j.bspc.2018.08.007

[43]

Saritha, C., Sukanya, V. & Murthy, Y. N. ECG signal analysis using wavelet transforms. Bulg. J. Phys. 35, 68–77 (2008).

[44]

Yu, S.-N. & Chen, Y.-H. Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network. Pattern Recognit. Lett. 28, 1142–1150 (2007). 10.1016/j.patrec.2007.01.017

[45]

Hoodbhoy, Z. et al. Diagnostic accuracy of machine learning models to identify congenital heart disease: a meta-analysis. Front. Artif. Intell. 4, 708365 (2021). 10.3389/frai.2021.708365

[46]

Morris, S. A. & Lopez, K. N. Deep learning for detecting congenital heart disease in the fetus. Nat. Med. 27, 764–765 (2021). 10.1038/s41591-021-01354-1

[47]

Bahado-Singh, R. O. et al. Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates. J. Matern.-Fetal Neonatal Med. 35, 457–464 (2022). 10.1080/14767058.2020.1722995

[48]

Karar, M. E., El-Khafif, S. H. & El-Brawany, M. A. Automated diagnosis of heart sounds using rule-based classification tree. J. Med. Syst. 41, 1–7 (2017). 10.1007/s10916-017-0704-9

[49]

Gavrovska, A. et al. Paediatric heart sound signal analysis towards classification using multifractal spectra. Physiol. Meas. 37, 1556 (2016). 10.1088/0967-3334/37/9/1556

[50]

2020 ESC Guidelines for the management of adult congenital heart disease

Helmut Baumgartner, Julie De Backer, Sonya V Babu-Narayan et al.

European Heart Journal 2021 10.1093/eurheartj/ehaa554

Showing 50 of 69 references

Metrics

58

Citations

69

References

Details

Published: Feb 01, 2024
Vol/Issue: 15(1)
License: View

Authors

Capital Medical University

School of Electronic Information, Wuhan University, Wuhan, China

D

DANNY Z. CHEN

Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

Ministry of Education

H

Huiying Liang

Cite This Article

Jintai Chen, Ying Zhang, Qing Chang, et al. (2024). Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts. Nature Communications, 15(1). https://doi.org/10.1038/s41467-024-44930-y

Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts

You May Also Like