Determination of left ventricular diastolic dysfunction using machine learning methods. (14th October 2021)

• Record Type: Journal Article
• Authors: Kuznetsova, N; Sagirova, Z H; Dhif, I; Gognieva, D; Gogiberidze, N; Chomakhidze, P; Kopylov, P
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Artificial Intelligence (Machine Learning, Deep Learning); Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.3051
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Introduction: Nowadays, more than 50% of new cases of heart failure (HF) are due to heart failure with preserved ejection fraction (HFpEF). Diastolic dysfunction of left ventricular underlies the development of HFpEF. Determination of diastolic function can be determinated using echocardiographic examination but not as a method of screening. Purpose: Create a Machine Learning model to determinate severe (2nd or 3rd degrees) left ventricular diastolic dysfunction (LV DD) using single channel ECG. Methods: The study prospectively included 375 patients aged between 18 and 88 years. Each of them underwent an assessment of left ventricular diastolic function using echocardiography based on ASE/EACVI guidelines and standard 2016. ECG was registered in I standard lead for 3 minutes. Then a spectral analysis of the ECG was performed using a wavelet transform based on the Fourier transform. Prediction of LV DD was implemented based on different machine learning methods. Results: Based on the time intervals between the waves, the energy of the ECG signal in the target zones of the complexes, the amplitude at different points of the ECG complexes and the asymmetry indicators, different mathematical models were created to determine the severe (2nd or 3rd degree) LV DD. Lasso regression predicted the presence of severe LV DD with sensitivity 90% and specificity 80, 6% (AUC- 0.856). The most significant for this method were the following variables: TA, SDNN, RonsF, ASEPMAX, B1, TpTe, ADP, SEPMAX, Sbeta, SEPF, RR, SRP-B0, Pan.1, QT/TQ, PpeakN, SA, SRP-B1. Random Forest also showed high sensitivity 80% and specificity 89, 3% (AUC- 0.860). For Random Forest model the most dignificant were: TpTe, VAT, QTc, QT/TQ, J80A, TA, Tenergy, Tpenergy, Beta, QRS11energy, Pan, Pan.1, RA, Pfi, QRSst, QRSfi, PpeakP, PpeakN, Rpeak, Speak, RonsF, B0-B1, SEP, SEP-B1, SEP-B0, SRP, SRP-B1, SRP-B0, ASEP. LV DD 1 degree did not have markers in our study. Conclusion: Several machine learning methods have shown high sensitivity and specificity in identifying severe LV DD. Potentially ECG can be used as a screening method for determination of the LV DD. Funding Acknowledgement: Type of funding sources: Public Institution(s). Main funding source(s): This work was financed by the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Centers "Digital biodesign and personalized healthcare" No. 075-15-2020-926