Left Ventricular Diastolic Dysfunction Screening by a Smartphone-Case Based on Single Lead ECG

Natalia Kuznetsova; Anastasiia Gubina; Zhanna Sagirova; Ines Dhif; Daria Gognieva; Anna Melnichuk; Oleg Orlov; Elena Syrkina; Vsevolod Sedov; Petr Chomakhidze; Hugo Saner; Philippe Kopylov

doi:10.1177/11795468221120088

Left Ventricular Diastolic Dysfunction Screening by a Smartphone-Case Based on Single Lead ECG

Clin Med Insights Cardiol. 2022 Aug 23:16:11795468221120088. doi: 10.1177/11795468221120088. eCollection 2022.

Authors

Natalia Kuznetsova^{1

2}, Anastasiia Gubina², Zhanna Sagirova², Ines Dhif², Daria Gognieva^{1

2}, Anna Melnichuk², Oleg Orlov², Elena Syrkina², Vsevolod Sedov², Petr Chomakhidze^{1

2}, Hugo Saner^{1

3}, Philippe Kopylov^{1

2}

Affiliations

¹ World-Class Research Center "Digital Biodesign and Personalized Healthcare" Sechenov First Moscow State Medical University, Moscow, Russia.
² Department of Cardiology, Functional and Ultrasound Diagnostics of N.V. Sklifosovsky Institute for Clinical Medicine Sechenov First Moscow State Medical University, Moscow, Russia.
³ ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Abstract

Aims: To investigate the potential of a signal processed by smartphone-case based on single lead electrocardiogram (ECG) for left ventricular diastolic dysfunction (LVDD) determination as a screening method.

Methods and results: We included 446 subjects for sample learning and 259 patients for sample test aged 39 to 74 years for testing with 2D-echocardiography, tissue Doppler imaging and ECG using a smartphone-case based single lead ECG monitor for the assessment of LVDD. Spectral analysis of ECG signals (spECG) has been used in combination with advanced signal processing and artificial intelligence methods. Wavelengths slope, time intervals between waves, amplitudes at different points of the ECG complexes, energy of the ECG signal and asymmetry indices were analyzed. The QTc interval indicated significant diastolic dysfunction with a sensitivity of 78% and a specificity of 65%, a Tpeak parameter >590 ms with 63% and 58%, a T value off >695 ms with 63% and 74%, and QRSfi > 674 ms with 74% and 57%, respectively. A combination of the threshold values from all 4 parameters increased sensitivity to 86% and specificity to 70%, respectively (OR 11.7 [2.7-50.9], P < .001). Algorithm approbation have shown: Sensitivity-95.6%, Specificity-97.7%, Diagnostic accuracy-96.5% and Repeatability-98.8%.

Conclusion: Our results indicate a great potential of a smartphone-case based on single lead ECG as novel screening tool for LVDD if spECG is used in combination with advanced signal processing and machine learning technologies.

Keywords: ECG; artificial intelligence; left ventricular diastolic dysfunction; machine learning; signal processing; spectral analysis.