Kuznetsova, Natalia; Gubina, Anastasiia; Sagirova, Zhanna; Dhif, Ines; Gognieva, Daria; Melnichuk, Anna; Orlov, Oleg; Syrkina, Elena; Sedov, Vsevolod; Chomakhidze, Petr; Saner, Hugo; Kopylov, Philippe (2022). Left Ventricular Diastolic Dysfunction Screening by a Smartphone-Case Based on Single Lead ECG. Clinical Medicine Insights. Cardiology, 16, p. 11795468221120088. Sage 10.1177/11795468221120088
|
Text
11795468221120088.pdf - Published Version Available under License Creative Commons: Attribution-Noncommercial (CC-BY-NC). Download (941kB) | Preview |
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.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Gerontechnology and Rehabilitation |
UniBE Contributor: |
Saner, Hugo Ernst |
Subjects: |
600 Technology > 610 Medicine & health |
ISSN: |
1179-5468 |
Publisher: |
Sage |
Language: |
English |
Submitter: |
Pubmed Import |
Date Deposited: |
05 Sep 2022 10:33 |
Last Modified: |
05 Dec 2022 16:23 |
Publisher DOI: |
10.1177/11795468221120088 |
PubMed ID: |
36046179 |
Uncontrolled Keywords: |
ECG artificial intelligence left ventricular diastolic dysfunction machine learning signal processing spectral analysis |
BORIS DOI: |
10.48350/172632 |
URI: |
https://boris.unibe.ch/id/eprint/172632 |
Actions (login required)
 |
Edit item |