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.

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Highlights. The article presents a novel and unique method for assessment of left ventricular systolic disfunction using electrocardiography and photoplethysmography data. This method will improve and simplify the detection of cardiovascular diseases.Aim. To evaluate left ventricular (LV) systolic function using electrocardiogram (ECG) and photoplethysmogram (PPG) signals recorded by a single-channel ECG and PPG-based monitor.Methods. The prospective study included 489 patients over 18 years old with various cardiovascular diseases. All participants underwent echocardiography to determine the main indicators of LV systolic function: LV ejection fraction (EF), LV outflow tract velocity time integral (LVOT VTI), and global longitudinal strain (GLS). Moreover, all patients underwent 1-lead ECG and PPG recording using a single-channel ECG and PPG-based monitor (CardioQvark). The obtained data were analyzed, and ROC curve analysis was performed.Results. We have identified ECG and PPG parameters associated with a decrease in LV contractile function. During the analysis, the ECG, T-wave amplitude (TA) and RonsF parameters showed the highest diagnostic accuracy. With EF below 55%, the area under the ROC curve (AUC) was 0.822, sensitivity (Se) 80%, specificity (Sp) 69% in EF below 55% in TA; in RonsF AUC was 0.743, Se 81%, Sp 77%. With EF below 40%, AUC was 0.915, Se 85%, Sp 83% in TA, and in RonsF AUC was 0.844, Se 82%, Sp 82%. Diagnostic accuracy of ECG signals in case of LVOT VTI lower than 16 cm was measured: TA (AUC 0.755, Se 82%, Sp 70%), RonsF (AUC 0.620, Se 77%, Sp 72%). PPG signals were not significantly associated with reduced EF; however, the pulse wave parameters were associated with lower LVOT VTI: in DP-B0 AUC was 0.687, Se 71%, Sp 74%. The combination of ECG and PPG signals was significantly associated with EF below 40% (RonsF * DP-SEP (AUC 0.877, Se 86%, Sp 85%). ECG and PPG signals were not associated with LV GLS.Conclusion. Assessment of LV systolic function can be performed by analyzing ECG and PPG signals recorded using a portable single-channel CardioQvark monitor.

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