Entropy-based reliable non-invasive detection of coronary microvascular dysfunction using machine learning algorithm

Purpose: Coronary microvascular dysfunction (CMD) is emerging as an important cause of myocardial ischemia, but there is a lack of a non-invasive method for reliable early detection of CMD. Aim: To develop an electrocardiogram (ECG)-based machine learning algorithm for CMD detection that will lay the groundwork for patient-specific non-invasive early detection of CMD. Methods: Vectorcardiography (VCG) was calculated from each 10-second ECG of CMD patients and healthy controls. Sample entropy (SampEn), approximate entropy (ApEn), and complexity index (CI) derived from multiscale entropy were extracted from ST-T segments of each lead in ECGs and VCGs. The most effective entropy subset was determined using the sequential backward selection algorithm under the intra-patient and inter-patient schemes, separately. Then, the corresponding optimal model was selected from eight machine learning models for each entropy feature based on five-fold cross-validations. Finally, the classification performance of SampEn-based, ApEn-based, and CI-based models was comprehensively evaluated and tested on a testing dataset to investigate the best one under each scheme. Results: ApEn-based SVM model was validated as the optimal one under the intra-patient scheme, with all testing evaluation metrics over 0.8. Similarly, ApEn-based SVM model was selected as the best one under the intra-patient scheme, with major evaluation metrics over 0.8. Conclusions: Entropies derived from ECGs and VCGs can effectively detect CMD under both intra-patient and inter-patient schemes. Our proposed models may provide the possibility of an ECG-based tool for non-invasive detection of CMD

Novel technique for ST-T interval characterization in patients with acute myocardial ischemia

Background The novel signal processing techniques have allowed and improved the use of vectorcardiography (VCG) to diagnose and characterize myocardial ischemia. Herein, we studied vectorcardiographic dynamic changes of ventricular repolarization in 80 patients before (control) and during Percutaneous Transluminal Coronary Angioplasty (PTCA). Methods We propose four vectorcardiographic ST-T parameters, i.e., (a) ST Vector Magnitude Area (aSTVM); (b) T-wave Vector Magnitude Area (aTVM); (c) ST-T Vector Magnitude Difference (ST-TVD), and (d) T-wave Vector Magnitude Difference (TVD). For comparison, the conventional ST-Change Vector Magnitude (STCVM) and Spatial Ventricular Gradient (SVG) were also calculated. Results Our results indicate that several vectorcardiographic parameters show significant differences (p-value<0.05) before starting and during PTCA. Statistical minute-by-minute PTCA comparison against the control situation showed that ischemic monitoring reached a sensitivity=90.5% and a specificity=92.6% at the 5th minute of the PTCA, when aSTVM and ST-TVD were used as classifiers. Conclusions We conclude that the sensitivity and specificity for acute ischemia monitoring could be increased with the use of only two vectorcardiographic parameters. Hence, the proposed technique based on vectorcardiography could be used in addition to the conventional ST-T analysis for better monitoring of ischemic patients.Fil: Correa, Raúl. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; ArgentinaFil: Arini, Pedro David. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; ArgentinaFil: Correa, Lorena. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; ArgentinaFil: Valentinuzzi, Max. Universidad de Buenos Aires. Facultad de Ingenieria. Instituto de Ingeniería Biomédica; ArgentinaFil: Laciar, Eric. Universidad Nacional de San Juan. Facultad de Ingenieria. Departamento de Electronica y Automatica. Gabinete de Tecnologia Medica; Argentin