Monitoring of cardiovascular function on a beat-to-beat basis is fundamental for protecting patients in different settings including emergency medicine and interventional cardiology, but still faces technical challenges and several limitations. In the present study, we propose a new method for the extraction of cardiovascular performance surrogates from analysis of the photoplethysmographic (PPG) signal alone.We propose using a multi-Gaussian (MG) model consisting of five Gaussian functions to decompose the PPG pulses into its main physiological components. From the analysis of these components, we aim to extract estimators of the left ventricular ejection time, blood pressure and vascular tone changes. Using a multi-derivative analysis of the components related with the systolic ejection, we investigate which are the characteristic points that best define the left ventricular ejection time (LVET). Six LVET estimates were compared with the echocardiographic LVET in a database comprising 68 healthy and cardiovascular diseased volunteers. The best LVET estimate achieved a low absolute error (15.41 ± 13.66 ms), and a high correlation (ρ = 0.78) with the echocardiographic reference.To assess the potential use of the temporal and morphological characteristics of the proposed MG model components as surrogates for blood pressure and vascular tone, six parameters have been investigated: the stiffness index (SI), the T1_d and T1_2 (defined as the time span between the MG model forward and reflected waves), the reflection index (RI), the R1_d and the R1_2 (defined as their amplitude ratio). Their association to reference values of blood pressure and total peripheral resistance was investigated in 43 volunteers exhibiting hemodynamic instability. A good correlation was found between the majority of the extracted and reference parameters, with an exception to R1_2 (amplitude ratio between the main forward wave and the first reflection wave), which correlated low with all the reference parameters. The highest correlation ([Formula: see text] = 0.45) was found between T1_2 and the total peripheral resistance index (TPRI); while in the patients that experienced syncope, the highest agreement ([Formula: see text] = 0.57) was found between SI and systolic blood pressure (SBP) and mean blood pressure (MBP).In conclusion, the presented method for the extraction of surrogates of cardiovascular performance might improve patient monitoring and warrants further investigation.
Artificial intelligence (AI) is developing rapidly in the medical technology field, particularly in image analysis. ECG-diagnosis is an image analysis in the sense that cardiologists assess the waveforms presented in a 2-dimensional image. We hypothesized that an AI using a convolutional neural network (CNN) may also recognize ECG images and patterns accurately. We used the PTB ECG database consisting of 289 ECGs including 148 myocardial infarction (MI) cases to develop a CNN to recognize MI in ECG. Our CNN model, equipped with 6-layer architecture, was trained with training-set ECGs. After that, our CNN and 10 physicians are tested with test-set ECGs and compared their MI recognition capability in metrics F1 (harmonic mean of precision and recall) and accuracy. The F1 and accuracy by our CNN were significantly higher (83 ± 4%, 81 ± 4%) as compared to physicians (70 ± 7%, 67 ± 7%, P < 0.0001, respectively). Furthermore, elimination of Goldberger-leads or ECG image compression up to quarter resolution did not significantly decrease the recognition capability. Deep learning with a simple CNN for image analysis may achieve a comparable capability to physicians in recognizing MI on ECG. Further investigation is warranted for the use of AI in ECG image assessment.
BackgroundSubclinical atrial fibrillation is one possible cause of embolic stroke of undetermined source (ESUS). It remains to be elucidated if a specific infarction site has a predictive value for detecting subclinical atrial fibrillation. We aimed to investigate the predictive value of infarction site in patients with ESUS for the detection of atrial tachyarrhythmia (AT) using an insertable cardiac monitor.Methods and ResultsConsecutive 146 patients (84 men; aged 62±12 years) underwent insertable cardiac monitor implantation after diagnosis of ESUS. The detection of AT >30 seconds was evaluated. The ESUS infarction sites were categorized into internal carotid artery and vertebral artery (VA) territories, with ophthalmic artery, anterior cerebral artery, and middle cerebral artery as internal carotid artery subterritories, and posterior cerebral artery and other vertebrobasilar arteries as VA subterritories. During a median follow‐up of 387 days, AT was detected in 33 patients (23%). Subclinical AT detection was significantly more frequent after VA territorial infarction opposed to internal carotid artery infarction (20/57 [35%] versus 13/89 [15%]; P=0.0039). Kaplan‐Meier analysis demonstrated a significantly higher AT detection rate after VA infarction (log‐rank, P=0.0076). Regression analysis revealed that VA territorial infarction, and particularly posterior cerebral artery area infarction, was an independent predictor of AT detection.ConclusionsPatients with ESUS in the posterior cerebral artery territory had a higher rate of subclinical AT detection than those with other infarct localizations. Our data suggest that the possible usefulness of ESUS site to identify candidates for direct oral anticoagulation should be confirmed in future research.
SummaryPulmonary vein isolation (PVI) is a cornerstone therapy in patients with atrial fibrillation (AF). With increasing numbers of PVI procedures, demand arises to reduce the cumulative fluoroscopic radiation exposure for both the physician and the patient. New technologies are emerging to address this issue. Here, we report our first experiences with a new fluoroscopy integrating technology in addition to a current 3D-mapping system. The new fluoroscopy integrating system (FIS) with 3D-mapping was used prospectively in 15 patients with AF. Control PVI cases (n = 37) were collected retrospectively as a complete series. Total procedure time (skin to skin), fluoroscopic time, and dose-area-product (DAP) data were analyzed. All PVI procedures were performed by one experienced physician using a commercially available circular multipolar irrigated ablation catheter. All PVI procedures were successfully undertaken without major complications. Baseline characteristics of the two groups showed no significant differences. In the group using the FIS, the fluoroscopic time and DAP were significantly reduced from 571 ± 187 seconds versus 1011 ± 527 seconds (P = 0.0029) and 4342 ± 2073 cGycm 2 versus 6208 ± 3314 cGycm 2 (P = 0.049), respectively. Mean procedure time was not significantly affected and was 114 ± 31 minutes versus 104 ± 24 minutes (P = 0.23) by the FIS.The use of the new FIS with the current 3D-mapping system enables a significant reduction of the total fluoroscopy time and DAP compared to the previous combination of 3D-mapping system plus normal fluoroscopy during PVI utilizing a circular multipolar irrigated ablation catheter. However, the concomitant total procedure time is not affected. Thus, the new system reduces the radiation exposure for both the physicians and patients. (Int Heart J 2016; 57: 299-303)
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