Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV

Integration of Attributes from Non-Linear Characterization of Cardiovascular Time-Series for Prediction of Defibrillation Outcomes

Objective The timing of defibrillation is mostly at arbitrary intervals during cardio-pulmonary resuscitation (CPR), rather than during intervals when the out-of-hospital cardiac arrest (OOH-CA) patient is physiologically primed for successful countershock. Interruptions to CPR may negatively impact defibrillation success. Multiple defibrillations can be associated with decreased post-resuscitation myocardial function. We hypothesize that a more complete picture of the cardiovascular system can be gained through non-linear dynamics and integration of multiple physiologic measures from biomedical signals. Materials and Methods Retrospective analysis of 153 anonymized OOH-CA patients who received at least one defibrillation for ventricular fibrillation (VF) was undertaken. A machine learning model, termed Multiple Domain Integrative (MDI) model, was developed to predict defibrillation success. We explore the rationale for non-linear dynamics and statistically validate heuristics involved in feature extraction for model development. Performance of MDI is then compared to the amplitude spectrum area (AMSA) technique. Results 358 defibrillations were evaluated (218 unsuccessful and 140 successful). Non-linear properties (Lyapunov exponent \u3e 0) of the ECG signals indicate a chaotic nature and validate the use of novel non-linear dynamic methods for feature extraction. Classification using MDI yielded ROC-AUC of 83.2% and accuracy of 78.8%, for the model built with ECG data only. Utilizing 10-fold cross-validation, at 80% specificity level, MDI (74% sensitivity) outperformed AMSA (53.6% sensitivity). At 90% specificity level, MDI had 68.4% sensitivity while AMSA had 43.3% sensitivity. Integrating available end-tidal carbon dioxide features into MDI, for the available 48 defibrillations, boosted ROC-AUC to 93.8% and accuracy to 83.3% at 80% sensitivity. Conclusion At clinically relevant sensitivity thresholds, the MDI provides improved performance as compared to AMSA, yielding fewer unsuccessful defibrillations. Addition of partial end-tidal carbon dioxide (PetCO2) signal improves accuracy and sensitivity of the MDI prediction model

Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction

© Springer International Publishing Switzerland 2015. In this paper, we present the results of classifying electroencephalographic (EEG) signals into four motor imagery tasks using a new method for feature extraction. This method is based on a signal-dependent orthogonal transform, referred to as LP-SVD, defined as the left singular vectors of the LPC filter impulse response matrix. Using a logistic tree based model classifier, the extracted features are mapped into one of four motor imagery movements, namely left hand, right hand, foot, and tongue. The proposed technique-based classification performance was benchmarked against those based on two widely used linear transform for feature extraction methods, namely discrete cosine transform (DCT) and adaptive autoregressive (AAR). By achieving an accuracy of 67.35 %, the LP-SVD based method outperformed the other two by large margins (+25 % compared to DCT and +6 % compared to AAR-based methods)

Applications of Signal Analysis to Atrial Fibrillation

This work was supported by projects TEC2010–20633 from the Spanish Ministry of Science and Innovation and PPII11–0194–8121 from Junta de Comunidades de Castilla-La ManchaRieta Ibañez, JJ.; Alcaraz Martínez, R. (2013). Applications of Signal Analysis to Atrial Fibrillation. En Atrial Fibrillation - Mechanisms and Treatment. InTech. 155-180. https://doi.org/10.5772/5340915518

Heart rate variability study using phase response curve

A noninvasive phase resetting experiment on human subjects was investigated. The phase response curve was estimated and was used to demonstrate cardiac phase resetting due to a vagal input. The estimated running phase response curve showed that the cardiac cycle resetting depended on the time and the amplitude of the vagal stimulation. The phase response curve was then studied using time circle analysis, topological analysis and nonlinear dynamics analysis. Also phase entrainment and stimulus frequency dependence of the phase response were evaluated. Further, the Van Der Pol model, Generalized Additive model and Knight and Peskin\u27s model were used to simulate the phase resetting process so that the characteristics of the phase resetting can be better understood

Applications of Vectorcardiography for Diagnosis and Risk Stratification in Subpopulations at Risk for Life-Threatening Arrhythmias

Introduction: Vectorcardiography, or 3-dimensional electrocardiography is a tool which can be used to identify subtle changes in the electrical forces of the heart, and which can be applied to atrial depolarization, ventricular depolarization and ventricular repolarization for prognostic and diagostic purposes. Methods: Kor’s regression-related and quasi orthogonal methods was used to derive vectorcardiographic parameters from the 12-lead electrocardiogram and applied to a cohort of cryptogenic stroke patients to assess atrial fibrillation, hypertrophic cardiomyopathy patients to assess for ventricular arrhythmias, applied with right-precordial directed quasi orthogonal method to arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC/D) patients for diagnosis, and applied to ventricular repolarization only to patients with genotype-positive/phenotype-negative Long QT2 syndrome (KNCH2 mutation) to assess for cardiac events. Parametric and non-parameteric parameters were presented as mean ± standard deviation and median (1st to 3rd interquartile ranges). Pearson and Spearman correlation coefficients were used for parametric and non-parametric data, respectively. Odds ratios with univariate and multivariate analyses as well as hazard ratios and Kaplan-Meier curves are presented. P-values under 0.05 were represented as significant. Results: In cryptogenic stroke patients, first atrial fibrillation event was predicted by baseline P-wave duration divided by P-wave vector magnitude (p<0.05). In hypertrophic cardiomyopathy patients, the spatial peaks QRS-T angle differentiated sustained ventricular arrhythmias (VA) from no VA (P < 0.001) and at 124.1 degrees gave positive and negative predictive values and an odds ratio of 36.7%, 96.1%, and 14.2 (95% confidence interval: 3.1-65.6), respectively. Combined right precordial-directed parameters were able to identify ARVD/C patients who otherwise met criteria but did not meet any ECG-specfiic 2010 Taskforce criteria from controls with a positive predictive value of 90.0% and negative predictive value of 83.3%. In patients with genotype positive KCNH2 mutations, without prolongation of the QTc, when dichotomized by the median of 0.30 mV, a low T-wave vector magnitude (TwVM) was associated with elevated cardiac event risk compared to those with high TwVM (HR=2.55, 95%CI 1.07-6.04, p=0.034) and the genotype-negative family members (HR=2.64, 95%CI 1.64-4.24, p<0.001). Conclusion: Vector magnitudes and spatial angles, involving atrial and ventricular depolarization as well as ventricular repolarization, can be helpful in identifying disease as well as first-onset arrhythmia in subpopulations at risk for sudden death or stroke