On the detection of myocardial scar based on ECG/VCG analysis

In this paper, we address the problem of detecting the presence of myocardial scar from standard ECG/VCG recordings, giving effort to develop a screening system for the early detection of scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of myocardial scar. Two of these methodologies: a.) the use of a template ECG heartbeat, from records with scar absence coupled with Wavelet coherence analysis and b.) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate an SVM classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. Classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying 10- fold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%)

A Time-domain morphology and gradient based algorithm for ECG feature extraction

A Time Domain Morphology and Gradient (TDMG) based algorithm is presented in this paper for the extraction of all the fiducial time instances from a single PQRST complex. By estimating these characteristic points, all clinically important temporal ECG parameters can be calculated. The proposed algorithm is based on a combination of extrema detection and slope information, with the use of adaptive thresholding to achieve the extraction of 11 time instances. A pre-processing step removes any noise and artefacts from the captured ECG signal. Initially, the position of the R-wave and the QRS-complex boundaries are localized in time. Following, by focusing on the part of the signal that precedes and succeeds the QRS-complex, the remaining fiducial points from the P and T waves are estimated. The initial localisation of the wave boundaries is complimented by amendment steps which are introduced to cater for atypical wave morphologies, indicative of particular heart conditions. The proposed algorithm is evaluated on the QT and PTB databases against medically annotated ECG samples. The results demonstrate the ability of the proposed scheme, to estimate the ECG fiducial points with acceptable accuracy from a single-lead ECG signal. In addition, this investigation reveals the ability of the TDMG algorithm to perform accurately irrespective of the lead chosen, the different disease categories and the sampling frequency of the captured ECG signal

'Concrete freedom' : C.L.R. James on culture and black politics

This article aims to provide a synoptic account of the cultural writings of the West Indian intellectual and activist C.L.R. James. I aim to make a case for greater recognition of his work among cultural sociologists. I go on to show how James’ original, historicising account of cultural forms relates closely to his wider political interventions including, specifically, his ground-breaking discussion of mid-twentieth century black politics in America

Risk stratification in sudden cardiac death: engineering novel solutions in heart failure

Sudden cardiac death (SCD) risk is reduced by implantable cardioverter defibrillator (ICD) use in appropriately selected patients. Established markers such as impairment of left ventricular function and QRS duration are non specific for arrhythmic death and therefore many patients receive ICD therapy from which they gain no benefit, either due to survival without arrhythmia or death from pump failure. Both myocardial scar and serum protein biomarkers have potential as SCD risk stratifiers, but novel solutions are needed to deliver non invasive tests that are suitable for point of care testing. The aims of this thesis were to explore novel assessment methods for the risk stratification of SCD, with particular focus on heart failure.Several approaches were chosen to explore these concepts: (i) meta-analysis to assess the utility of fragmented QRS, (ii) retrospective evaluation of ECG and CMR to assess ECG markers of repolarisation and (iii) QRS scoring, (iv) prospective evaluation of an automated QRS scoring algorithm to predict myocardial scar, (v) artificial intelligence machine learning techniques to develop and validate an algorithm capable to classifying ECG scar, and (vi) a novel high resolution proteomic technique to propose biomarkers of SCD risk, validated using ELISA (vii). The hypothesis is that novel clinical tools, encompassing technologies and techniques which could stretch across the clinical landscape from primary to specialised care services, can be identified as indicators of ICD benefit in patients at risk of SCD.My results indicate that simpler ECG markers such as T-peak-end, fQRS and QRS scoring have a significant association with myocardial scar, although the strength of association varies according to scar characteristics, and is not specific. The specificity of these markers for mode of death is also weak. Computerised algorithms can serve to speed up manual ECG scoring, whilst maintaining overall accuracy, but greatest potential is seen in using a novel marker, custom developed using artificial intelligence techniques. I also found that candidate serum biomarkers, predictive of death or ventricular arrhythmia, could be identified through high resolution proteomic techniques. Clinical and technical validation with ELISA is possible.Novel non invasive markers, such as serum proteins and computer ECG analysis may be valuable tools to improve risk prediction. The incremental benefit of these tools to determine prognosis, and select those who will most benefit from ICD therapy, can now be addressed by future prospective studies

Detection of myocardial scar from the VCG using a supervised learning approach

This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the in- vestigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue

Pore architecture of diatom frustules: potential nanostructured membranes for molecular and particle separations

Diatoms produce diverse three-dimensional regular silica structures with nanometer to micrometer dimensions and hold considerable promise for biological and biomimetic fabrication of nanostructured materials and devices. In the present work, we describe the ultrastructural characterization of porous structures in diatom biosilica and discuss their potential as membrane filters for diffusion based separations. The frustules of two centric diatom species, Coscinodiscus sp. and Thalassiosira eccentrica, were investigated using scanning electron microscopy and atomic force microscopy. Their morphological features, including pore size, shape, porosity, and pore organization, are described. We observed that although pore organization in frustules of Thalassiosira eccentrica and Coscinodiscus sp. is in reverse order, a striking commonality is the size range of the smallest pores in both species (around 40 nm). The consensus lower pore size suggests that frustule valves have a common function at this size of excluding viruses or other deleterious particles, and the pore size and organization is optimized for this purpose. We suggest and implement an experimental approach to study the potential of diatom frustules for diffusive separation of molecular or nanoparticular components in microfluidic or lab-on-a-chip environments

Late gadolinium enhancement cardiac magnetic resonance imaging for the prediction of ventricular tachyarrhythmic events: a meta-analysis

AIMS:Approaches to the risk stratification for sudden cardiac death (SCD) remain unsatisfactory. Although late gadolinium enhancement cardiac magnetic resonance imaging (LGE-CMR) for SCD risk stratification has been evaluated in several studies, small sample size has limited their clinical validity. We performed this meta-analysis to better gauge the predictive accuracy of LGE-CMR for SCD risk stratification.METHODS AND RESULTS:Electronic databases and published bibliographies were systematically searched to identify studies evaluating the association between the extent of LV scar on LGE-CMR and ventricular arrhythmic events [SCD, resuscitated cardiac arrest, the occurrence of ventricular arrhythmias, or appropriate implantable cardioverter defibrillator (ICD) therapy]. Only studies enrolling patients with CAD or non-ischaemic cardiomyopathy were included. Summary estimates of the relative risk (RR) and likelihood ratios (LRs) were calculated using random effects models. Eleven studies comprising 1105 patients were identified. During a mean/median follow-up of 8.5-41 months 207 patients had ventricular arrhythmic events. Ventricular arrhythmic events were more common in patients with a greater extent of LV scar: RR 4.33 [95% confidence interval (CI) 2.98-6.29], positive LR 1.98 (95% CI 1.66-2.37), and negative LR 0.33 (95% CI 0.24-0.46).CONCLUSION:The extent of LGE on CMR is strongly associated with the occurrence of ventricular arrhythmias in patients with reduced LVEF and may be a valuable risk stratification tool for identifying patients who will benefit from ICD therapy. However, uncertainties regarding clinical application persist and need to be addressed prior to introduction into broad clinical practice

Can QRS scoring predict left ventricular scar and clinical outcomes?

AIMS: Identifying patients with potential to benefit from implantable cardioverter defibrillator (ICD) therapy is challenging. Myocardial scar detected using cardiovascular myocardial resonance imaging with late gadolinium enhancement (CMR-LGE) is associated with ventricular arrhythmia. Its use is constrained due to limited availability, unlike electrocardiogram (ECG) which is widely available. Selvester QRS scoring detects scar, although the reported performance varies. The study aims were to determine whether QRS score (a) detects scar (b) varies with scar characteristics, and (c) can meaningfully predict sudden cardiac death.METHODS AND RESULTS:We investigated 64 consecutive ICD recipients (age 66 ± 11 years, 80% male, median left ventricular ejection fraction 30%) with coronary artery disease who had undergone CMR-LGE prior to device implantation, over 4 years in a single centre (2006-2009). A modified QRS score was measured on the ECG performed prior to ICD implantation. Clinical end points were (i) appropriate ICD therapy and (ii) all cause mortality. QRS score was associated with CMR scar (r = 0.42, P = 0.001) and scar surface area (r = 0.41, P = 0.001), but not subendocardial scar. Strongest correlation was seen in those patients with transmural scar only (r = 0.62, P = 0.01). During 42 ± 13 months follow-up, QRS score was not predictive of appropriate ICD therapy, but was significantly related to all cause mortality (hazard ratio = 1.16; confidence interval = 1.03-1.30; P = 0.01).CONCLUSION:QRS scoring performed best in quantifying transmural scar, and shows association with medium-term mortality risk, but not with risk of ventricular arrhythmia. It may be that the score is best suited as a risk stratifier of those with least potential to benefit from IC