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Aims Risk stratification of sudden cardiac death (SCD) is challenging. Fragmented QRS (fQRS) is proposed as a non-invasive electrocardiogram marker associated with mortality and SCD. Results from individual studies including small numbers of patients are discrepant. We therefore performed a meta-analysis of studies evaluating fQRS as a risk stratification tool to predict all-cause mortality and SCD. Methods and results Electronic databases and bibliographies were systematically searched (1996–2014). Twelve studies (5009 patients) recruiting patients with coronary artery disease or non-ischaemic cardiomyopathy met our inclusion criteria. Fragmented QRS was associated with an all-cause mortality relative risk of 1.71 (CI 1.02–2.85) and a relative risk of SCD of 2.20 (CI 1.05–4.62). Subgroup analysis demonstrated greater mortality and SCD risk in those with left ventricular ejection fraction >35% and SCD risk in those with QRS duration

The Selvester score is an effective means for estimating the extent of myocardial scar in a patient from lowcost ECG recordings. Automation of such a system is deemed to help implementing low-cost high-volume screening mechanisms of scar in the primary care. This article describes, for the first time to the best of our knowledge, an automated implementation of the updated Selvester scoring system for that purpose, where fractionated QRS morphologies and patterns are identified and classified using a novel Stationary Wavelet Transform (SWT) based fractionation detection algorithm. This stage informs the two principal steps of the updated Selvester scoring scheme - the confounder classification and the point awarding rules. The complete system is validated on 51 ECG records of patients detected with ischemic heart disease. Validation has been carried out using manually detected confounder classes and computation of the actual score by expert cardiologists as the ground truth. Our results show that as a stand-alone system it is able to classify different confounders with 94.1% accuracy whereas it exhibits 94% accuracy in computing the actual score. When coupled with our previously proposed automated ECG delineation algorithm, that provides the input ECG parameters, the overall system shows 90% accuracy in confounder classification and 92% accuracy in computing the actual score and thereby showing comparable performance to the stand-alone system proposed here, with the added advantage of complete automated analysis without any human intervention.

In this paper, we address the problem of detecting the presence of a myocardial scar from the standard electrocardiogram (ECG)/vectorcardiogram (VCG) recordings, giving effort to develop a screening system for the early detection of the 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 the myocardial scar. Two of these methodologies are: 1) the use of a template ECG heartbeat, from records with scar absence coupled with wavelet coherence analysis and 2) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate a support vector machine classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. The classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying tenfold 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%).

Spontaneous coronary artery dissection is a rare cause of acute presentations to the catheter laboratory. Often, the angiographic findings are subtle and may be mistaken for a plaque rupture. We descibe a case where repeat presentation revealed the diagnosis of recurrent spontaneous coronary artery dissection.

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.

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.

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 ICD.

This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the discrete wavelet transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time- and frequency-domain signal processing. Feature extraction results from 27 ECG signals from QTDB were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2.423N+214 additions and 1.093N+12 multiplications for N ≤ 861 or 2.553N+102 additions and 1.093N+10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems.

Aims Sudden cardiac death (SCD) risk can be managed by implantable cardioverter defibrillators (ICD). Defibrillation shocks can be delivered via ICD generator and/or intracardiac or subcutaneous coil configurations. We present our single-centre use of childhood ICDs. Methods and results Twenty-three patients had ICD implantation, with median age and weight of 12.96 years and 41.35 kg. Indications included eight long QT; four hypertrophic cardiomyopathy; three Brugada syndrome; two idiopathic ventricular fibrillation; two post-congenital heart repair; two family history of SCD with abnormal repolarization; one catecholaminergic polymorphic ventricular tachycardia; and one left ventricle non-compaction. Twelve had out of hospital cardiac arrests prior to implantation. Techniques included 13 conventional ICD implants (pre-pectoral device with endocardial leads), 7 with subcutaneous defibrillation coils (sensing via epicardial or endocardial leads tunnelled to the ICD), and 3 with exclusive subcutaneous ICD (sensing and defibrillation via the same subcutaneous lead). Satisfactory defibrillation efficacy and ventricular arrhythmia sensing was confirmed at implantation. Follow-up ranged from 0.17 to 11.08 years. One child died with the ICD in situ . Ten children received appropriate shocks; five on more than one occasion. Five received inappropriate shocks (for inappropriate recognition of sinus tachycardia or supraventricular tachycardia). Five children underwent six further interventions; all had intracardiac leads. Conclusion Innovative shock delivery systems can be used in children requiring an ICD. The insertion technique and device used need to accommodate the age and weight of the child, and concomitant need for pacing therapy. We have demonstrated effective defibrillation with shocks delivered via configurations employing subcutaneous coils in children.

Delayed Enhancement Magnetic Resonance Imaging can be used to non-invasively differentiate viable from non-viable myocardium within the Left Ventricle in patients suffering from myocardial diseases. Automated segmentation of scarified tissue can be used to accurately quantify the percentage of myocardium affected. This paper presents a method for cardiac scar detection and segmentation based on supervised learning and level set segmentation. First, a model of the appearance of scar tissue is trained using a Support Vector Machines classifier on image-derived descriptors. Based on the areas detected by the classifier, an accurate segmentation is performed using a segmentation method based on level sets.

INTRODUCTION: The extent of left ventricular (LV) scar, characterized by late gadolinium enhancement cardiac MRI (LGE-CMR), has been shown to predict the occurrence of ventricular arrhythmias in implantable cardioverter defibrillator (ICD) recipients. However, the specificity of LGE-CMR for sudden cardiac death (SCD) versus non-SCD is unclear. The aim of this retrospective, observational study was to evaluate this relationship in a cohort of ICD recipients. METHODS AND RESULTS: We included consecutive patients who had undergone LGE-CMR before ICD implantation over a 4-year period (2006-2009). Scar (defined as myocardium with a signal intensity ?50% of the maximum in scar tissue) was characterized in terms of percent scar and number of transmural LV scar segments in a 17-segment model. The endpoints were appropriate ICD therapy and all-cause mortality. Sixty-four patients (average age 66 ± 11 years, 51 male, median LVEF 30%) were included. During 42 ± 13 months follow-up, appropriate ICD therapy occurred in 28 patients (44%), and 14 patients (22%) died. Number of transmural scar segments (P = 0.005) and percentage LV scar (P = 0.03) were both significantly associated with appropriate ICD therapy. However, neither number of transmural scar segments (P = 0.32) or percent LV scar (P = 0.59) was significantly associated with all-cause mortality. CONCLUSION: In this observational study, in medium-term follow-up, the extent of LV scar characterized by LGE-CMR was strongly associated with the occurrence of spontaneous ventricular arrhythmias but not all-cause mortality. We hypothesize that scar quantification by LGE-CMR may be more specific for SCD than non-SCD, and may prove a valuable tool for the selection of patients for ICD therapy.

The ability to predict mode, as well as risk, of death in left ventricular systolic dysfunction (LVSD) is important, as the clinical and cost-effectiveness of implantable cardioverter defibrillators (ICD) therapy depends on its use in appropriately selected patient populations. The value of a proteomic approach in identifying prognostic biomarkers in LVSD is unknown. The aims of this pilot study were to use proteomic techniques to identify serum biomarkers associated with LVSD and to prospectively explore their association with prognosis.Serum was analysed by surface-enhanced laser desorption ionisation time-of-flight mass spectrometry (SELDI-TOF MS) in patients with (n=78) and without (n=45) systolic heart failure (SHF). Spectra were compared to identify differentially expressed signal peaks as potential biomarker indicators. The ability of these peaks to predict all-cause mortality and survival with appropriate ICD therapy was then tested prospectively in patients with ICDs, on the background of LVSD (n=141).For the identification stage spectra (2-200 kDa) from SHF and control patients were randomly separated into two equally sized discovery and validation sets. Six protein peaks were identified that were differentially expressed in SHF in both sets. In the prospective phase, during a mean follow-up of 15±3 months, 11 patients died and 39 survived with appropriate ICD therapy. Five out of the six proteomic biomarkers predicted all-cause mortality but none predicted appropriate ICD therapy.These results provide proof-of-principle and are supportive of the SELDI proteomic approach as a high-throughput screening tool in identifying potentially prognostic protein peaks in patients with LVSD.

Aims The markers of ventricular repolarization corrected QT interval (QTc), QT dispersion (QTD) and Tpeak-to-Tend interval (Tpeak-end) have shown an association with sudden cardiac death (SCD) in the general population. However, their mechanistic relationship with SCD is unclear. The study aim was to evaluate the relationship between QTc, QTD, and Tpeak-end, and the extent and distribution of left ventricular (LV) scar in patients with coronary artery disease at high SCD risk. Methods and results We included 64 consecutive implantable cardioverter defibrillator (ICD) recipients (66 ± 11 years, 80% male, median left ventricular ejection fraction 30%) who had undergone late gadolinium enhancement cardiac magnetic resonance (CMR) imaging prior to device implantation over 4 years. Scar was quantified using the CMR images and characterized in terms of percent LV scar and number of LV segments with subendocardial/transmural scar. Repolarization parameters were measured on an electrocardiogram performed prior to ICD implantation. After adjustment for potential confounders there was a strong association between the number of limited subendocardial (1–25% transmurality) scar segments and QTc ( P = 0.003), QTD ( P = 0.002), and Tpeak-end ( P = 0.008). However, there was no association between the repolarization parameters and percent LV scar or the amount of transmural scar. During a mean follow-up of 19 ± 10 months 19 (30%) patients received appropriate ICD therapy, but none of the repolarization parameters were associated with its occurrence. Conclusion In this pilot study there was a strong association between limited subendocardial LV scar and prolonged QTc, QTD, and Tpeak-end. However, there was no association between any of these repolarization markers and the delivery of appropriate ICD therapy.

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.

912 Predictors for arrhythmic and major adverse cardiovascular events in patients with hypertrophic cardiomyopathy {#article-title-2} Introduction: Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) is useful for in vivo assessment of myocardial fibrosis, perfusion defect, volumetric factors. However, the clinical implication of these parameters of LGE-CMR in hypertrophic cardiomyopathy (HCM) remains unclear. Methods: Relation between LGE-CMR parameters and Major adverse events (MAEs) was evaluated in 310 consecutive patients with HCM (57.5 ± 13.4 years old, male: female =241:69). The extent of left ventricular (LV) LGE was scored. MAEs included sustained ventricular tachyarrhythmia (VTA), atrial fibrillation (AF), ischemic stroke, hospitalization for heart failure (HF) and cardiac death. Results: During the 49 ± 45 month follow-up, MAEs were noted in 73 (23.5%) of 310 patients with HCM. Arrhythmic events were observed in 46 patients (14.8%); AF in 35 and defibrillator implantation due to sustained VTA in 11 patients, respectively. Twenty two patients (7.1%) developed ischemic stroke and 15 patients (4.8%) were hospitalized for HF treatment. Patients with supramedian LGE score were more frequently associated with MAEs (29% versus 19%, P=0.033) or with arrhythmic events (18% versus 12%, P=0.09). LGE extent (odds ratio 1.14, 95% CI 1.01 to 1.30, P=0.043) and hypertension (odds ratio 2.22, 95% CI 1.13 to 4.35, P=0.021) were independent predictors for MAEs in multivariate analysis adjusted by LV maximal thickness, LV dimensions, and LV ejection fraction. Hypertension was also independently associated with total arrhythmic events (P

Introduction The ability to predict arrhythmia risk in patients with LVSD is important, as the clinical and cost-effectiveness of implantable cardioverter defibrillator (ICD) therapy depends on its use in appropriately selected patient populations. Individual biomarkers can be powerful predictors of prognosis, but studies are limited to small marker panels, chosen a priori. Mass spectroscopy based proteomic techniques can powerfully and simultaneously demonstrate differential candidate proteins in an unbiased fashion but resolving power can be limited by high serum abundant proteins. Depletion methods can overcome this, but often result in coremoval of potential biomarkers too. The aim of the study was to utilise a novel wide spectrum technique to identify candidate biomarkers from the whole proteome associated with arrhythmia outcomes. Methods Consecutive patients attending device follow up clinic were recruited to the study. Serum was collected and stored at −80°C. The occurrence of prespecified arrhythmia end points and death was recorded. Sera were then pooled according to clinical categories occurring during follow up. Grouped sera underwent stable isotope iTRAC labelling, and following protein fractionation and immunodepletion, samples underwent simultaneous tandem mass spectroscopy followed by data processing and identification of differentially expressed protein peaks p Results 243 patients (54% male, age 71±6) were included in the analysis. During follow up of 40 months, there were eight cardiovascular deaths. 25 experienced VT>182 bpm or VF, whilst 48 never experienced VT at any rate/VF (controls). 634 proteins were identified by this method. When compared to the control group, proteins had significant differential expression if twofold up- or down-regulated. Overall, 94 proteins were differentially expressed in those who died or experienced VT>182 or VF. 15 proteins were associated with the arrhythmia endpoint but not death (table 1). BNP was detected by both MS and ELISA, and had greater up-regulation in patients who died, but was not discriminative for arrhythmia occurrence. Conclusions This study provides proof of principle that proteomic techniques can identify candidate proteins for use as biomarkers of arrhythmia risk. Further investigation is needed to select proteins with potential for clinical application before testing in a prospective setting.

Introduction Myocardial scar burden, quantified by MRI, predicts ventricular arrhythmogenesis. MRI use is resource and cost limited in contrast to conventional 12 lead ECG. Manual assessment of ECG time domain features lacks specificity for scar. We hypothesise that frequency and phase domain ECG analysis could yield data indicative of myocardial scar burden. A supervised learning method (support vector machine, SVM) was used to develop a novel computerised algorithm capable of screening ECGs for the presence of myocardial scar, so allowing large data volumes to be processed. Methods 153 consecutive adult patients (age 63±12, male 65%), attending for cardiac MRI with scar analysis for clinical reasons were recruited, and underwent digital ECG acquisition (500Hz, 18bit) at the time of scanning. Semi automated quantification of late gadolinium contrast enhancement was used to identify myocardial scar. The ECGs from those with no scar were used to construct a median template beat for each of the standard 12 leads. ECGs from 35 patients with scar and 35 with no scar were used to train the SVM by feature comparison with the median beat. The algorithm was then tested on the remaining 83 ECGs. Results 16 comparative parameters were included: cross correlation, covariance, magnitude and phase of wavelet coherence, mean value, median value, variance, SD, inter-quartile range, skewness, kurtosis, mobility and complexity of the power spectrum, Hurst exponent slope, detrended fluctuation factor, and differential entropy. Clinical and ECG characteristics in those with scar (age 64±11, LVEF 56±17%, QRS width 115±22 ms) and no scar (age 61±13, LVEF 60±19, QRS width 110±18 ms) were similar (p=NS). The test set consisted of ECGs from 72 patients with scar and 11 patients with no scar. SVM was able to classify the test ECGs with 80.6% sensitivity and 72.7% specificity. Conclusions Digital ECG analysis of frequency and phase signals, using a newly developed SVM, characterises features which classify myocardial scar, but our observations require confirmation in larger prospective studies. The sensitivity and specificity of this approach could make it valuable in population screening for more costly complex investigations such as MRI scanning.