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Aims: Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment.Methods and Results: Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias.Conclusion: Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field. [ABSTRACT FROM AUTHOR]

Dilated cardiomyopathy (DCM) is a common yet challenging cardiac disease. Great strides have been made in improving DCM prognosis due to heart failure but sudden cardiac death (SCD) due to ventricular arrhythmias remains significant and challenging to predict. High-risk patients can be effectively managed with implantable cardioverter defibrillators (ICDs) but because identification of what is high risk is very limited, many patients unnecessarily experience the morbidity associated with an ICD implant and many others are not identified and have preventable mortality. Current guidelines recommend use of left ventricular ejection fraction and New York Heart Association class as the main markers of risk stratification to identify patients who would be at higher risk of SCD. However, when analysing the data from the trials that these recommendations are based on, the number of patients in whom an ICD delivers appropriate therapy is modest. In order to improve the effectiveness of therapy with an ICD, the patients who are most likely to benefit need to be identified. This review article presents the evidence behind current guideline-directed SCD risk markers and then explores new potential imaging, electrophysiological and genetic risk markers for SCD in DCM. [ABSTRACT FROM AUTHOR]

Objective: Ablation treatment for persistent atrial fibrillation (persAF) remains challenging due to the absence of a ‘ground truth’ for atrial substrate characterization and the presence of multiple mechanisms driving the arrhythmia. We implemented an unsupervised classification to identify clusters of atrial electrograms (AEGs) with similar patterns, which were then validated by AEG-derived markers. Methods: 956 bipolar AEGs were collected from 11 persAF patients. CARTO variables (Biosense Webster; ICL, ACI and SCI) were used to create a 3D space, and subsequently used to perform an unsupervised classification with k-means. The characteristics of the identified groups were investigated using nine AEG-derived markers: sample entropy (SampEn), dominant frequency, organization index (OI), determinism, laminarity, recurrence rate (RR), peak-to-peak (PP) amplitude, cycle length (CL), and wave similarity (WS). Results: Five AEG classes with distinct characteristics were identified (F = 582, P<0.0001). The presence of fractionation increased from class 1 to 5, as reflected by the nine markers. Class 1 (25%) included organized AEGs with high WS, determinism, laminarity, and RR, and low SampEn. Class 5 (20%) comprised fractionated AEGs with in low WS, OI, determinism, laminarity, and RR, and in high SampEn. Classes 2 (12%), 3 (13%) and 4 (30%) suggested different degrees of AEG organization. Conclusions: Our results expand and reinterpret the criteria used for automated AEG classification. The nine markers highlighted electrophysiological differences among the five classes found by the k-means, which could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies. [ABSTRACT FROM AUTHOR]

Introduction: Sudden cardiac death (SCD) risk assessment is limited, particularly in patients with nonischemic cardiomyopathies. This is the first application, in patients with cardiomyopathies, of two novel risk markers, regional restitution instability index (R2I2) and peak electrocardiogram restitution slope (PERS), which have been shown to be predictive of ventricular arrhythmias (VA) or death in ischemic heart disease patients. Methods: Blinded retrospective study of 50 patients: 33 dilated cardiomyopathy and 17 other; undergoing electrophysiological study (EPS) for SCD risk stratification, and 29 controls with structurally normal hearts undergoing EPS. R2I2 was calculated from an EPS using electrocardiogram surrogates for action potential duration and diastolic interval. Cut‐offs for high and low R2I2/PERS were predefined. Results: R2I2 was significantly higher in study than control patients (0.99 ± 0.05 vs. 0.63 ± 0.04, p <.001). PERS showed a trend to higher values in the study group (1.18[0.63] vs. 1.09[0.54], p =.07). During median follow up of 5.6 years [interquartile range 1.9 years], nine study patients reached the endpoint of VA/death. Patients who experienced VA/death showed trends to higher mean R2I2 (1.14 ± 0.07 vs.0.95 ± 0.05, p =.12) and PERS (1.46[0.49] vs. 1.13[0.62], p =.22). A Cox proportional hazards model using grouped markers: R2I2 < 1.03 + PERS < 1.21/either R2I2 ≥ 1.03 or PERS ≥ 1.21/R2I2 ≥ 1.03 + PERS ≥ 1.21; significantly predicted VA/death (p =.02) with a hazard ratio per positive component of 3.2 (95% confidence interval 1.2–8.8). Conclusion: R2I2≥ 1.03 + PERS ≥ 1.21 may predict VA/death in patients with cardiomyopathies. R2I2 ≥ 1.03 + PERS ≥ 1.21 have the potential to play an important role in SCD risk stratification in cardiomyopathies but their validity should be confirmed in a larger study. [ABSTRACT FROM AUTHOR]

Aims: Hypoglycaemia is associated with increased cardiovascular risk among individuals with diabetes mellitus. It has been hypothesized that hypoglycaemia may trigger autonomic changes leading to increased cardiac arrhythmia risk. We conducted a systematic review and meta‐analysis to explore this association. Materials and methods: Ovid Medline, Embase, Scopus, Web of Science and Cochrane were searched from inception to October 10, 2017. We included studies of adults with diabetes (Type 1 or Type 2) that compared acute electrocardiogram (ECG) changes during episodes of hypoglycaemia and euglycaemia. Results: Our search resulted in 4625 citations, among which 20 studies met the predefined inclusion criteria. Finally, 12 studies were included in the descriptive analysis and 15 in the meta‐analysis. Overall hypoglycaemia was associated with a reduction in heart rate variability and an increase in arrhythmia occurrence. QTc interval length was more significantly prolonged during hypoglycaemia compared to euglycaemia (pooled mean difference [95% confidence intervals] [0.64 (0.27‐1.01], P = ·001). Subgroup analysis based on diabetes type showed that QTc prolongation occurred in individuals with Type 1 and Type 2 diabetes; however, the change between euglycaemia reached statistical significance only among individuals with Type 1 diabetes. Conclusion: Our findings suggest that hypoglycaemia results in ECG alterations that are associated with increased risk of cardiac arrhythmia, which is associated with increased cardiovascular events and mortality. More clinical studies are needed to determine the cardiac risks of hypoglycaemia in individuals with diabetes, especially in Type 2 diabetes. [ABSTRACT FROM AUTHOR]

The unstable temporal behavior of atrial electrical activity during persistent atrial fibrillation (persAF) might influence ablation target identification, which could explain the conflicting persAF ablation outcomes in previous studies. We sought to investigate the temporal behavior and consistency of atrial electrogram (AEG) fractionation using different segment lengths. Seven hundred ninety-seven bipolar AEGs were collected with three segment lengths (2.5, 5,and 8 s) from 18 patients undergoing persAF ablation. The AEGs with 8-s duration were divided into three 2.5-s consecutive segments. AEG fractionation classification was applied off-line to all cases following the CARTO criteria; 43% of the AEGs remained fractionated for the three consecutive AEG segments, while nearly 30% were temporally unstable. AEG classification within the consecutive segments had moderate correlation (segment 1 vs 2: Spearman's correlation ρ = 0.74, kappa score κ = 0.62; segment 1 vs 3: ρ = 0.726, κ = 0.62; segment 2 vs 3: ρ = 0.75, κ = 0.68). AEG classifications were more similar between AEGs with 5 and 8 s (ρ = 0.96, κ = 0.87) than 2.5 versus 5 s (ρ = 0.93, κ = 0.84) and 2.5 versus 8 s (ρ = 0.90, κ = 0.78). Our results show that the CARTO criteria should be revisited and consider recording duration longer than 2.5 s for consistent ablation target identification in persAF. [ABSTRACT FROM AUTHOR]

Frequency Trajectory of Atrial Fibrillation Introduction The role of substrates in the maintenance of persistent atrial fibrillation (persAF) remains poorly understood. The use of dominant frequency (DF) mapping to guide catheter ablation has been proposed as a potential strategy, but the characteristics of high DF sites have not been extensively studied. This study aimed to assess the DF spatiotemporal stability using high density noncontact mapping (NCM) in persAF. Methods and Results Eight persAF patients were studied using NCM during AF. Ventricular far-field cancellation was performed followed by the calculation of DF using Fast Fourier Transform. Analysis of DF stability and spatiotemporal behavior were investigated including characteristics of the highest DF areas (HDFAs). A total of 16,384 virtual electrograms (VEGMs) and 232 sequential high density 3-dimensional DF maps were analyzed. The percentage of DF stable points decreased rapidly over time. Repetition or reappearance of DF values were noted in some instances, occurring within 10 seconds in most cases. Tracking the HDFAs' center of gravity revealed 3 types of propagation behavior, namely (i) local, (ii) cyclical, and (iii) chaotic activity, with the former 2 patterns accounting for most of the observed events. Conclusions DF of individual VEGMs was temporally unstable, although reappearance of DF values occurred at times. Hence, targeting sites of 'peak DF' from a single time frame is unlikely to be a reliable ablation strategy. There appears to be a predominance of local and cyclical activity of HDFAs hinting a potentially nonrandom temporally periodic behavior that provides further mechanistic insights into the maintenance of persAF. [ABSTRACT FROM AUTHOR]

The main objective of this article is to implement and compare QRS subtraction techniques for intra-cardiac atrial electrograms based on using the surface ECG as a reference. A band-pass filter between 8 and 20 Hz followed by rectification, and then a low-pass filter at 6 Hz are used for QRS detection. QRS subtraction was performed using three different approaches: flat, linear and spline interpolations. QRS subtraction affects the power of the signals but it normally does not affect the dominant frequency. The average power of the atrial electrograms after QRS subtraction is significantly reduced for frequencies above 10 Hz. [ABSTRACT FROM AUTHOR]

The reliable and accurate diagnosis of regular broad complex tachycardia (BCT) by the ECG is a goal that has eluded clinicians and electrophysiologists alike for years. This article explores the reason for this by first giving an historical account on the development of the subject. Next, the electrophysiological mechanisms of ventricular tachycardia, supraventricular tachycardia with aberrant conduction, and preexcited tachycardia, the three main differential diagnoses for regular BCT, according to the latest knowledge from cellular and clinical electrophysiology study will be reviewed, together with considerations on how such understanding may help account for the manifestations of these tachycardias on the ECC and the difficulty in distinguishing between them. Finally, the use of electrophysiological study as the criterion standard for diagnosing regular BCT, as has been the case in most studies on the subject, will be critiqued in terms of the potential for misdiagnosis by the method and the use of any ECC diagnostic algorithms developed with its aid in the acute medical care setting. [ABSTRACT FROM AUTHOR]

The authors previously proposed a Bayesian approach to the electrocardiographic diagnosis of regular broad complex tachycardia (BCT), which can be due to VT or supraventricular tachycardia with aberrant conduction (SVTAC). They also published an account comparing the theoretical merits in the design of two of the most commonly used diagnostic algorithms for the same purpose, those of Brugada et al. and Griffith et al. In this study, a direct head-to-head comparison was performed on the practical performances of the three algorithms in this study. A set of 111 ECGs showing regular BGT (77 VT, 34 SVTAG) whose diagnoses were confirmed by electrophysiological study was shown to five internists in general medicine at a district general hospital. The observers were asked to comment on whether the EGG criteria in the three algorithms tested were fulfilled or not, and a computer program then derived the corresponding diagnoses. The sensitivity and specificity for VT achieved by the Brugada algorithm were 92% and 44%, 92% and 44% by the Griffith algorithm, and 97% and 56% by the Bayesian algorithm. The Bayesian algorithm achieved a higher sensitivity and specificity than the other two algorithms, but the differences are not statistically significant (P = 0.6583 and P = 0.5334, respectively). The Brugada, Griffith, and Bayesian algorithms show comparable performances in terms of overall sensitivity and specificity when tested in practice. Of the three algorithms, the Griffith algorithm excels in simplicity and is the easiest to implement in practice. The Bayesian algorithm achieved slightly higher values for sensitivity and specificity than the Brugada and Griffith algorithms but may be more suitable for automated computer-aided diagnosis of EGG due to its complexity. [ABSTRACT FROM AUTHOR]

Despite numerous attempts at devising algorithms for diagnosing broad complex tachycardia (BCT) on the basis of the electrocardiogram (ECG), misdiagnosis is still common. The reason for this may lie with difficulty in implementing existent algorithms in practice, due to imperfect ascertainment of ECG features within them. An attempt was made to approach the problem afresh with the Bayesian inference by the construction of a diagnostic algorithm centered around the likelihood ratio (LR). Previously studied EGG features most effective in discriminating ventricular tachycardia (VT) from supraventricular tachycardia with aberrant conduction (SVTAC), according to their LR values, were selected for inclusion into a Bayesian diagnostic algorithm. A test set of 244 BCT ECGs was assembled and shown to three independent observers who were blinded to the diagnoses made at electrophysiological study. Their diagnostic accuracy by the Bayesian algorithm was compared against that by clinical judgement with the diagnoses from EPS as the criteria! standard. Clinical judgement correctly diagnosed 35% of SVTAC, 85% of VT, and 47% of fascicular tachycardia. In comparison, by the Bayesian algorithm devised, 52% of SVTAC, 95% of VT, and 97% of fascicular tachycardia were correctly diagnosed. The Bayesian algorithm devised has proved to be superior to the clinical judgement of the observers who participated in this study, and theoretically will obviate the problem of imperfect ascertainment of ECC features. Hence, it holds the promise for being an effective tool for routine use in clinical practice. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 68-year-old woman who was referred for first-time ablation of symptomatic paroxysmal atrial fibrillation (AF).Topics discussed include selection of a single catheter approach to mapping and ablation, disclosure of a left-sided superior vena cava (LSVC) draining into a markedly dilated coronary sinus (CS) and deployment of nMARQ Catheter System in the LSVC for targeting markedly fractionated signals in bipolar and unipolar energy delivery configurations.

The article presents the case of a 64-year-old man who sought coronary assessment. The patient had a history of two hospitalizations for ventricular tachycardia and a history of myocardial infarction 11 years back. After several confirmatory examinations, the patient was then diagnosed to have arrhythmogenic right ventricular dysplasia. Treatment included a beta-blocker and an angiotensin converting enzyme inhibitor which are aimed at preventing arrhythmia and sudden death.

A 72 year old woman presented with A troponin positive acute coronary syndrome and was transferred to a tertiary centre a week later. Her admission ECG showed minimal ST depression in V2-V6 with a sinus bradycardia of 38 beats/mm. There was resolution of symptoms and ECG changes on optimized medical treatment which included low molecular weight heparin. Coronary angiography showed a giant thrombus in the distal left main stem. Ostial disease was present in the left anterior descending, circumflex, and obtuse marginal vessels. The right coronary artery was a non-dominant vessel with moderate proximal disease.

This paper presents an innovative approach for T-wave peak detection and subsequent T-wave end location in 12-lead paced ECG signals based on a mathematical model of a skewed Gaussian function. Following the stage of QRS segmentation, we establish search windows using a number of the earliest intervals between each QRS offset and subsequent QRS onset. Then, we compute a template based on a Gaussian-function, modified by a mathematical procedure to insert asymmetry, which models the T-wave. Cross-correlation and an approach based on the computation of Trapezium's area are used to locate, respectively, the peak and end point of each T-wave throughout the whole raw ECG signal. For evaluating purposes, we used a database of high resolution 12-lead paced ECG signals, recorded from patients with ischaemic cardiomyopathy (ICM) in the University Hospitals of Leicester NHS Trust, UK, and the well-known QT database. The average T-wave detection rates, sensitivity and positive predictivity, were both equal to 99.12%, for the first database, and, respectively, equal to 99.32% and 99.47%, for QT database. The average time errors computed for T-wave peak and T-wave end locations were, respectively, −0.38±7.12ms and −3.70±15.46ms, for the first database, and 1.40±8.99ms and 2.83±15.27ms, for QT database. The results demonstrate the accuracy, consistency and robustness of the proposed method for a wide variety of T-wave morphologies studied. [ABSTRACT FROM AUTHOR]