Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.

Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.

In the past few years, the presence of fragmentation in the QRS complex has been demonstrated to be related to diseases such as myocardial fibrosis, cardiac sarcoidosis, arrythmogenic cardiopathies, acute coronary syndrome, and Brugada syndrome, among others. The detection of fragmentation in the QRS is usually carried out manually, which represents a subjective pattern recognition task that demands an effort by the clinician, increasing with the number of patients. These problems have made the process of fragmentation detection a good candidate to its automatization. In this work, we used a database with over six-thousand 12-lead ECG from Hospital Virgen de la Arrixaca de Murcia (Spain), which where digitally recorded with GE MAC5000. Affected and non-affected patients records were extracted for computerized analysis. Clinical supervision was performed for gold-standard development and for signal classification. Fragmentation detection algorithms were developed using first and second derivatives calculation in the pre-qualified segments of the signal, after fiducial point detection. The obtained results were 96.88% sensitivity , 72.92% specificity, and 82.50% accuracy. These results confirm that it is possible to automatically detect fragmentation, constituting a relevant tool to pre-qualify patients for further diagnostic-tests, and it also opens new opportunities for computerized diagnosis.

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Hypertrophic cardiomyopathy (HCM) is a myocardial disorder that affects 0.2% of the population and it is genetically transmitted. Several ECG findings have been related to the presence of fibrosis in other cardiac diseases, but data for HCM in this setting are lacking. Our hypothesis is that fibrosis affects the electrical cardiac propagation in patients with HCM in a relatively specific way and that this effect may be detected with suitable postprocessing applied to the ECG signals. We used 43 standard 12-lead ECGs from patients with previous clinical diagnosis of HCM. Principal Component Analysis (PCA) was applied by combining the ECG-leads oriented to different anatomic regions, hence assessing the potential fibrosis effects in the resulting leads for postprocessing convenience. Linear classifier of Support Vector Machine type were used with several statistics extracted from the resulting PCA-components, including normalized power, standard deviation, kurtosis, skewness, and local maxima. Results reached 75.0% sensitivity, 80.0% specificity, 85.7% positive predictive value, 66.7% negative predictive value, and 76.9% accuracy in our database. There is evidence that myocardial fibrosis can be detected in patients with HCM by postprocessing their ECG signals.

A 62-year-old woman who underwent heart transplantation 6 years later presented a regular atrial tachycardia. Electrophysiologic evaluation showed an atrial arrhythmia in the recipient atrium with 2:1 conduction to the donor atrium, with a confusing electroanatomical map. With the suspect of alternant conduction through two different breakthroughs, the map was split in two concordant maps, corresponding to two connections that were successfully ablated. Later on, a third connection was detected and therefore ablated.

Background Cardiac resynchronization therapy (CRT) with biventricular pacing has demonstrated clinical benefits in heart failure patients with left bundle branch block (LBBB) and ventricular dysfunction. Left bundle branch area pacing (LBBAP) results in a relatively short QRS duration (QRSd) with fast left ventricular activation and could be considered as an alternative to conventional CRT. Purpose The aim of the present study was to evaluate the feasibility and outcomes of LBBAP in patients with indications for CRT. Methods Consecutive patients with indications for CRT were included. LBBAP was performed via transventricular septal approach (1–3). We aimed to achieve a paced QRS with right bundle branch conduction delay morphology, a stimulus to peak left ventricular activation time (S-LVAT) <100ms and/or a QRSd ≤130ms. AV delay programming was individualized in patients in sinus rhythm, taking consideration of the AV conduction, programming the one that generated the shortest QRSd at rest. Rate adaptive AV was also activated in these patients. Pacing electrical and echocardiographic parameters were recorded at baseline and during follow-up. Results LBBAP was achieved in 19 of 21 (90.5%) patients with indication for CRT. Indications were heart failure with LBBB and left ventricular ejection fraction (LVEF) ≤35% in 8 (42%), AV node ablation or AV block with LVEF <50% and high expected RV pacing burden in 9 (47%), 1 pacing-induced cardiomyopathy and 1 patient with biventricular pacemaker malfunction (high LV capture threshold). The mean follow-up was 4.6±1.7 months and the percentage of ventricular pacing was 93.4±13.9%. There were no device-related complications during this period. LBBA capture threshold was 0.6±0.3V at 0.4ms at the implantation, and remained stable (0.7±0.1 V, p=0.17). The lead impedance and R-wave amplitude at implantation were 636±106 ohms and 13.4±6.8 mV, and 541±88 ohms and 13.0±5.1 mV during the follow-up (p<0.001 and p=0.27, respectively). Mean S-LVAT was 85.5±13.9 ms, and mean QRSd was 122±9 ms, that remained stable during follow-up (122 vs 124 ms, p=0.21). In patients with LBBB, a significant narrowing of paced QRSd was achieved (160.9±16.7 vs. 123.9±9.7 ms, p<0.001). Mean LVEF increased by 15.9%, from 35.4±8.9% at baseline to 51.3±9.8% at follow-up (p<0.001) in the overall population, and 14.5% (from 32.7±4.8% to 47.2±10.7%, p=0.001) in patients with LBBB. After one month, estimated time for elective replacement was 11.9±0.4 years. Conclusions LBBPA was successfully achieved in 90.5% of the patients with indication for CRT, with good and stable pacing electrical parameters, long estimated battery longevity and relatively narrow QRS, and was associated with improvement in cardiac function. LBBAP may be considered as a first-line option for patients with indications for CRT. FUNDunding Acknowledgement Type of funding sources: None.

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