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Background Ventricular tachyarrhythmias (VTA) with low and varying signal amplitudes and morphologies may not be successfully identified utilizing traditional implantable cardioverter defibrillator algorithms. Objective Develop and validate a novel algorithm (VF Therapy Assurance, VFTA) to improve detection and timely delivery of high voltage therapy (HVT) for these arrhythmias. Method Arrhythmia detection was simulated on recorded VTA electrograms (EGMs) utilizing Abbott's Merlin.net database. EGMs where an HVT occurred only when VFTA was enabled, or where VFTA provided an HVT >30 seconds earlier than without VFTA were re-adjudicated with physician review. As VFTA never prevents detection or therapy, EGMs where VFTA didn't activate or alter HVT were not adjudicated. Results Among 564,353 recorded VTA EGMs from 20,000 devices, VFTA altered HVT in 105 EGMs from 67 devices. Physician adjudication determined that 81.9% (86/105) of these EGMs were true undertreated VTA episodes and would have received appropriate HVT with VFTA enabled. Furthermore, 65% (56/86) of the episodes were ventricular fibrillation, polymorphic, did not self-terminate during the recording window, or not amenable anti-tachycardia pacing. 87.5% (49/56) would not have elicited HVT without VFTA. Overall, VFTA provided new or earlier appropriate HVT in 0.27% (53/20,000) of devices with an increase in inappropriate HVT in 0.07% (14/20,000) devices. Conclusion The VFTA algorithm successfully identifies VTA missed by traditional detection algorithms, due to undersensed ventricular signals resulting in rate falling below the programmed detection rate. The use of VFTA increases the likelihood of delivering life-saving HVT.

Background Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with high morbidity and mortality rate. Evidence shows that in every three patients with AF, one is asymptomatic. The asymptomatic and paroxysmal nature of AF is the reason for unsatisfactory and delayed detection using traditional instruments. Research indicates that wearing a dynamic electrocardiogram (ECG) recorder can guide accurate and safe analysis, interpretation, and distinction of AF from normal sinus rhythm. This is also achievable in an upright position and after exercises, assisted by an artificial intelligence (AI) algorithm. Methods This study enrolled 114 participants from the outpatient registry of our institution from June 24, 2020 to July 24, 2020. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60 s. Results Of the 114 subjects enrolled in the study, 61 had normal sinus rhythm and 53 had AF. The number of cases that could not be determined by the wristband of dynamic ECG recorder was two, one and one respectively. Case results that were not clinically objective were defined as false-negative or false-positive. Results for diagnostic accuracy, sensitivity, and specificity tested by wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76–97.80%), 88.68% (95% CI 77.06–95.07%), and 100% (95% CI 92.91–100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI 92.21–99.44%), 94.34% (95% CI 84.03–98.65%), and 100% (95% CI 92.91–100%), respectively. Similar results as those of the upright position were obtained after exercise. Conclusion The widely accessible wearable dynamic ECG recorder integrated with an AI algorithm can efficiently detect AF in different postures and after exercises. As such, this tool holds great promise as a useful and user-friendly screening method for timely AF diagnosis in at-risk individuals.

A previously asymptomatic woman in her 40s presented to the emergency with sudden onset of weakness of the right side upper and lower limbs and slurring of speech. Her symptoms improved entirely within 24 hours. Cardiac examination revealed a loud first heart sound and a short mid-diastolic rumble at the apex. ECG showed normal sinus rhythm. Non-contrast CT of the head done in the emergency was normal. Transthoracic echocardiogram image in apical four-chamber view is shown in figure 1. Figure 1 Echocardiogram in apical four chamber view showing a multilobular mass (arrows) in the left atrium during ventricular …

Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10−3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample.

Background Atrial fibrillation (AF) is the world’s most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods In our 2-step algorithm, we first calculate the R-R interval variability–based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

In this era of eHealth, healthcare data has gained a significant importance due to having human survival information. Detection of the atrial fibrillation (AF) from electrocardiogram (ECG) rhythm is a promising area of research because of its critical impact on mortality ratio in all over the world. Although, most of the studies have shown more than 90% results accuracy in terms of specificity (Sp) and sensitivity (Se), yet this accuracy is not sufficient and cannot be considered reliable for AF continuous monitoring due to high ratio of false alarms. Existing works scarcely compare the accuracy of the results generated by a classifier with those of other robust classifiers. Further, the results are needed to be verified with more statistical measures. In this paper, a multiple classifiers-based model is proposed in which the transition between normal sinus rhythm (NSR) and AF are performed on the basis of ventricle activities of the heart. The proposed scheme first extracts several features that are pertinent to AF diagnosis from the ECG data. Later, the classification model categorizes rhythms into classes using statistical techniques. The experimental evaluation is performed on five datasets which include AF Challenge 2017 database, NSR database (NSRDB), NSR RR interval database (NSRDB-2), AF database (AFDB) and long-term AF database (LTAFDB). Based on verification of results using different measures, the proposed scheme outperforms in comparison to the existing systems in terms of area under the curve (AUC), Se, Sp, positive predictive value (PPV), accuracy (ACC), and negative predictive value (NPV) for all datasets. Specifically, the decision tree (DT) obtains 99% AUC, 92% Se and 98% Sp for the AF Challenge 2017 database, which are improved than parallel systems.

A 63-year-old man presented to the emergency department for fluttering in the chest. The initial electrocardiogram (ECG) suggested rapid atrial fibrillation with nonspecific RS- and ST-segment abnormalities. Treatment with intravenous β-blocker resulted in conversion to a normal sinus rhythm. The repeat ECG revealed conversion to normal sinus rhythm with an inferior lateral myocardial infarction. This case presentation identifies the importance of an algorithmic approach to identifying narrow complex tachyarrhythmias considering both cardiac and noncardiac causes. Furthermore, evaluating ECG changes when varying rates and rhythms exist on differing ECGs. Identifying the importance of serial ECGs is emphasized by the case presented, concluding with an understanding of arrhythmias associated with myocardial infarctions and clinical pearls.

Newborn male with symptomatic bradycardia initially diagnosed with complete atrioventricular block. Isoproterenol drip was initiated, and the patient was scheduled for pacemaker implantation. During the hospital course, repeat electrocardiogram and Holter monitor revealed evidence of near continuous blocked atrial bigeminy with occasional aberrantly conducted premature atrial contractions. Flecainide was started, resulting in normal sinus rhythm, and the pacemaker implantation was cancelled.

Aims Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948–0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

Purpose: The safe delivery of cardiac radioablation (CR) for atrial fibrillation (AF) is challenged by multi-direction target motion, cardiac rate variability, target proximity to critical structures, and the importance of complete target dose coverage for therapeutic benefit. Careful selection of appropriate treatment procedures is therefore essential. This work characterizes AF cardiac radioablation target motion and target proximity to surrounding structures in both healthy and AF participants to guide optimal treatment technique and technology choice. Methods: Ten healthy participants and five participants with AF underwent MRI acquisition. Multi-slice, cardiac-gated, breath-hold cines were acquired and interpolated to create three-dimensional images for 18-30 cardiac phases. Treatment targets at the left and right pulmonary vein ostia (CTVLeft and CTVRight respectively) and adjacent cardiac structures were contoured and their displacements throughout the cardiac cycle were assessed. Target proximity to surrounding structures were measured. Free-breathing real-time two-dimensional cine images were also acquired at 4 Hz frequency for between 1- and 2-min duration. The motion of easily identifiable points within the target, diaphragm and sternum was measured to assess respiratory motion. Results: Target motion due to cardiac contraction was most prominent in the medial-lateral direction and of 4-5 mm magnitude. CTVRight displacements were smaller in participants with AF than healthy participants in normal sinus rhythm. Nearby cardiac structures often moved with different magnitudes and motion trajectories. CTVLeft and/or CTVRight were in direct contact with the esophagus in 73% of participants. Target motion due to respiration was most prominent in the superior-inferior direction and of 13-14 mm magnitude in both healthy and AF participants. Conclusion: AF CR target motion and relative displacement was characterized. The combination of target motion magnitude and relative displacement to critical structures highlights the importance of personalizing motion compensation techniques for effective AF CR treatments.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

The cellular action potential of cardiac muscles generates Electrocardiogram (ECG) signals. Disturbances in cardiac cells are determined by analyzing the stability of ECG intervals. The PTa Interval (PTaI) of ECG represents the atrial Action Potential Duration (APD) and the evaluation of the causes of PTaI instability can predict the onset of arrhythmia. This study developed an Autoregressive Moving Average with Exogenous Input (ARMAX) model to explore the roles of Heart Rate Variability (HRV) and Premature Activation (PA) in PTaI dynamics using PTaI and PP Interval (PPI) as exogenous inputs. Minute ECG signals were collected from twenty Normal Sinus Rhythm (NSR) and ten Atrial Tachycardia (AT) volunteers. The EDAN PC ECG system was used in the Modified Limb Lead (MLL) configuration to evaluate instability. The instabilities of PTaI were found at the minimum model orders (Amin) of 10 and 11, in the NSR and AT groups, respectively. In the NSR group, the predominant reason for PTaI instability was HRV, whereas among AT patients, it was largely due to PA that preceded the onset of AT. The proposed model showed better prediction of PTaI with minimum Mean Square Error (MSE) between the measured and predicted PTa Intervals (PTaIs). The factor that led to PTaI instability in AT patients was found to be different from that of the NSR group. The frequency of PA (fPA) was found to contribute more in the AT than the NSR group. The developed ARMAX model was better in predicting instability of atrial ECG dynamics in both groups than other autoregressive models currently in use.

The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal dynamics. Based ...

Atrial fibrillation (AF) is associated with significant risk of heart failure and consequent death. Its episodic appearance, the wide variety of arrhythmias exhibiting irregular AF-like RR intervals and noises accompanying the ECG acquisition, impede the reliable AF detection. Therefore, the Computing in Cardiology Challenge 2017 organizers encourage the development of methods for classification of short, single-lead ECG as AF, normal sinus rhythm (NSR), other rhythm (OR), or noisy signal (NOISE). This study presents a set of 118 time and frequency domain feature including descriptors of the RR and PP intervals; QRS and P-wave amplitudes; ECG behavior within the TQ intervals, deviation of the TQ and PQRST segments from their first principle component analysis vector; dominant frequency; regularity index, width and area of the power spectrum estimated for the ECG signal with eliminated QRS complexes. Three classification techniques have been applied over the 118 ECG features – linear discriminant analysis (LDA), classification tree (CT) and neural network (NN) approach. The scores over a test subset are: (i) FNSR = 0.81; FAF = 0.61; FOR = 0.53, F1 = 0.65 for CT, which is the most simple model; (ii) FNSR = 0.82; FAF = 0.62; FOR = 0.53, F1 = 0.66 for LDA, which is the model with the most reproducible accuracy results; (iii) FNSR = 0.86; FAF = 0.74; FOR = 0.57, F1 = 0.72 for NN, which is the most accurate model.

Equine athletes have a pattern of exercise which is analogous to human athletes and the cardiovascular risks in both species are similar. Both species have a propensity for atrial fibrillation (AF), which is challenging to detect by ECG analysis when in paroxysmal form. We hypothesised that the proarrhythmic background present between fibrillation episodes in paroxysmal AF (PAF) might be detectable by complexity analysis of apparently normal sinus-rhythm ECGs. In this retrospective study ECG recordings were obtained during routine clinical work from 82 healthy horses and from 10 horses with a diagnosis of PAF. Artefact-free 60-second strips of normal sinus-rhythm ECGs were converted to binary strings using threshold crossing, beat detection and a novel feature detection parsing algorithm. Complexity of the resulting binary strings was calculated using Lempel-Ziv (‘76 & ‘78) and Titchener complexity estimators. Dependence of Lempel-Ziv ‘76 and Titchener T-complexity on the heart rate in ECG strips obtained at low heart rates (25–60 bpm) and processed by the feature detection method was found to be significantly different in control animals and those diagnosed with PAF. This allows identification of horses with PAF from sinus-rhythm ECGs with high accuracy.

Vasospastic angina is an uncommon cause of cardiac arrest. We describe a patient who presented with sudden cardiac arrest due to severe coronary vasospasm. Telemetry during the event revealed ventricular arrhythmias and asystole followed by spontaneous self-conversion back to normal sinus rhythm. The patient underwent implantable cardioverter-defibrillator therapy. (Level of Difficulty: Beginner.).

Electrocardiogram (ECG) technology plays a vital role in detecting arrhythmia. Numerous achievements have been marked in ECG-related research. Most methods first pre-process ECG signals, then extract features, and finally classify them. Most of the ECG signals used in the related studies were analyzed in specific time intervals or using a fixed number of samples. However, it is not always possible to see significant changes in a short term, and the symptoms of some patients are relatively short-lived. Misjudgments are possible because the ECG signal was not accurately extracted. This study proposes a computer-aided diagnosis (CAD) system for classification of Atrial Fibrillation and Normal Sinus Rhythm based on ECG signals through convolutional neural network. The proposed system considers a single heartbeat, rather than a specific number of seconds. This study eschews the one-dimensional digital ECG signal used in previous studies and uses convolutional neural networks to analyze two-dimensional ECG image. This study explores whether two-dimensional image ECG requires signal filtering. The final classification results in filtered ECG signals is accuracy of 99.23%, sensitivity of 99.71%, and specificity of 98.66%. The best result in non-filtered ECG signals achieves accuracy of 99.18%, sensitivity of 99.31%, and specificity of 99.03%. With no cumbersome artificial settings, the results of this study are comparable to the related studies. The proposed CAD system has high generalizability; it can help doctors to diagnose diseases effectively and reduce misdiagnosis.

One of the rare causes of diffuse T-wave inversion (TWI) in electrocardiogram (ECG) is memory T-waves. This should be considered among the differentials of diffuse TWI in ECG of patients presenting to the emergency department (ED), especially when they have previous episodes of ventricular tachycardia (VT) or pacemaker implantation or Wolff-Parkinson-White syndrome. These TWIs are benign and do not require any treatment. However, it is of paramount importance for the emergency physician to differentiate it from ischemia-related T-wave changes. In the following case series, we report three cases of memory T-waves. Two of the cases had TWI in leads II, III, aVF, and V3 to V6 following reversion of VT. The other patient, with a? VVI (Left ventricle paced, Left ventricle sensed, Inhibition to sensing) pacemaker, had memory T-waves in the ECG taken during normal sinus rhythm. In all the three patients, we considered memory T-waves to be the possible cause of TWI. The electrocardiographic diagnostic criteria for memory T-waves are positive T in lead aVL and positive/isoelectric T in the lead I; and precordial TWI >inferior TWI. These criteria are 92% sensitive and 100% specific. In the following case series, we also provide an algorithmic approach for patients with suspected memory T-waves in their 12-lead ECG when they present to the ED.

The occlusion in one of the coronary arteries of the heart leads to the cardiac ailment, myocardial infarction (MI). The localization of MI based on the investigation of the morphology of the multi-lead electrocardiogram (ECG) is the initial task for the diagnosis of this ailment. In this paper, the multiscale convolutional neural network is proposed for the automated localization of MI ailment from multi-lead electrocardiogram (ECG) beats. The Fourier-Bessel (FB) series expansion based empirical wavelet transform (EWT) with fixed order ranges is introduced for the multiscale analysis of multi-lead ECG beat. The FB spectrum of each lead ECG beat is segregated into contiguous segments using the fixed order ranges. Furthermore, the order ranges from these contiguous segments are used to design an empirical wavelet filter bank for the extraction of subband signals from each lead ECG beat. The convolutional neural network (CNN) is used for the classification of various categories of MI as anterior MI (AMI), anterio-lateral MI (ALMI), anterio-septal MI (ASMI), inferior MI (IMI), inferio-lateral MI (ILMI), inferio-posterio-lateral MI (IPLMI) and normal sinus rhythm (NSR). The experimental results reveal that the lower-order range subband signal coupled with CNN attains higher average accuracy values of 99.92%, 99.34%, 99.95%, 99.95%, 99.91%, and 99.86% respectively, for AMI, ALMI, ASMI, IMI, ILMI, and IPLMI classes. The subband signal of multi-lead ECG beats with order range of [1-26] is highly affected during various categories of MI heart disease, and this band signal has higher performance as compared to the existing MI localization approaches.

Spatial resolution is typically prioritized over temporal in conventional echocardiography, which restricts the use of time-shifted based techniques at higher frame rates (FR). Electromechanical Wave Imaging (EWI) is an echocardiography-based modality that non-invasively maps the cardiac activation sequence at a high frame rate. At lower FRs, EWI strain curves have a different profile which constrains manual input and interpretation. In this study, we investigate how machine learning (ML) can assist in the accurate activation time estimation at low FR data. We show that with the use of a Random Forest classifier for automated EWI estimation, we successfully identify the normal, early activated basal septum in normal sinus rhythm lower FR cases ( $\mathrm{N}=4$ , FRs of 500 Hz, 250 Hz, 125 Hz) while maintaining the electromechanical activation propagation pattern over the rest of the myocardium. These findings indicate that ML can be used to generate accurate EWI activation maps with a more flexible FR range that includes clinical systems at low FRs, where manual processing would otherwise be impractical and/or inaccurate.

Given the rarity of significant first-degree atrioventricular block (AVB) after Occlutech Figulla Flex II atrial septal defect occluder (OFF II, Occlutech International AB, Helsingborg, Sweden) placement, its management and predictors of recovery have not been established. We report a 9-year-old boy who developed significant first-degree AVB a day after OFF II deployment. Thereafter, oral prednisolone was initiated at 1.5 mg/kg/day. On day 2 of steroid treatment, normal sinus rhythm without conduction delays was occasionally observed, while the prolonged PR interval on the exercise stress test was ameliorated. After 5 days of steroid administration, normal sinus rhythm without conduction delays was completely achieved. An increase in the frequency of normal sinus rhythm and the absence of conduction delays on electrocardiogram monitoring and exercise stress test play an important role in predicting recovery from prolonged PR interval. Thus, steroid treatment can be effective and should be considered for patients who develop first-degree AVB after undergoing OFF II placement for transcatheter device closure of atrial septal defect.

The use of nasopharyngeal (NP) swab sampling for the detection of various respiratory pathogens has been a standard procedure in medicine for many years. While this is a fairly common procedure, there has been a significant increase in utilization recently due to the SARS-CoV-2 pandemic. We describe a case of a 40-year-old SARS-CoV-2 positive patient with no prior cardiac history who developed asystole while an NP swab was being used to obtain a sample for a SARS-CoV-2 assay. Return of normal sinus rhythm was achieved with chest compressions alone. The incident was deemed to have been an exaggerated vagal response to intranasal stimulation; better known as the trigeminocardiac reflex. This is the first reported case describing asystole during use of an NP swab. This case occurred in a patient with no known cardiac disease and highlights the potential importance of the arrhythmogenic nature of COVID-19 that could potentiate the vagal response in susceptible individuals undergoing NP sampling.

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.

A 59-year-old female underwent a dual-chamber pacemaker implantation for intermittent complete heart block. A baseline electrocardiogram showed normal sinus rhythm with first-degree atrioventricular (AV) block and right bundle branch block. A His bundle lead placement was attempted. An intracardiac electrogram from the His bundle lead demonstrated atrial-His, and His-ventricular intervals were 186 and 110 ms, respectively. Pacing was performed from the His bundle lead with a decremental pacing output to assess for the His bundle capture threshold. However, there were no significant QRS morphology changes during the pacing. Is the His bundle captured? The tracing evaluation demonstrated the fascinating physiology of activation wavefront in His Purkinje system that could be applied in the use of conducting system pacing technologies.

Absztrakt: Akut légzési elégtelenség miatt felvett 70 éves férfi betegnél a felvételi EKG pitvari tachycardiát és makro-T-hullám-alternánst mutatott, amely a sinusrhythmus helyreállása után megszűnt. Az EKG-jelenséget sem QT-megnyúlás, sem heveny ischaemia nem kísérte. Az eset arra hívja fel a figyelmet, hogy makro-T-hullám-alternáns jelentkezhet pitvari tachycardia következtében is, amely esetünkben akut respiratoricus insufficientia miatt lépett fel. Orv Hetil. 2020; 161(7): 275–277.

Although various changes in electrocardiogram (ECG) were reported with normal serum potassium levels in acute intracranial pathology, giant asymmetrical T-wave change has not been reported. We report a case of a previously healthy male child who presented with acute febrile illness and features of raised intracranial pressure. Full standardized ECG shows normal sinus rhythm and tall, broad, and giant asymmetrically peaked T-wave. Serum potassium, echocardiography, and cardiac injury marker were normal. The child managed with supportive care and antimicrobials and showed recovery in 7 days.

Supraventricular tachycardia is a common arrhythmia in pregnancy. During labor and delivery, neuraxial analgesia is important to prevent arrhythmia recurrence. We present the case of a 27-year-old gravida 2 para 1 woman at 35 weeks’ gestation presenting with supraventricular tachycardia that converted to normal sinus rhythm with adenosine. To prevent recurrence of the arrhythmia, an early epidural was provided during labor to minimize catecholamine release.

Background The factors that influence cognitive function in patients with atrial fibrillation (AF) remain unclear. Methods This study involved an AF group and control group (normal sinus rhythm) of 150 patients each. Cognitive function was assessed with the adjusted Mini-Mental State Examination (MMSEadj) score and Memory and Executive Screening (MES) score. The relationship between cognitive function and the CHA2DS2VASc score was analyzed. Subgroup analysis was performed according to stroke history. Clinical factors affecting the MMSE score were screened by logistic regression analysis. Results Baseline data were similar between the two groups. The MMSEadj and MES scores were significantly lower in the AF than control group; the mean MMSEadj score in the AF non-stroke subgroup and control non-stroke subgroup was 26.2 ± 2.7 and 27.9 ± 2.0, respectively. In non-stroke patients with AF, the MMSEadj and MES scores were negatively correlated with the CHA2DS2VASc score. Factors significantly influencing the MMSE score in these patients were age, education, smoking history, NT-proB-type natriuretic peptide, hemoglobin, and anticoagulation. Conclusion AF is associated with cognitive dysfunction regardless of stroke history. High CHA2DS2VASc scores are associated with impaired cognitive function. Factors influencing cognitive function in non-stroke patients with AF are age, education, smoking history, NT-proB-type natriuretic peptide, hemoglobin, and anticoagulation.

INTRODUCTION Atrial fibrillation is often asymptomatic and un-diagnosed in the community resulting in an increased risk of heart failure and stroke to those patients. We evaluated the effectiveness, tolerability, and accuracy of a novel six-channel electrocardiogram digital-health screening device, the RhythmPad, for the detection of atrial fibrillation. METHODS Seven hundred and fifty-two participants attending the cardiology department were recruited. Two recordings were taken-a six-lead electrocardiogram using the RhythmPad device and a standard 12-lead electrocardiogram. Recorded traces were analyzed by two blinded cardiologists. The computer-generated automated diagnostic reports from both systems were also compared. Post-participation feedback was obtained from study participants using a three-part questionnaire. RESULTS The sensitivity of the six-lead electrocardiogram compared to the 12-lead electrocardiogram, analyzed by two blinded cardiologists, for the detection of normal sinus rhythm was 95.9%, with a specificity of 97.2%. The sensitivity for the detection of atrial fibrillation using the six-lead ECG was 93.4%, with specificity 96.8%. The six-lead automated diagnostic report had a sensitivity and specificity of 97.5% and 98.6%, respectively, for correctly diagnosing normal sinus rhythm. For the correct diagnosis of atrial fibrillation, the six-lead automated diagnostic report had a sensitivity and specificity of 95.4% and 98.8%, respectively. A total of 95.4% of participants found RhythmPad to be comfortable, with only 0.5% preferring the 12-lead ECG device in comparison to six-lead ECG acquisitions. CONCLUSION The RhythmPad digital health device and its automated diagnostic report were highly accurate in detecting atrial fibrillation when compared to a standard 12-lead electrocardiogram.

Heart diseases are rapidly increasing worldwide and in our country. Thisincrease causes difficulties in the diagnosis processes of heart diseases. Consideringthese problems, the studies of engineering applications related to medicalscience give effective results in terms of solutions. By means of engineeringdevices and algorithms, positive contributions are made to medicalapplications. These contributions assist physicians especially in the diagnosisstages and speed up these processes. In this study, a new algorithm isdevelopped so that Atrial Fibrillation (AF), which is the most common type ofarrhythmia encountered, can be automatically detected at a high success rate.Electrocardiogram (ECG) data used in this study were obtained from physiobank ATMdatabase. 31 samples of Atrial Fibrillation Rhythm (AFR) and 31 samples ofNormal Sinus Rhythm (NSR) were obtained from this database. RR Interval (RRI)sequences being 12 hours long are used in the study. The change of the RRIsequences is an important parameter for AF. The RRI sequences are re-sampled usingsignal pre-processing techniques. The Discrete Wavelet Transform (DWT) was thenapplied to the resampled signals. In this way, feature extraction process isperformed and the wavelet energies of these signals are visually examined withboxplot. The wavelet energies of the RRI sequences are classified by the SupportVector Machine (SVM). Finally, AFR and NSR are successfully separated as 99.60%achievement., Kalp hastalıkları dünyagenelinde ve ülkemizde hızlı bir biçimde artmaktadır. Bu artış kalphastalıklarının tanı süreçlerinde zorlukların oluşmasına neden olmaktadır. Busorunlar düşünüldüğünde mühendislik uygulamalarının tıp bilimi ile ilgili olançalışmaları çözümler açısından etkili sonuçlar vermektedir. Mühendisliksayesinde geliştirilen cihazlar ve algoritmalar sayesinde tıp uygulamalarınaolumlu katkılar sağlanmaktadır. Bu uygulamalar özellikle hekimlere tanıaşamalarında yardımcı olmakta ve bu süreçleri hızlandırmaktadır. Bu çalışmadaen sık rastlanan aritmi çeşidi olarak karşımıza çıkan Atriyal Fibrilasyon’un(AF) otomatik olarak tespitinin yüksek başarı oranında yapılmasıtasarlanmıştır. Bu çalışmada kullanılan Elektrokardiyogram (EKG) verileri, PhsiyobankATM veritabanından elde edilmiştir. Bu veritabınından 31 adet AtriyalFibrilasyon Ritmi (AFR) ve 31 adet Normal Sinüs Ritmi (NSR) olan sinyalleralınmıştır. Bu sinyaller 12’şer saatlik uzunlukta olup çalışmada RR Aralıklarıdizileri kullanılmıştır. RRA dizilerinin değişimi AF için önemli bir parametreolarak karşımıza çıkmaktadır. Sinyal işleme teknikleri ile RR Aralıkları zamanekseninde yeniden örneklenmiştir. Ardından yeniden örneklenen sinyallere AyrıkDalgacık Dönüşümü (ADD) uygulanmıştır. Bu sayede özellik çıkarımı işlemiyapılmış ve bu sinyallerin dalgacık enerjileri boxplot ile görsel olarakincelenmiştir. RRA dizilerinin dalgacık enerjileri Destek Vektör Makinası (DVM)ile sınıflandırma işlemine tabi tutulmuş ve %99,60 oranında başarıyla AFR veNSR birbirinden ayrılmıştır.

Atrial fibrillation (AF) is associated with thrombus formation in the left atrial appendage and systemic embolic events including ischemic stroke. Cardiogenic thromboembolism can also occur in the absence of clinical AF as a result of various pathological conditions affecting the endocardium. The inconsistent temporal relation between AF and ischemic events has stimulated exploration for factors other than clinical AF that contribute to thromboembolism. These include subclinical AF, a thrombogenic atrial cardiomyopathy, and left atrial appendage dysfunction and embolism from other sources. In conclusion, thromboembolism during normal sinus rhythm is likely multifactorial, involving intertwined pathologic processes. Patients at risk, if accurately identified, could theoretically benefit from anticoagulation.

Atrial fibrillation (AF) is one of the most common arrhythmia diseases, the incidence of which is ascendant with age increase. What’s more, AF is a high-risk factor for stroke, ischemia myocardial and other malignant cardiovascular diseases, which would threaten people’s life significantly. Using a mobile device to screen AF segments is an effective way to reduce the mortality and morbidity of malignant cardiovascular diseases. However, most of existing AF detection methods mainly centered on clinical resting ECG signals and were incapable of processing mobile ECG signals with low signal-to-noise ratio which collected by mobile devices. In this paper, we take advantage of a fully convolutional network variant named U-Net for heart rhythmic information capturing by locating R peak positions as well as calculating RR intervals and a 34-layer residual network for waveform morphological features capturing from ECG signals. Combining both rhythmic information and waveform morphological features, two-layer fully connected networks are employed successively to discriminate AF, normal sinus rhythm , and other abnormal rhythm (other). The extensive experimental results show that our proposed AF our proposed AF screening framework named FRM-CNN can achieve $$F_{1}$$ value of 85.08 ± 0.99% and accuracy of $$87.22 \pm 0.71$$ % on identifying AF segments well without handcraft engineering. Compared with the cutting-edge AF detection methods, the FRM-CNN has more superior performance on monitoring people’s health conditions with mobile devices.

BACKGROUND Tiletamine-zolazepam is a widely used as an alternative chemical immobilization method in non-human primates, with a safe application pathway and necessary relaxation. In order to determine the electrocardiographic parameters of Sapajus libidinosus after intramuscular tiletamine-zolazepam administration at the dose of 5 mg/kg, nine animals were submitted to anesthesia. METHODS The interpretation of the electrocardiogram determined: heart rate in bpm and heart rhythm, P wave, P-R interval, R wave, QRS complex, T wave, Q-T interval, corrected QT interval and electrical axis. The mean HR was 206 ± 32 bpm. RESULTS The majority of the monkeys showed normal sinus rhythm, but one animal showed sinus tachycardia. The most commonly observed electrical axis was between +30° and +90°. Two animals presented bigger alterations in the tracing such as low amplitude QRS and T wave bigger than 1/4 of the R wave. CONCLUSIONS The administration of tiletamine-zolazepam was safe and efficacious, with minimal cardiovascular effects.

We determined the procedure to reduce arrhythmia-related ghosting artifacts in the late gadolinium enhancement (LGE) imaging of cardiovascular magnetic resonance (CMR) in patients with arrhythmia by examining the causing factors using phantoms. Inversion recovery gradient echo and phase-sensitive inversion recovery (PSIR) sequences were compared under normal sinus rhythm and premature ventricular contraction (PVC) conditions. Under the PVC condition, trigger interval irregularly performed induced ghosting artifacts. A phase-corrected real image in PSIR, however; demonstrated an accurately positive contrast of pale LGE area indicative of mild fibrosis with minimal ghosting artifacts. The study results indicate that PSIR has an advantage for LGE CMR in patients with arrhythmia. Even without having PSIR method, the 2R-R method ensures consistency of contrast and enables reduction of ghost artifacts.

Previously we performed radiated electro-magnetic compatibility tests of automated external defibrillators (AEDs) for radiated immunity per the IEC 60601-1-2 and ANSI/AAMI/IEC 60601-2-4 standards, in the prescribed 80–2500 MHz radiofrequency range, with the patient leads connected. We observed malfunctions for several AEDs in the 30 to 45 MHz range. Our present study involves conducted (injected current) testing from 10 to 80 MHz using prescribed IEC methods. We now tested the same AEDs plus one. We injected 3 Vrms on the patient leads from 10 MHz–80 MHz and 6 Vrms at Industrial Scientific and Medical frequencies in this range. Ventricular fibrillation (V-Fib) signals and normal sinus rhythm (NSR) signals were delivered to the patient leads from an AED tester. We also performed radiated testing in the 70–90 MHz band with 10V/m and 20V/m to compare with conducted testing. For conducted tests three AEDs gave false diagnoses (failed to identify V-Fib signals) at some frequencies. For the radiated testing, only one AED gave a false diagnosis. Two of these AEDs are no longer produced but are still in use. Discrepancies between the radiated and conducted test results are due to basic differences in the test methods and mechanisms of interaction.

Background: Junctional ectopic tachycardia (JET) is one troublesome supraventricular arrhythmia in postoperative pediatric cardiac surgical patients. Unless treated timely and effectively it may lead to morbidity and even mortality. Aim: To understand the role of Ivabradine in the treatment of JET in postoperative pediatric cardiac surgical patients. Case: We present a postoperative case of complete repair of Tetralogy of Fallot who was in normal sinus rhythm in the preoperative period and developed JET 4 hours later in postoperative period which was persistent despite measures to optimize the internal milieu of the body and conventional antiarrhythmics, that was successfully treated with Ivabradine at a dose of 0.05 mg/kg/12 hourly. Conclusion: Ivabradine can be used to successfully treat postoperative JET in cases ofrefractory to other antiarrhythmic drugs like Digoxin and Amiodarone.

Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versus time traces over a period. Deep learning technology, a robust artificial intelligence algorithm, can imitate the data processing patterns of the human brain, and it has experienced remarkable success in disease screening, diagnosis, and prediction. Compared with traditional machine learning, deep learning algorithms possess more powerful learning capabilities and can automatically extract features without extensive data pre-processing or hand-crafted feature extraction, which makes it a suitable tool to analyze complex structures of high-dimensional data. With the advances in computing power and digitized data availability, deep learning provides us an opportunity to improve ECG data interpretation with higher efficacy and accuracy and, more importantly, expand the original functions of ECG. The application of deep learning has led us to stand at the edge of ECG innovation and will potentially change the current clinical monitoring and management strategies. In this review, we introduce deep learning technology and summarize its advantages compared with traditional machine learning algorithms. Moreover, we provide an overview on the current application of deep learning in ECGs, with a focus on arrhythmia (especially atrial fibrillation during normal sinus rhythm), cardiac dysfunction, electrolyte imbalance, and sleep apnea. Last but not least, we discuss the current challenges and prospect directions for the following studies.

Objetivo: Describir el caso de un paciente masculino de 19 años que presenta bradicardia e hipotensión después de una picadura de abeja haciendo una revisión de la literatura y fisiopatología de los cambios cardiovasculares y electrocardiográficos después de una picadura de abeja. Métodos: Se inspeccionó y aseguró la vía aérea del paciente. Un rastreo de ECG realizado al ingreso reveló ritmo idioventricular a una ritmo de 41 latidos por minuto. Se administró oxígeno, se estableció un acceso IV y se colocaron los cables del marcapasos transcutáneo en el tórax del paciente, luego se indujo el manejo de las guías publicadas. Resultados: El electrocardiograma mostró un ritmo idioventricular a una frecuencia de 41 latidos por minuto que se resolvió a un ritmo sinusal normal después del tratamiento. Conclusiones: Se debe realizar una evaluación cardiovascular completa y rápida en todos los pacientes que se presentan al departamento de emergencias después de una picadura de abeja, y se deben seguir las pautas publicadas sobre el manejo de la bradicardia y la anafilaxia para lograr resultados exitosos.

Electrocardiography has been employed successfully in medicine for many years to provide vital knowledge about the cardiovascular system. Although processing and evaluation of electrocardiogram (ECG) signals provide helpful information in the detection of anomalies in the vessel, diagnosis of heart defect, and treatment of diseases, multi-channel ECG signals have been started to be employed in order to achieve higher success. Utilizing a multi-channel ECG signal instead of a one-channel ECG signal yields more adequate achievements but require higher complexity in analysis and higher computational cost. To achieve faster and accurate results in multi-channel ECG signals, an artificial intelligence-based automatic diagnosis system employing the texture features of two-dimensional images, which are constructed by projecting the ECG signal vector as a row of the image, is proposed. The hypothesis proposed in this study conjectures that these texture features in the images contain determinative indicators of various diseases (cardiovascular abnormalities/disturbance) even for the short-time intervals. Accordingly, the main contribution of this study is to expose that detection of cardiovascular defects can be done with the classical image texture methods by utilizing multi-channel biomedical signals in a sufficiently short-time-interval. The methodology has been implemented in different time intervals of a large dataset constructed from a diverse population that is labeled as one normal sinus rhythm type and eight abnormal types of ECG signals. The accuracy of this hypothesis has been proven by achieving high detection rates of identifying cardiac abnormalities and reduced the computational load of the processing system without any sacrificing accuracy.

Background:Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods:The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results:For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions:Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR) versus Atrial Fibrillation (AF) from a single channel short ECG segment. For this purpose, AF and NSR from PhysioNet/Computing in Cardiology Challenge 2017 training dataset were used in this study. RR intervals were extracted using GQRS algorithm, and RR time series with less than 30 beats were excluded from the analysis. The stratified split was applied to create a training set (NSR: 1521 and AF: 239) and test set (NSR: 1527 and AF:234). Shapelets were extracted by scanning RR time series in the training set and identifying statistically significant patterns. For classification, an XGBoost model was trained using the presence or absence of the top 100 shapelets. Using the top 100 significant shapelets (Shapelet length between 5 and 29), we achieved the area under the ROC curve (AUC) and the area under the precession recall curve (AUPRC) of 0.94 and 0.77 in discrimination between AF and NSR. Among the top 100 significant shapelets, we used all shapelets with length no greater than a certain threshold (maximum acceptable Shapelet length) for training different models. Increasing the number of shapelet features by varying the threshold from 5 to 30 in the model training improved AUC/AUPRC 0.91/0.68 to 0.94/0.77

Atrioventricular block in children is not common but is a life-threatening disease. As no spontaneous regression of conductive disruption was reported, those sustaining idiopathic atrioventricular blocks are difficult to manage and often require pacemaker implantation. In this study, we presented the first case of a child who surprisingly recovered from idiopathic complete atrioventricular block without intervention 4 years after initial presentation.

Electrocardiogram (ECG) represents the electrical activity of the heart which is used for the diagnosis purpose by the doctors. This paper proposed a method of converting printed ECG data into digital electrocardiogram (ECG) signals. We have developed a system that converts scanned ECG stripped taken from MIT-BIH Normal Sinus Rhythm/ Arrhythmia Database and also data from real patients’ records taken from hospitals into samples as text using digital image processing techniques. Digital data obtained using our proposed model retains all the ECG information of the raw data with precision. The proposed method is reliable for diagnosing ECG based analysis and different cardiac diseases.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

In this paper, we propose electrocardiogram (ECG) signal processing using mini computer Raspberry Pi 3 Model B (RasPi) for Sudden Cardiac Death (SCD) prediction. AD8232 and TL084 are ICs used for ECG signal processing until R peak was detected. Poincare Plot of RR Interval Differences (PORRID) was used by the RasPi to process the R peak until the PORRID parameters obtained. Fuzzy classification was employed to make decision of SCD probability. The system was tested with 6 data sets of each Normal Sinus Rhythm (NSR) and SCD. The system successfully categorized the 12 data sets according to the information in the database. Furthermore, 10 persons experienced actual implementation and the highest percentage of SCD probability is 36.82%.

El objetivo de este trabajo fue analizar en el plano Entropía-Complejidad (HxC) un conjunto de series de tiempo provenientes de ECG de seres humanos con el objetivo de discriminar registros de dos grupos diferentes de pacientes: los de ritmo sinusal normal, y los de arritmias cardíacas.Las series de tiempo utilizadas en este artículo se obtuvieron de la base de registros de ECG de PhysioNet. El set estuco compuesto por 47 señales de pacientes con arritmias cardíacas diagnosticadas y 18 señales de pacientes con ritmo sinusal normal.El plano HxC se construyó con la entropía de Shannon como eje X, y con la complejidad estadística como eje Y. Dado que el contenido de información de las series de tiempo se transmite en forma de una función de distribución de probabilidad, FDP, ésta se obtuvo por medio de la metodología propuesta por Bandt & Pompe (2002).Los valores medios de complejidad estadística y entropía de Shannon normalizada se calcularon y analizaron en el plano HxC para cada serie de tiempo, donde los valores medios de complejidad de las series de tiempo de ECG de pacientes con arritmias diagnosticadas fueron mayores que el grupo de ritmo sinusal normal. Por otro lado, los valores medios de la entropía de Shannon para los pacientes con arritmias fueron más bajos que los del grupo de ritmo sinusal normal. Estas notorias características hicieron posible la discriminación de los diferentes espacios para ambos grupos de señales en el plano HxC. Los resultados se analizaron mediante una hipótesis de prueba estadística multivariada.La metodología propuesta tiene una notable simplicidad conceptual, velocidad computacional, y robustez al ruido, mostrando además una promisoria eficacia en la detección de patologías cardiovasculares.

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.