Robust wave-feature adaptive heartbeat classification based on self-attention mechanism using a transformer model

Objective: Electrocardiography is a common method for screening cardiovascular diseases. Accurate heartbeat classification assists in diagnosis and has attracted great attention. In this paper, we proposed an automatic heartbeat classification method based on a transformer neural network using a self-attention mechanism. Approach: An adaptive heartbeat segmentation method was designed to selectively focus on the time-dependent representation of heartbeats. A one-dimensional convolution layer was used to embed wave characteristics into symbolic representations, and then, a transformer block using multi-head attention was applied to deal with the dependence of wave-embedding. The model was trained and evaluated using the MIT-BIH arrhythmia database (MIT-DB). To improve the model performance, the model pre-trained on MIT-BIH supraventricular arrhythmia database (MIT-SVDB) was used and fine-tuned on MIT-DB. Main results: The proposed method was verified using the MIT-DB for two groups. In the first group, our method attained F1 scores of 0.86 and 0.96 for the supraventricular ectopic beat (SVEB) class and ventricular ectopic beat (VEB) class, respectively. In the second group, our method achieved an average F1 value of 99.83% and better results than other state-of-the-art methods. Significance: We proposed a novel heartbeat classification method based on a transformer model. This method provides a new solution for real-time electrocardiogram heartbeat classification, which can be applied to wearable devices.

Prevalence of paroxysmal atrial fibrillation in patients with cerebrovascular stroke of undetermined etiology

Background cerebrovascular stroke is major cause of morbidity and disability. Many causes may contribute to its presence, however. Some patients have none of the identified risk factors, yet face the consequences of stroke or transit ischemic attack. This type of stroke known to be stroke of undetermined source or etiology. It has a high rate of recurrence due to the persistence of the unknown etiology. Paroxysmal atrial fibrillation remains the hidden bottom of an iceberg representing a major part of the causes of ischemic cerebrovascular stroke of undetermined etiology . Aim and Objectives: to determine the prevalence of subclinical atrial fibrillation in patients with ischemic cerebrovascular stroke of undetermined etiology in a population in Egypt by 48h holter monitoring. Patients and Methods Patients diagnosed with acute cerebrovascular stroke of undetermined etiology at the neurology department of Ain Shams university hospitals underwent 48 hours holter monitoring within the first week of the insulting event. Results This study included 50 patients with cryptogenic stroke (CS) who underwent 48 hours holter monitoring. The patients' ages ranged between 22 and 77 years old (mean age 48.46 ± 12.74years). This study included 34 males and 16 females. Their body mass index BMI ranges from 21-35 kg/m2, with mean BMI of 24.78 ± 2.99 kg/m2. Their left atrial diameter ranges from 26-47mm, with mean diameter of 36.08 ± 5.23mm .Eight out of fifty patients newly diagnosed with subclinical atrial fibrillation with prevalence of 16%. There was statistically significant association between the atrial fibrillation (AF) and both age (p value, 009) and left atrial (LA) diameter (p value, 001) .There was an associated finding that need further investigation about the significant association between the ventricular ectopic beat VEB burden and the presence of AF or stroke. Conclusion The prevalence of paroxysmal atrial fibrillation among the population of ischemic stroke of undetermined etiology in a population in Egypt is close to worldwide percentage and the recent met analysis studies of 11%.

Linear increase in the number of non-pulmonary vein triggers from paroxysmal to persistent and long-standing persistent AF in patients undergoing repeat procedure after successful isolation of pulmona

Introduction This study evaluated the prevalent triggers responsible for recurrence following successful PVI in different types of atrial fibrillation (AF). Methods Consecutive AF patients undergoing repeat catheter ablation with permanently isolated PV were included in the analysis. High-dose isoproterenol challenge (20- 30μg/min for 15–20min) was used to confirm PV reconnection and identify non-PV triggers. Circular mapping catheter (CMC) was used to map the site of origin of significant ectopic activity by comparing the activation sequence of the sinus beat with that of the ectopic beat. For the coronary sinus (CS), ablation catheter was positioned at the level of the mitral valve annulus, parallel to the one positioned in the CS. Left atrial appendage (LAA) firing was detected by placing the CMC in the left superior PV and thus recording far-field potentials from the LAA. Results This prospective study included 1850 AF patients undergoing repeat AF ablation (Table 1), of which 573 (31%) had received one and the remaining 1277 patients had received 2 earlier ablations. Permanent PVI was confirmed with isoproterenol challenge. Table 1 shows the distribution of non-PV triggers. A linear increase in the number of non-PV triggers was observed from PAF to PerAF to LSPAF. Significantly higher number of LSPAF patients had detectable non-PV triggers compared to PerAF and PAF cases. Conclusion We observed a linear increase in the number of non-PV triggers in PAF to PerAF and LSPAF patients experiencing recurrence with successful isolation of PVs. As non-PV triggers are often not targeted by operators, this could be the underlying mechanism for more frequent recurrences in non-paroxysmal AF. FUNDunding Acknowledgement Type of funding sources: None. Table 1

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors’ consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

Komplikasi Ibu Hamil dengan Penyakit Jantung

Penyebab kematian ibu hamil pada operasi non kardiak 25 – 50 % adalah komplikasi kardiovaskuler seperti infark, miokard, edema paru, gagal jantung, aritmia dan tromboemboli perioperatif. Prediktor risiko komplikasi kardiovaskuler pada maternal dan neonatal sangat penting dilakukan agar risiko kematian dapat ditekan semaksimal mungkin. Prediktor mortalitas pada maternal dengan penyakit jantung seperti atrial septal defect (ASD), ventricular septal defect (VSD), persisten ductus aeteiousus (PDA) dengan hipertensi pulmonal, ectopic beat, atrial ventrikuler yang tidak respon terapi, stenosis pulmonal berat dan prolap katub mitral. Tujuan anestesi pada kehamilan dan kelahiran spontan antara lain mengoptimalkan fungsi kardiovaskuler dan respirasi dengan memanipulasi hemodinamik sesuai target dan pemilihan teknik anestesi yang sesuai kondisi pasien. Prinsip dari manajemen anestesi adalah menjaga sirkulasi uteroplasenta dengan mencegah kompresi aorto cava, meminimalkan blok simpatis dan menjaga kecukupan cairan serta monitoring ketat pada ibu dan janin.

Location and coupling interval of an ectopic excitation determine the initiation of atrial fibrillation from the pulmonary veins

Background and Objective: Ectopic beats originating from the pulmonary vein (PV) trigger atrial fibrillation (AF). The purpose of this study was to clarify the electrophysiological determinant of AF initiation from the PVs. Methods: Pacing studies were performed with a single extra stimulus mimicking an ectopic beat in the left superior pulmonary veins (LSPVs) in 62 patients undergoing AF ablation. Inducibility of AF, effective refractory period (ERP) and conduction properties within the PVs were analyzed. Results: A single extra stimulus in LSPV induced AF in 20 patients (32% of all patients) at the mean coupling interval (CI) of 172 ms. A CI-dependent anisotropic conduction at the AF onset was visualized in a 3D-mapping. Onset of AF was site-specific with reproducibility in each individual. Mean ERP in LSPV in the AF inducible group was shorter than that in the AF non-inducible group (182 ± 55 ms vs 254 ± 51 ms, P<0.0001). LSPV ERP dispersion was greater in the AF inducible group than in the AF non-inducible group (45 ± 28 ms vs 27 ± 19 ms, P<0.01). Circumferential intra-PV conduction time (IPVCT) exhibited decremental properties in response to shortening of CI, and the prolongation of IPVCT in the AF inducible site was greater than that in the AF non-inducible site (P<0.05) in each individual. Conclusions: Location and coupling interval of an ectopic excitation ultimately determine the initiation of AF from the PVs. ERP dispersion and circumferential conduction delay may lead to anisotropic conduction and reentry within the PVs that initiate AF.

O-WCNN: an optimized integration of spatial and spectral feature map for arrhythmia classification

The regular monitoring and accurate diagnosis of arrhythmia are critically important, leading to a reduction in mortality rate due to cardiovascular diseases (CVD) such as heart stroke or cardiac arrest. This paper proposes a novel convolutional neural network (CNN) model for arrhythmia classification. The proposed model offers the following improvements compared with traditional CNN models. Firstly, the multi-channel model can concatenate spectral and spatial feature maps. Secondly, the structural unit is composed of a depthwise separable convolution layer followed by activation and batch normalization layers. The structural unit offers effective utilization of network parameters. Also, the optimization of hyperparameters is done using Hyperopt library, based on Sequential Model-Based Global Optimization algorithm (SMBO). These improvements make the network more efficient and accurate for arrhythmia classification. The proposed model is evaluated using tenfold cross-validation following both subject-oriented inter-patient and class-oriented intra-patient evaluation protocols. Our model achieved 99.48% and 99.46% accuracy in VEB (ventricular ectopic beat) and SVEB (supraventricular ectopic beat) class classification, respectively. The model is compared with state-of-the-art models and has shown significant performance improvement.

Polymorphic ventricular tachycardia, ischaemic ventricular fibrillation, and torsade de pointes: importance of the QT and the coupling interval in the differential diagnosis

Aims Distinctive types of polymorphic ventricular tachycardia (VT) respond differently to different forms of therapy. We therefore performed the present study to define the electrocardiographic characteristics of different forms of polymorphic VT. Methods and results We studied 190 patients for whom the onset of 305 polymorphic VT events was available. The study group included 87 patients with coronary artery disease who had spontaneous polymorphic VT triggered by short-coupled extrasystoles in the absence of myocardial ischaemia. This group included 32 patients who had a long QT interval but nevertheless had their polymorphic VT triggered by ectopic beats with short coupling interval, a subcategory termed ‘pseudo-torsade de pointes] (TdP). For comparison, we included 50 patients who had ventricular fibrillation (VF) during acute myocardial infarction (‘ischaemic VF’ group) and 53 patients with drug-induced TdP (‘true TdP’ group). The QT of patients with pseudo-TdP was (by definition) longer than that of patients with polymorphic VT and normal QT (QTc 491.4 ± 25.2 ms vs. 447.3 ± 55.6 ms, P &lt; 0.001). However, their QT was significantly shorter than that of patients with true TdP (QTc 564.6 ± 75.6 ms, P &lt; 0.001). Importantly, the coupling interval of the ectopic beat triggering the arrhythmia was just as short during pseudo-TdP as during polymorphic VT with normal QT (359.1 ± 38.1 ms vs. 356.6 ± 39.4 ms, P = 0.467) but was much shorter than during true TdP (581.2 ± 95.3 ms, P &lt; 0.001). Conclusions The coupling interval helps discriminate between polymorphic VT that occurs despite a long QT interval (pseudo-TdP) and polymorphic arrhythmias striking because of a long QT (true TdP).

Estimating Ectopic Beat Probability with Simplified Statistical Models that Account for Experimental Uncertainty

Ectopic beats (EBs) are cellular arrhythmias that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. Here, we develop a simplified approach in which logistic regression models (LRMs) are used to define a mapping between the parameters of complex cell models and the probability of EBs (P(EB)). As an example, in this study, we build an LRM for P(EB) as a function of diastolic cytosolic Ca2+ concentration ([Ca2+]i), sarcoplasmic reticulum (SR) Ca2+ load, and kinetic parameters of the inward rectifier K+ current (IK1) and ryanodine receptor (RyR). This approach, which we refer to as arrhythmia sensitivity analysis, allows for evaluation of the relationship between these arrhythmic event probabilities and their associated parameters. This LRM is also used to demonstrate how uncertainties in experimentally measured values determine the uncertainty in P(EB). In a study of the role of [Ca2+]SR uncertainty, we show a special property of the uncertainty in P(EB), where with increasing [Ca2+]SR uncertainty, P(EB) uncertainty first increases and then decreases. Lastly, we demonstrate that IK1 suppression, at the level that occurs in heart failure myocytes, increases P(EB).Author summaryAn ectopic beat is an abnormal cellular electrical event which can trigger dangerous arrhythmias in the heart. Complex biophysical models of the cardiac myocyte can be used to reveal how cell properties affect the probability of ectopic beats. However, such analyses can pose a huge computational burden. We develop a simplified approach that enables a highly complex biophysical model to be reduced to a rather simple statistical model from which the functional relationship between myocyte model parameters and the probability of an ectopic beat is determined. We refer to this approach as arrhythmia sensitivity analysis. Given the efficiency of our approach, we also use it to demonstrate how uncertainties in experimentally measured myocyte model parameters determine the uncertainty in ectopic beat probability. We find that, with increasing model parameter uncertainty, the uncertainty in probability of ectopic beat first increases and then decreases. In general, our approach can efficiently analyze the relationship between cardiac myocyte parameters and the probability of ectopic beats and can be used to study how uncertainty of these cardiac myocyte parameters influences the ectopic beat probability.