A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

Leadwise clustering multi-branch network for multi-label ECG classification.

The 12-lead electrocardiogram (ECG) is widely used for diagnosing cardiovascular diseases in clinical practice. Recently, deep learning methods have become increasingly effective for automatically classifying ECG signals. However, most current research simply combines the 12-lead ECG signals into a matrix without fully considering the intrinsic relationships between the leads and the heart's structure. To better utilize medical domain knowledge, we propose a multi-branch network for multi-label ECG classification and introduce an intuitive and effective lead grouping strategy. Correspondingly, we design multi-branch networks where each branch employs a multi-scale convolutional network structure to extract more comprehensive features, with each branch corresponding to a lead combination. To better integrate features from different leads, we propose a feature weighting fusion module. We evaluate our method on the PTB-XL dataset for classifying 4 arrhythmia types and normal rhythm, and on the China Physiological Signal Challenge 2018 (CPSC2018) database for classifying 8 arrhythmia types and normal rhythm. Experimental results on multiple multi-label datasets demonstrate that our proposed multi-branch network outperforms state-of-the-art networks in multi-label classification tasks.

"Re-evaluation of variants of uncertain significance in patients with hereditary arrhythmogenic disorders".

BACKGROUND: Genetic diagnostics support the diagnosis of hereditary arrhythmogenic diseases, but variants of uncertain significance (VUS) complicate matters, emphasising the need for regular reassessment. Our study aims to reanalyse rare variants in different genes in order to decrease VUS diagnoses and thus improve risk stratification and personalized treatment for patients with arrhythmogenic disorders. METHODS: Genomic DNA was analysed using Sanger sequencing and next-generation sequencing (NGS). The Data was evaluated using various databases and in silico prediction tools and classified according to current ACMG standards by two independent experts. RESULTS: We identified 53 VUS in 30 genes, of which 17 variants (32%) were reclassified. 13% each were downgraded to likely benign (LB) and benign (B) and 6% were upgraded to likely pathogenic (LP). Reclassifications mainly occurred among variants initially classified in 2017-2019, with rates ranging from 50 to 60%. CONCLUSION: The results support the assumption that regular reclassification of VUS is important, as it provides new insights for genetic diagnostics, that benefit patients and guide therapeutic approach.

[Diagnostic and intervention value of implantable cardiac monitor in patients over 60 years of age with unexplained syncope].

Objective: To investigate the value of implantable cardiac monitor (ICM) in the diagnosis and treatment of patients over 60 years old with unexplained syncope. Methods: This was a multi-center, prospective cohort study. Between June 2018 and April 2021, patients over the age of 60 with unexplained syncope at Beijing Hospital, Fuwai Hospital, Beijing Anzhen Hospital and Puren Hospital were enrolled. Patients were divided into 2 groups based on their decision to receive ICM implantation (implantation group and conventional follow-up group). The endpoint was the recurrence of syncope and cardiogenic syncope as determined by positive cardiac arrhythmia events recorded at the ICM or diagnosed during routine follow-up. Kaplan-Meier survival analysis was used to compare the differences of cumulative diagnostic rate between the 2 groups. A multivariate Cox regression analysis was performed to determine independent predictors of diagnosis of cardiogenic syncope in patients with unexplained syncope. Results: A total of 198 patients with unexplained syncope, aged (72.9±8.25) years, were followed for 558.0 (296.0,877.0) d, including 98 males (49.5%). There were 100 (50.5%) patients in the implantation group and 98 (49.5%) in the conventional follow-up group. Compared with conventional follow-up group, patients in the implantation group were older, more likely to have comorbidities, had a higher proportion of first degree atrioventricular block indicated by baseline electrocardiogram, and had a lower body mass index (all P<0.05). During the follow-up period, positive cardiac arrhythmia events were recorded in 58 (58.0%) patients in the ICM group. The diagnosis rate (42.0% (42/100) vs. 4.1% (4/98), P<0.001) and the intervention rate (37.0% (37/100) vs. 2.0% (2/98), P<0.001) of cardiogenic syncope in the implantation group were higher than those in the conventional follow-up group (all P<0.001). Kaplan-Meier survival analysis showed that the cumulative diagnostic rate of cardiogenic syncope was significantly higher in the implantation group than in the traditional follow-up group (HR=11.66, 95%CI 6.49-20.98, log-rank P<0.001). Multivariate analysis indicated that ICM implantation, previous atrial fibrillation, diabetes mellitus or first degree atrioventricular block in baseline electrocardiogram were independent predictors for cardiogenic syncope (all P<0.05). Conclusions: ICM implantation improves the diagnosis and intervention rates in patients with unexplained syncope, and increases diagnostic efficiency in patients with unexplained syncope.

A High-Performance Anti-Noise Algorithm for Arrhythmia Recognition.

In recent years, the incidence of cardiac arrhythmias has been on the rise because of changes in lifestyle and the aging population. Electrocardiograms (ECGs) are widely used for the automated diagnosis of cardiac arrhythmias. However, existing models possess poor noise robustness and complex structures, limiting their effectiveness. To solve these problems, this paper proposes an arrhythmia recognition system with excellent anti-noise performance: a convolutionally optimized broad learning system (COBLS). In the proposed COBLS method, the signal is convolved with blind source separation using a signal analysis method based on high-order-statistic independent component analysis (ICA). The constructed feature matrix is further feature-extracted and dimensionally reduced using principal component analysis (PCA), which reveals the essence of the signal. The linear feature correlation between the data can be effectively reduced, and redundant attributes can be eliminated to obtain a low-dimensional feature matrix that retains the essential features of the classification model. Then, arrhythmia recognition is realized by combining this matrix with the broad learning system (BLS). Subsequently, the model was evaluated using the MIT-BIH arrhythmia database and the MIT-BIH noise stress test database. The outcomes of the experiments demonstrate exceptional performance, with impressive achievements in terms of the overall accuracy, overall precision, overall sensitivity, and overall F1-score. Specifically, the results indicate outstanding performance, with figures reaching 99.11% for the overall accuracy, 96.95% for the overall precision, 89.71% for the overall sensitivity, and 93.01% for the overall F1-score across all four classification experiments. The model proposed in this paper shows excellent performance, with 24 dB, 18 dB, and 12 dB signal-to-noise ratios.

The Association Between Late Gadolinium Enhancement by Cardiac Magnetic Resonance and Ventricular Arrhythmia in Patients With Mitral Valve Prolapse: A Systematic Review and Meta-Analysis.

INTRODUCTION: Malignant ventricular arrhythmia (VA) and sudden cardiac death (SCD) have been reported in patients with mitral valve prolapse (MVP); however, effective risk stratification methods are still lacking. Myocardial fibrosis is thought to play an important role in the development of VA; however, observational studies have produced contradictory findings regarding the relationship between VA and late gadolinium enhancement (LGE) in MVP patients. The aim of this meta-analysis and systematic review of observational studies was to investigate the association between left ventricular LGE and VA in patients with MVP. METHODS: We searched the PubMed, Embase, and Web of Science databases from 1993 to 2023 to identify case-control, cross-sectional, and cohort studies that compared the incidence of VA in patients with MVP who had left ventricular LGE and those without left ventricular LGE. RESULTS: A total of 1464 subjects with MVP from 12 observational studies met the eligibility criteria. Among them, VA episodes were reported in 221 individuals (15.1%). Meta-analysis demonstrated that the presence of left ventricular LGE was significantly associated with an increased risk of VA (pooled risk ratio 2.96, 95% CI: 2.26-3.88, p for heterogeneity = 0.07, I2 = 40%). However, a meta-regression analysis of the prevalence of mitral regurgitation (MR) showed that the severity of MR did not significantly affect the association between the occurrence of LGE and VA (p = 0.079). CONCLUSION: The detection of LGE could be helpful for stratifying the risk of VA in patients with MVP.

Association between obstructive sleep apnea and arrhythmia and heart rate variability among hypertensive patients.

BACKGROUND: The relationship between obstructive sleep apnea (OSA) and the occurrence of arrhythmias and heart rate variability (HRV) in hypertensive patients is not elucidated. Our study investigates the association between OSA, arrhythmias, and HRV in hypertensive patients. METHODS: We conducted a cross-sectional analysis involving hypertensive patients divided based on their apnea-hypopnea index (AHI) into two groups: the AHI ≤ 15 and the AHI > 15. All participants underwent polysomnography (PSG), 24-hour dynamic electrocardiography (DCG), cardiac Doppler ultrasound, and other relevant evaluations. RESULTS: The AHI > 15 group showed a significantly higher prevalence of frequent atrial premature beats and atrial tachycardia (P = 0.030 and P = 0.035, respectively) than the AHI ≤ 15 group. Time-domain analysis indicated that the standard deviation of normal-to-normal R-R intervals (SDNN) and the standard deviation of every 5-minute normal-to-normal R-R intervals (SDANN) were significantly higher in the AHI > 15 group (P = 0.020 and P = 0.033, respectively). Frequency domain analysis revealed that the low-frequency (LF), high-frequency (HF) components, and the LF/HF ratio were also significantly elevated in the AHI > 15 group (P < 0.001, P = 0.031, and P = 0.028, respectively). Furthermore, left atrial diameter (LAD) was significantly larger in the AHI > 15 group (P < 0.001). Both univariate and multivariable linear regression analyses confirmed a significant association between PSG-derived independent variables and the dependent HRV parameters SDNN, LF, and LF/HF ratio (F = 8.929, P < 0.001; F = 14.832, P < 0.001; F = 5.917, P = 0.016, respectively). CONCLUSIONS: Hypertensive patients with AHI > 15 are at an increased risk for atrial arrhythmias and left atrial dilation, with HRV significantly correlating with OSA severity.

[Cardiac channelopathies in the context of hereditary arrhythmia syndromes]. / Kardiale Kanalopathien im Kontext hereditärer Arrhythmiesyndrome.

Genetic arrhythmia disorders are rare diseases; however, they are a common cause of sudden cardiac death in children, adolescents, and young adults. In principle, a distinction can be made between channelopathies and cardiomyopathies in the context of genetic diseases. This paper focuses on the channelopathies long and short QT syndrome, Brugada syndrome, and catecholaminergic polymorphic ventricular tachycardia (CPVT). Early diagnosis of these diseases is essential, as drug therapy, behavioral measures, and if necessary, implantation of a cardioverter defibrillator can significantly improve the prognosis and quality of life of patients. This paper highlights the pathophysiological and genetic basis of these channelopathies, describes their clinical manifestations, and comments on the principles of diagnosis, risk stratification and therapy.

Comparison of Patient Outcomes Between Leadless vs Transvenous Pacemakers Following Transcatheter Aortic Valve Replacement.

BACKGROUND: Evidence is limited regarding the effectiveness of leadless pacemaker implantation for conduction disturbance following transcatheter aortic valve replacement (TAVR). OBJECTIVES: This study sought to examine the national trends in the use of leadless pacemaker implantation following TAVR and compare its performance with transvenous pacemakers. METHODS: Medicare fee-for-service beneficiaries aged ≥65 years who underwent leadless or transvenous pacemakers following TAVR between 2017 and 2020 were included. Outcomes included in-hospital overall complications as well as midterm (up to 2 years) all-cause death, heart failure hospitalization, infective endocarditis, and device-related complications. Propensity score overlap weighting analysis was used. RESULTS: A total of 10,338 patients (730 leadless vs 9,608 transvenous) were included. Between 2017 and 2020, there was a 3.5-fold increase in the proportion of leadless pacemakers implanted following TAVR. Leadless pacemaker recipients had more comorbidities, including atrial fibrillation and end-stage renal disease. After adjusting for potential confounders, patients with leadless pacemakers experienced a lower rate of in-hospital overall complications compared with patients who received transvenous pacemakers (7.2% vs 10.1%; P = 0.014). In the midterm, we found no significant differences in all-cause death (adjusted HR: 1.13; 95% CI: 0.96-1.32; P = 0.15), heart failure hospitalization (subdistribution HR: 0.89; 95% CI: 0.74-1.08; P = 0.24), or infective endocarditis (subdistribution HR: 0.98; 95% CI: 0.44-2.17; P = 0.95) between the 2 groups, but leadless pacemakers were associated with a lower risk of device-related complications (subdistribution HR: 0.37; 95% CI: 0.21-0.64; P < 0.001). CONCLUSIONS: Leadless pacemakers are increasingly being used for conduction disturbance following TAVR and were associated with a lower rate of in-hospital complications and midterm device-related complications compared to transvenous pacemakers without a difference in midterm mortality.

State-of-the-art pulsed field ablation for cardiac arrhythmias: ongoing evolution and future perspective.

Pulsed field ablation (PFA) is an innovative approach in the field of cardiac electrophysiology aimed at treating cardiac arrhythmias. Unlike traditional catheter ablation energies, which use radiofrequency or cryothermal energy to create lesions in the heart, PFA utilizes pulsed electric fields to induce irreversible electroporation, leading to targeted tissue destruction. This state-of-the-art review summarizes biophysical principles and clinical applications of PFA, highlighting its potential advantages over conventional ablation methods. Clinical data of contemporary PFA devices are discussed, which combine predictable procedural outcomes and a reduced risk of thermal collateral damage. Overall, these technological developments have propelled the rapid evolution of contemporary PFA catheters, with future advancements potentially impacting patient care.

Energy-efficient dynamic sensor time series classification for edge health devices.

BACKGROUND AND OBJECTIVE: Time series data plays a crucial role in the realm of the Internet of Things Medical (IoMT). Through machine learning (ML) algorithms, online time series classification in IoMT systems enables reliable real-time disease detection. Deploying ML algorithms on edge health devices can reduce latency and safeguard patients' privacy. However, the limited computational resources of these devices underscore the need for more energy-efficient algorithms. Furthermore, online time series classification inevitably faces the challenges of concept drift (CD) and catastrophic forgetting (CF). To address these challenges, this study proposes an energy-efficient Online Time series classification algorithm that can solve CF and CD for health devices, called OTCD. METHODS: OTCD first detects the appearance of concept drift and performs prototype updates to mitigate its impact. Afterward, it standardizes the potential space distribution and selectively preserves key training parameters to address CF. This approach reduces the required memory and enhances energy efficiency. To evaluate the performance of the proposed model in real-time health monitoring tasks, we utilize electrocardiogram (ECG) and photoplethysmogram (PPG) data. By adopting various feature extractors, three arrhythmia classification models are compared. To assess the energy efficiency of OTCD, we conduct runtime tests on each dataset. Additionally, the OTCD is compared with state-of-the-art (SOTA) dynamic time series classification models for performance evaluation. RESULTS: The OTCD algorithm outperforms existing SOTA time series classification algorithms in IoMT. In particular, OTCD is on average 2.77% to 14.74% more accurate than other models on the MIT-BIH arrhythmia dataset. Additionally, it consumes low memory (1 KB) and performs computations at a rate of 0.004 GFLOPs per second, leading to energy savings and high time efficiency. CONCLUSION: Our proposed algorithm, OTCD, enables efficient real-time classification of medical time series on edge health devices. Experimental results demonstrate its significant competitiveness, offering promising prospects for safe and reliable healthcare.

A Clinical Diagnostic Test for Calcium Release Deficiency Syndrome.

Importance: Sudden death and cardiac arrest frequently occur without explanation, even after a thorough clinical evaluation. Calcium release deficiency syndrome (CRDS), a life-threatening genetic arrhythmia syndrome, is undetectable with standard testing and leads to unexplained cardiac arrest. Objective: To explore the cardiac repolarization response on an electrocardiogram after brief tachycardia and a pause as a clinical diagnostic test for CRDS. Design, Setting, and Participants: An international, multicenter, case-control study including individual cases of CRDS, 3 patient control groups (individuals with suspected supraventricular tachycardia; survivors of unexplained cardiac arrest [UCA]; and individuals with genotype-positive catecholaminergic polymorphic ventricular tachycardia [CPVT]), and genetic mouse models (CRDS, wild type, and CPVT were used to define the cellular mechanism) conducted at 10 centers in 7 countries. Patient tracings were recorded between June 2005 and December 2023, and the analyses were performed from April 2023 to December 2023. Intervention: Brief tachycardia and a subsequent pause (either spontaneous or mediated through cardiac pacing). Main Outcomes and Measures: Change in QT interval and change in T-wave amplitude (defined as the difference between their absolute values on the postpause sinus beat and the last beat prior to tachycardia). Results: Among 10 case patients with CRDS, 45 control patients with suspected supraventricular tachycardia, 10 control patients who experienced UCA, and 3 control patients with genotype-positive CPVT, the median change in T-wave amplitude on the postpause sinus beat (after brief ventricular tachycardia at ≥150 beats/min) was higher in patients with CRDS (P < .001). The smallest change in T-wave amplitude was 0.250 mV for a CRDS case patient compared with the largest change in T-wave amplitude of 0.160 mV for a control patient, indicating 100% discrimination. Although the median change in QT interval was longer in CRDS cases (P = .002), an overlap between the cases and controls was present. The genetic mouse models recapitulated the findings observed in humans and suggested the repolarization response was secondary to a pathologically large systolic release of calcium from the sarcoplasmic reticulum. Conclusions and Relevance: There is a unique repolarization response on an electrocardiogram after provocation with brief tachycardia and a subsequent pause in CRDS cases and mouse models, which is absent from the controls. If these findings are confirmed in larger studies, this easy to perform maneuver may serve as an effective clinical diagnostic test for CRDS and become an important part of the evaluation of cardiac arrest.