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We present an unusual case of alternating left anterior and left posterior fascicular block. Given the known risk for progression to complete atrioventricular block with alternating right bundle and left bundle branch block, we performed an electrophysiological study. Findings were consistent with infra-Hisian disease, and the patient underwent pacemaker implantation.

OBJECTIVES: Earlier electrocardiogram (ECG) acquisition for ST-elevation myocardial infarction (STEMI) is associated with earlier percutaneous coronary intervention (PCI) and better patient outcomes. However, the exact relationship between timely ECG and timely PCI is unclear. METHODS: We quantified the influence of door-to-ECG (D2E) time on ECG-to-PCI balloon (E2B) intervention in this three-year retrospective cohort study, including patients from 10 geographically diverse emergency departments (EDs) co-located with a PCI center. The study included 576 STEMI patients excluding those with a screening ECG before ED arrival or non-diagnostic initial ED ECG. We used a linear mixed-effects model to evaluate D2Es influence on E2B with piecewise linear terms for D2E times associated with time intervals designated as ED intake (0-10 min), triage (11-30 min), and main ED (>30 min). We adjusted for demographic and visit characteristics, past medical history, and included ED location as a random effect. RESULTS: The median E2B interval was longer (76 vs 68 min, p 10 min than in those with timely D2E. The proportion of patients identified at the intake, triage, and main ED intervals was 65.8%, 24.9%, and 9.7%, respectively. The D2E and E2B association was statistically significant in the triage phase, where a 1-minute change in D2E was associated with a 1.24-minute change in E2B (95% confidence interval [CI]: 0.44-2.05, p = 0.003). CONCLUSION: Reducing D2E is associated with a shorter E2B. Targeting D2E reduction in patients currently diagnosed during triage (11-30 min) may be the greatest opportunity to improve D2B and could enable 24.9% more ED STEMI patients to achieve timely D2E.

Background: Delayed intervention for ST-segment elevation myocardial infarction (STEMI) is associated with higher mortality. The association of door-to-ECG (D2E) with clinical outcomes has not been directly explored in a contemporary US-based population. Methods: This was a three-year, 10-center, retrospective cohort study of ED-diagnosed patients with STEMI comparing mortality between those who received timely (10 min) diagnostic ECG. Among survivors, we explored left ventricular ejection fraction (LVEF) dysfunction during the STEMI encounter and recovery upon post-discharge follow-up. Results: Mortality was lower among those who received a timely ECG where one-week mortality was 5% (21/420) vs. 10.2% (26/256) among those with untimely ECGs (p = 0.016), and in-hospital mortality was 6.0% (25/420) vs. 10.9% (28/256) (p = 0.028). Data to compare change in LVEF metrics were available in only 24% of patients during the STEMI encounter and 46.5% on discharge follow-up. Conclusions: D2E within 10 min may be associated with a 50% reduction in mortality among ED STEMI patients. LVEF dysfunction is the primary resultant morbidity among STEMI survivors but was infrequently assessed despite low LVEF being an indication for survival-improving therapy. It will be difficult to assess the impact of STEMI care interventions without more consistent LVEF assessment.

Introduction: Chest pain is the second most common chief complaint for patients undergoing evaluation in emergency departments (ED) in the United States. The American Heart Association recommends immediate physician interpretation of all electrocardiograms (ECG) performed for adults with chest pain within 10 minutes to evaluate for the finding of ST-elevation myocardial infarction (STEMI). The ECG machines provide computerized interpretation of each ECG, potentially obviating the need for immediate physician analysis; however, the reliability of computer-interpreted findings of “normal” or “otherwise normal” ECG to rule out STEMI requiring immediate intervention in the ED is unknown.Methods: We performed a prospective cohort analysis of 2,275 ECGs performed in triage in the adult ED of a single academic medical center, comparing the computerized interpretations of “normal” and“otherwise normal” ECGs to those of attending cardiologists. ECGs were obtained with a GE MAC 5500 machine and interpreted using Marquette 12SL.Results: In our study population, a triage ECG with a computerized interpretation of “normal” or “otherwise normal” ECG had a negative predictive value of 100% for STEMI (one-sided, lower 97.5% confidence interval 99.6%). None of the studied patients with these ECG interpretations had a final diagnosis of STEMI, acute coronary syndrome, or other diagnosis requiring emergent cardiac catheterization.Conclusion: In our study population, ECG machine interpretations of “normal” or “otherwise normal” ECG excluded findings of STEMI. The ECGs with these computerized interpretations could safely wait for physician interpretation until the time of patient evaluation without delaying an acute STEMI diagnosis.

Clustering functional data aims to identify unique functional patterns in the entire domain, but this can be challenging due to phase variability that distorts the observed patterns. Curve registration can be used to remove this variability, but determining the appropriate level of warping flexibility can be complicated. Curve registration also requires a target to which a functional object is aligned, typically the cross‐sectional mean of functional objects within the same cluster. However, this mean is unknown prior to clustering. Furthermore, there is a trade‐off between flexible warping and the number of resulting clusters. Removing more phase variability through curve registration can lead to fewer remaining variations in the functional data, resulting in a smaller number of clusters. Thus, the optimal number of clusters and warping flexibility cannot be uniquely identified. We propose to use external information to solve the identification issue. We define a cross validated Kullback‐Leibler information criterion to select the number of clusters and the warping penalty. The criterion is derived from the predictive classification likelihood considering the joint distribution of both the functional data and external variable and penalizes the uncertainty in the cluster membership. We evaluate our method through simulation and apply it to electrocardiographic data collected in the Chronic Renal Insufficiency Cohort study. We identify two distinct clusters of electrocardiogram (ECG) profiles, with the second cluster exhibiting ST segment depression, an indication of cardiac ischemia, compared to the normal ECG profiles in the first cluster. [ABSTRACT FROM AUTHOR]

ABSTRACT Automated categorization of electrocardiogram (ECG) waveforms using deep learning (DL) methods has garnered considerable attention in recent research. However, prevalent DL networks encounter challenges including overfitting, class imbalance, limitations in deeper network training, and high computational demands. To address these issues, this study proposes an Automated ECG Arrhythmia Classification framework employing the Reinforced Visual Geometry Group‐27 (REF‐VGG‐27). Initially, the framework encompasses preprocessing steps such as denoising, R‐peak identification, data balancing, and cross‐validation. For automatic feature extraction and classification, two DL architectures are suggested: a novel hybrid model combining 2D convolutional neural network (2DCNN) with VGG‐16, featuring a deep architecture for extracting morphological characteristics, frequency features related to heart rate variability (HRV), and statistical attributes crucial for identifying atrial fibrillation (AF). Subsequently, to classify arrhythmia patterns, the VGG‐16 Model is employed. Utilizing publicly available ECG image datasets, the proposed model achieved remarkable accuracy benchmarks: 99.61% accuracy, precision of 99.61%, and recall of 99.48%. Comparative analysis with existing approaches substantiates the efficiency and robustness of our model. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is a critical biomedical signal and plays an imperative role in diagnosing cardiovascular disorders. During ECG data acquisition in clinical environment, noise is frequently present. Various noises such as powerline interference (PLI) and baseline wandering (BLW) distort the ECG signal which may lead to incorrect interpretation. Consequently, substantial emphasis has been dedicated to ECG denoising for reliable diagnosis and analysis. In this study, a novel hybrid ECG denoising method based on variational mode decomposition (VMD) and the empirical wavelet transform (EWT) is presented. For effective denoising using the VMD and EWT approach, the noisy ECG signal is decomposed within narrow-band variational mode functions (VMFs). The aim is to remove noise from these narrow-band VMFs. In current approach, the centre frequency of each VMF was computed and utilized to design an adaptive wavelet filter bank using EWT. This leads to effective removal of noise components from the signal. The proposed approach was applied to ECG signals obtained from the MIT-BIH Arrhythmia database. To evaluate the denoising performance, noise sources from the MIT-BIH Noise Stress Test Database (NSTDB) are used for simulation. The assessment of denoising performance in based on two key metrics: the percentage-root-mean-square difference (PRD) and the signal-to-noise ratio (SNR). The findings of the simulation experiment demonstrate that the suggested method has lower percentage root mean square difference and higher signal-to-noise ratio as compared to existing state of the art denoising methods. An average output SNR of 24.03 was achieved, along with a 5% reduction in PRD. [ABSTRACT FROM AUTHOR]

Background: It is essential for nurses to interpret electrocardiograms accurately in cardiac care and emergency departments. Despite rigorous training, nursing students frequently encounter challenges in mastering electrocardiogram interpretation. The purpose of our study was to evaluate the effectiveness of an electrocardiogram interpretation software specifically designed for Iranian nursing students enrolled in undergraduate programs. Methods: A nonequivalent control group pretest-posttest design was conducted at Ramsar University of Medical Sciences in 2020. Using the census sampling method, 75 nursing students from the two educational hospitals were recruited. Participants were divided into two intervention groups and a control group according to their rotations at their respective hospitals. The software contains evidence-based guidelines, interactive learning modules, practice exercises, and real-life examples. Statistical analyses, including chi-square tests and t tests, were conducted using descriptive and inferential statistics. Results: A comparison of the two groups according to demographic characteristics, such as sex, age, was not statistically significant (p > 0.05). The knowledge and skills of the individuals in the control group significantly improved in comparison to those before the intervention. The use of software enhanced students' ability to interpret electrocardiograms. Moreover, there was no statistically significant difference between the intervention and control groups in terms of knowledge and skills of electrocardiogram interpretation. Nursing students reported higher levels of satisfaction after using the software. Conclusion: Moreover, undergraduate nursing students were able to learn more using electrocardiogram interpretation software combined with traditional teaching methods. Combining these two methods in a blended learning approach can improve learning. This software can be integrated into nursing curricula to assist nursing students in interpreting electrocardiograms. [ABSTRACT FROM AUTHOR]

Introduction: End-stage lung disease causes cardiac remodeling and induces electrocardiogram (ECG) changes. On the other way, whether lung transplantation (LTx) in end-stage lung disease patients are associated with ECG change is unknown. The object of this study was to investigate ECG changes before and after LTx in end-stage lung disease patients and whether these changes had clinical significance. Method: This was a single-center retrospective cohort study of 280 end-stage lung disease patients who consecutively underwent LTx at a tertiary referral hospital. ECG findings before LTx and within 1 week and 1, 3, and 6 months after LTx were obtained and analyzed. To find clinical meaning, the ECG at 1 month after LTx was analyzed according to 1-year survival (survivor vs non-survivor groups). Survival data were estimated using the Kaplan–Meier method. Results: Significant differences were observed in the PR interval, QRS duration, QT interval, QTc interval, and heart rate before LTx and 1 month after LTx; the PR interval, QRS duration, QTc interval, and heart rate were decreased. Particularly, the QTc interval was significantly decreased 1 month after LTx, whereas there was no significant change in the QTc interval from 1 to 6 months thereafter. The PR interval, QT interval, QTc interval, and heart rate were significantly different between the survivor and non-survivor groups. The serial changes in QTc interval before LTx and 1 and 3 months after LTx were also significantly different between the survivor and non-survivor groups (p = 0.040 after adjusting for age and body mass index). Upon dividing the patients based on the range of QTc interval change ≤ -8 ms, >-8–10 ms, >10–35 ms, >35 ms), the survival rate was significantly lower in the group whose QTc interval at 1 month after LTx decreased by > 35 m (p = 0.019). Conclusion: LTx in patients with end-stage lung disease may induce ECG changes. Patients whose QTc interval at 1 month after LTx decreased by > 35 ms have a significantly higher 1-year mortality rate. Hence, these ECG changes may have clinical and prognostic significance. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) have been found to be prevalent in society, frequently ending in death. According to the findings of a recent survey, the mortality rate is increasing due to the prevalence of adult cigarette consumption, elevated blood pressure, high cholesterol levels, and obesity. The previously mentioned causes are exacerbating the severity of the condition. A pressing necessity exists for a study on the variability of these factors and their impact on cardiovascular disease (CVD). This involves the use of advanced tools to detect the disease early on and aid in the reduction of fatality rates. With their extensive methodologies that would help in the early CVD prediction and recognition of behavioral patterns in large amounts of data, artificial intelligence, and data mining disciplines offer a broad study potential. The results of these predictions will help physicians make decisions and early diagnoses, decreasing the risk of patient death. This work compares and reports the classification, machine learning, and deep learning algorithms that predict cardiovascular illnesses. For this study, articles from 2012 to 2023 were considered; after filtering, 82 articles were chosen for primary research. Future researchers will benefit from this review on cardiovascular disorders by better understanding the Deep Learning and Machine Learning models now in the healthcare sector. The review encompasses commonly employed methodologies such as support vector machine, decision tree, random forest, and convolutional neural networks (CNNs). Additionally, this survey aggregates and presents information on the performance metrics used to report accuracy. It also goes over the most popular datasets used by various diagnostic models (ECG and PCG signals datasets). In addition, it emphasizes prominent publishers, journals, and conferences that serve as platforms for the evaluation of scholarly works. Additionally, it will facilitate their understanding of the unresolved challenges or hurdles experienced by past researchers. A lack of more extensive and consistent datasets was the most common issue, followed by the need to improve existing models. [ABSTRACT FROM AUTHOR]

Introduction: Typical atrial flutter (AFL) is a macroreentrant tachycardia in which intracardiac conduction rotates counterclockwise around the tricuspid annulus. Typical AFL has specific electrocardiographic characteristics, including a negative sawtooth‐like wave in the inferior lead and a positive F wave in lead V1. This study aimed to analyze the origin of the positive F wave in lead V1, which has not been completely understood. Methods: This study enrolled 10 patients who underwent radiofrequency catheter ablation for a typical AFL. Electroanatomical mapping was performed both during typical AFL and entrainment from the right atrial appendage (RAA). The 12‐lead electrocardiogram (ECG) and three‐dimensional (3D) electroanatomical maps were analyzed. Results: The positive F wave in lead V1 changed during entrainment from the RAA in all the cases. The 3D map during entrainment from the RAA revealed an area of antidromic capture around the RAA, which collided with the orthodromic wave in the anterior right atrium. This area of antidromic capture around the RAA was the only difference from the 3D electroanatomical map of AFL and is considered the cause of the change in the F wave in lead V1 during entrainment. Conclusion: The analysis of the differences in the 12‐lead ECG and 3D maps between tachycardia and entrainment from the RAA clearly demonstrated that activation around the RAA is responsible for the generation of the positive F wave in lead V1 of typical AFL. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To compare multiple noninvasive ECG Methods in pond sliders based upon repeatability, ability to recognize standard waveforms, and measurability. METHODS: The study was performed from November 2023 through January 2024. Ten healthy adult pond turtles were enrolled in the study. ECG tracings were obtained using 4 previously reported and 1 novel ECG methodology, using adhesive patches applied to the prehumeral fossae and abdominal scutes. The 50 ECG tracings were blinded by method and turtle, randomized for evaluation by 4 observers, and assessed for quality on a scale from 0 to 3. RESULTS: Interobserver and intraobserver intraclass correlation coefficients for all tracings were 0.84 and 0.97, respectively, indicating an almost perfect agreement. The average score amongst the observers for each tracing was then averaged by method, ranging from 0.875 to 2.15. The novel method demonstrated a collective average of 2.15 and was the highest scoring method for 8 of 10 turtles. CONCLUSIONS: Electrocardiogram utilizing Methods that apply adhesive patches to the prehumeral fossae and either the abdominal scutes of the plastron or prefemoral fossae in pond turtles can be performed to produce recognizable waveforms. CLINICAL RELEVANCE: Diagnostic tools, such as ECGs, are imperative to enhance veterinary care in nonconventional species, particularly with the rising trend of exotic pets worldwide. [ABSTRACT FROM AUTHOR]

Premature ventricular contraction (PVC) is one of the most common arrhythmias, originating from ectopic beats in the ventricles. Precision in localizing the origin of PVCs has long been a focal point in electrophysiology research. Machine learning (ML) has developed rapidly in the past two decades with increasingly widespread applications. With the increase of clinical data such as electrocardiograms (ECGs), computed tomography (CT), and magnetic resonance imaging (MRI), ML and its subfields, deep learning (DL), have become powerful analytical tools, playing an increasingly important role in electrophysiological research. In this review, we mainly provide an overview of the development of ML in the localization of PVC origins, including its applications, advantages, disadvantages, and future research directions. This information is intended to serve as a reference for clinicians and researchers, aiding them in better‐utilizing ML techniques for the diagnosis and study of PVC origins. [ABSTRACT FROM AUTHOR]

Background: Severe QT interval prolongation requires monitoring QTc intervals during anaesthesia with recommended therapeutic interventions at a threshold of 500 ms. The need for 12‐lead ECG and lack of standardisation limit such monitoring. We determined whether automated continuous intraoperative QTc monitoring with 5‐lead ECG measures QTc intervals comparable to 12‐lead ECG and whether the interpretation of QTc intervals depends on the correction formulae and the patient's sex. We compared intraoperative QTc times to QTc times from resting ECGs of a population from the same region, to substantiate the hypothesis that patients under general anaesthesia may need specific treatment thresholds. Methods: In this prospective observational study, intraoperative QT/QTc intervals were automatically recorded using 12 and 5‐lead ECG in 100 patients (44% males). QTc values were analysed for sex and formula‐specific aspects after correction for heart rate according to Bazett, Fridericia, Hodges, Framingham, Charbit and QTcRAS, and compared to a regional community‐based cohort. The level of significance was set to α = 0.05. Results: QT interval duration was not significantly different between 12‐lead and 5‐lead ECG (difference − 0.09 ms ± 8.5 ms, p = 0.793). The QTc interval duration significantly differed between the correction formulae (p < 0.001) and between sexes (p < 0.001). Mean intraoperative QTc duration was higher than in resting ECGs from a large community‐based population with the same regional background (438 vs. 417 ms). The incidence of prolonged values >500 ms significantly depended on the correction formula (p < 0.001) and was up to tenfold higher in women versus men. Conclusion: Intraoperative QTc interval measurement using a 5‐lead ECG is valid. Correction formulae and gender influence the intraoperative QTc interval duration and the incidence of pathologically prolonged values according to current limits. The consideration and definition of sex‐specific normal limits for QTc times under general anaesthesia, therefore, warrant further investigation. [ABSTRACT FROM AUTHOR]

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Cardiovascular diseases (CVDs) present a significant global public health threat, contributing to a substantial number of cases involving morbidity and mortality. Therefore, the early and accurate detection of CVDs plays an indispensable role in enhancing patient outcomes. Decades of extensive research on electrocardiography at high frequencies have yielded a wealth of knowledge regarding alterations in the QRS complex during myocardial ischemia, as well as the methodologies to assess and quantify these changes. In recent years, the analysis of high-frequency QRS (HF-QRS) components has emerged as a promising non-invasive approach for diagnosing various cardiovascular conditions. Alterations in HF-QRS amplitude and morphology have demonstrated remarkable sensitivity as diagnostic indicators for myocardial ischemia, often surpassing measures of ST-T segment changes. This comprehensive review aims to provide an intricate overview of the current advancements, challenges, and prospects associated with HF-QRS analysis in the field of CVDs. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) signals generate massive volume of digital data, so they need to be suitably compressed for efficient transmission and storage. Polynomial approximations and polynomial interpolation have been used for ECG data compression where the data signal is described by polynomial coefficients only. Here, we propose approximation using hermite polynomial interpolation with chebyshev nodes for compressing ECG signals that consequently denoises them too. Recommended algorithm is applied on various ECG signals taken from MIT-BIH arrhythmia database without any additional noise as the signals are already contaminated with noise. Performance of the proposed algorithm is evaluated using various performance metrics and compared with some recent compression techniques. Experimental results prove that the proposed method efficiently compresses the ECG signals while preserving the minute details of important morphological features of ECG signal required for clinical diagnosis. [ABSTRACT FROM AUTHOR]

A Field Programmable Gate Array (FPGA) is a semiconductor device based around a Configurable Logic Blocks (CLB) matrix connected by programmable interconnects. FPGA has numerous applications in biomedical signal processing due to its flexible programming and low power consumption. An Electrocardiogram (ECG) is a medical test used to determine heart rates, cardiac activity, and classify arrhythmias. All pass filters have Low Pass Filter (LPF), High Pass Filter (HPF), Band Stop Filter (BSF), and Band Pass Filter (BPF) to produce the exact amplitude in peak detection. However, the separate coefficient for individual filters increases the area in traditional all-pass filters. To overcome this issue, a Reconfigurable All-Pass Filter (RAPF) which considers a single coefficient for all filters and minimizes memory usage, consuming less area and power is employed. The LPF coefficient is utilized to perform the LPF operation, and then the two's complement of this LPF coefficient is computed to produce the HPF coefficient. Next, the two's complement is fed into the Hilbert Transform (HT) to produce the BSF coefficient and determine BSF operations. A RAPF is designed to perform these operations effectively. The RAPF performance is determined using Register, Look Up Table (LUT), Global Buffer (BUFG), Digital Signal Processing (DSP), Power, and Flip Flop (FF). RAPF consumes lesser power of 34 mW for Artix 7 XC7A200TFBG676-2 FPGA device, as opposed to the existing technique, Single Node Reservoir Computing (SNRC) using cumulative mean filter. [ABSTRACT FROM AUTHOR]

Background and Objectives: Accurate diagnosis of cardiovascular diseases often relies on the electrocardiogram (ECG). Since the cardiac vector is located within a three-dimensional space and the standard ECG comprises 12 projections or leads derived from it, redundant information is inherently present. This study aims to quantify this redundancy and its impact on classification tasks using Convolutional Neural Networks (CNNs) in cardiovascular diseases. Methods: We employed signal theory and mutual information to introduce a novel redundancy metric and explored techniques for redundancy augmentation and reduction. This involved lead selection and transformation to evaluate the effects on neural network performance. Results: Our findings indicate that optimizing input configurations through redundancy reduction techniques can enhance the performance of deep learning models in cardiovascular diagnostics, provided that the information is preserved and minimally distorted. Conclusion: For the first time, this research has quantified the redundancy present in the input by validating various redundancy reduction techniques using a CNN. This discovery paves the way for advancing biomedical signal processing research, simplifying model complexity, and enhancing diagnostic performance in cardiovascular medicine within reduced lead systems, such as Holter monitors or wearables. [ABSTRACT FROM AUTHOR]

Cardiac arrhythmias are the leading cause of death and pose a huge health and economic burden globally. Electrocardiography (ECG) is an effective technique for the diagnosis of cardiovascular diseases because of its noninvasive and cost-effective advantages. However, traditional ECG analysis relies heavily on the clinical experience of physicians, which can be challenging and time-consuming to produce valid diagnostic results. This work proposes a new hybrid deep learning model that combines convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) with multi-head attention (CBGM model). Specifically, the model consists of seven convolutional layers with varying filter sizes (4, 16, 32, and 64) and three pooling layers, respectively, while the BiGRU module includes two layers with 64 units each followed by multi-head attention (8-heads). The combination of CNN and BiGRU effectively captures spatio-temporal features of ECG signals, with multi-head attention comprehensively extracted global correlations among multiple segments of ECG signals. The validation in the MIT-BIH arrhythmia database achieved an accuracy of 99.41%, a precision of 99.15%, a specificity of 99.68%, and an F1-Score of 99.21%, indicating its robust performance across different evaluation metrics. Additionally, the model's performance was evaluated on the PTB Diagnostic ECG Database, where it achieved an accuracy of 98.82%, demonstrating its generalization capability. Comparative analysis against previous methods revealed that our proposed CBGM model exhibits more higher performance in automatic classification of arrhythmia and can be helpful for assisting clinicians by enabling real-time detection of cardiac arrhythmias during routine ECG screenings. [ABSTRACT FROM AUTHOR]

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts. [ABSTRACT FROM AUTHOR]

Heart disease has been the leading cause of mortality worldwide in the recent decade. Since 2019, new lung-related infections have increased heart attack mortality. To minimize mortality, a unified framework is needed to predict early diagnosis utilizing different patient data. Existing methods failed to produce automatic solutions on the unified approach to cardiac problems considering lung infections. A unique automated disease diagnosis and classification framework using patient chest X-ray images and Electrocardiogram (ECG) signal is proposed. This integrated framework is unique in diagnosing and monitoring lung disease and cardiac problems utilizing patient X-ray and ECG. The proposed system is called the Deep Intelligence Framework for Diseases Detection (DIFDD). DIFDD procedures include pre-processing, automated feature extraction, and classification. An effective pre-processing method is designed to improve X-ray and ECG data. The 2D and 1D Convolutional Neural Network (CNN) techniques are proposed to extract automated features from pre-processed X-ray and ECG data. According to feature learning, automated detection uses several classifiers. Based on classifier results, a consolidated approach is presented for medical judgment on the patient's health for suitable treatment. The simulation results using the synthetic dataset revealed the efficiency of the proposed DIFDD model over the existing methods. The overall accuracy of the DIFDD model is improved by 1.5% and computational overhead is reduced by 13.56%. [ABSTRACT FROM AUTHOR]

Learning-based denoising techniques have become superior to the traditional assumption-based denoising methods in this modern era. Also, with the advancement of wearable technologies and remote electrocardiogram (ECG) monitoring systems, the requirement for optimal storage has increased due to the limited availability of hardware resources. Therefore, denoising and compression both are essential at the preprocessing stage of the ECG signal. Deep learning-based denoising auto encoder-decoder (DAED) models guarantee cutting-edge performance for these tasks. This article presents a lightweight, adaptive, hybrid Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based DAED model that achieves a signal compression ratio of 64 with high signal-to-noise ratio improvement for the elimination of ECG noises. The novelty of this work lies in the customization of the CNN layers and utilization of the advantages of the GRU layer in a proper channel for compression and denoising ECG signals. The comparative study with other complex deep learning-based DAED arrangements and state-of-the-art denoising techniques shows the proposed model has simplicity in construction and an improved signal-to-noise ratio with minimum mean square error. [ABSTRACT FROM AUTHOR]

Aims The aim of this study was to examine the effect of Reiki in patients with cardiac disease. Methods and results This study was a single-blind, pre–post-test, randomized, placebo-controlled study. Patients from the cardiology outpatient clinic of a training and research hospital were randomized into three groups: Reiki (n = 22), sham (placebo) (n = 21), and control (no treatment) (n = 22). Data were collected using a personal information form, biochemical parameters, cortisol levels, Beck Anxiety Inventory, and electrocardiography analysis. The Reiki group received Reiki to nine main points for 30 min, while the sham Reiki group received the same points during the same period without starting the energy flow. On Day 2, distance Reiki was performed for 30 min. After 1 week, the researchers administered the Beck Anxiety Inventory, assessed the biochemical parameters and cortisol levels, and analysed the electrocardiography again. Of the patients, 52.3% were male and 47.7% were female, and the mean age (years) was 60.45 ± 9.67 years. The control group had a significantly higher post-test cortisol level than the other groups (P = 0.002). According to the post hoc analysis, there was a significant difference between the Reiki vs. control groups and sham vs. control groups (P = 0.002). The control group had a significantly higher post-test cortisol level than the pre-test cortisol level (P = 0.008). Reiki group had a significantly lower mean post-test Beck Anxiety Inventory score than the other groups (P < 0.001). There was no difference between the electrocardiography results of the groups (P > 0.05). Conclusion Reiki reduces blood cortisol levels and anxiety levels in patient with cardiac diseases. Registration ClinicalTrials.gov : NCT05483842 [ABSTRACT FROM AUTHOR]

The evaluation of adult patients suspected of ST-segment elevation myocardial infarction (STEMI) includes a focused history and examination, 12-lead electrocardiogram (ECG), and cardiac serum marker analysis. The ECG plays a pivotal role in the early diagnosis and management of STEMI. A number of ECG entities in this patient population will present with ST-segment elevation and other electrocardiographic abnormalities which can mimic STEMI. In this article, we review the most frequent STEMI mimic patterns, highlight their ECG characteristics, and compare these individual ECG entities to the electrocardiographic abnormalities present with STEMI. [ABSTRACT FROM AUTHOR]

The clinical manifestations of ischemic cardiomyopathy (ICM) bear resemblance to dilated cardiomyopathy (DCM), yet their treatments and prognoses are quite different. Early differentiation between these conditions yields positive outcomes, but the gold standard (coronary angiography) is invasive. The potential use of ECG signals based on variational mode decomposition (VMD) as an alternative remains underexplored. An ECG dataset containing 87 subjects (44 DCM, 43 ICM) is pre-processed for denoising and heartbeat division. Firstly, the ECG signal is processed by empirical mode decomposition (EMD) and VMD. And then, five modes are determined by correlation analysis. Secondly, bispectral analysis is conducted on these modes, extracting corresponding bispectral and nonlinear features. Finally, the features are processed using five machine learning classification models, and a comparative assessment of their classification efficacy is facilitated. The results show that the technique proposed provides a better categorization for DCM and ICM using ECG signals compared to previous approaches, with a highest classification accuracy of 98.30%. Moreover, VMD consistently outperforms EMD under diverse conditions such as different modes, leads, and classifiers. The superiority of VMD on ECG analysis is verified. [ABSTRACT FROM AUTHOR]

Background/Objectives: In cases of myocarditis, electrocardiograms (ECGs) may suggest a pattern of ST-segment elevation myocardial infarction (STEMI) or non-ST-segment elevation myocardial infarction (NSTEMI). NSTEMI patterns are less frequent in myocarditis cases, but it remains unclear if the presence of ST-segment elevation in myocarditis cases is related to a more severe condition and more damage in the myocardium. Methods: This is a retrospective study involving 38 patients admitted to hospital with myocarditis. Patients were divided into two groups: patients with ST-segment elevation (STE) patterns in the ECG (25), and patients without ST-segment elevation (non-STE) patterns (13). The data compared included results from epidemiological, laboratory, and instrumental tests. Data were analysed using IBM SPSS Statistics v26.0. A p value of <0.05 was established as the threshold for statistical significance. Results: C-reactive protein (CRP) levels were higher in the STE group (103.40 ± 82.04 mg/L vs. 43.54 ± 61.93 mg/L, p = 0.017). The left ventricle ejection fraction (LVEF) was significantly higher in the non-STE pattern group (49.71 ± 4.14 vs. 56.58 ± 3.99, p < 0.001). A lower LVEF correlates with higher TnI levels (r= −0.353, p = 0.032) and higher CRP levels (r = −0.554, p < 0.001). Lower left ventricle (LV) strain correlates with higher levels of Troponin I (TnI) (r = −0.641, p = 0.013). Conclusions: LVEFs in the STE group were lower compared to those in the non-STE pattern group. STE pattern was associated with higher CRP levels. Higher TnI levels in cases of myocarditis were associated with lower LV strain and lower LVEF; higher CRP levels also correlated with lower LVEF. Based on a 6-month echocardiographic follow-up, the prognosis of myocarditis was favourable. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Heart failure with reduced ejection fraction (HFrEF) patients need to be risk stratify as guidelines have shown that patients with left ventricular ejection fraction (LVEF) <35% could be prevented from sudden cardiac death (SCD) by insertion of prophylactic implantable cardioverter-defibrillator (ICD). Thus we conducted a retrospective single tertiary centre study to evaluate the used of electrocardiogram (ECG) risk score model in identifying the individuals who at higher risk of SCD. MATERIALS AND METHODS: A total of 356 heart failure with reduced ejection fraction (HFrEF) patients treated at University Malaya Medical Centre between January 2017 and December 2021 were enrolled into this study. The patients' demographics, types of heart failure, medications, and ECG parameters data were collected. The study outcomes were survivor or death in and the cause of death were subdivided into SCD or non-sudden cardiac death (non-SCD). RESULTS: A total of 156 study patients were survivor whereas another 120 had SCD and 70 had non-SCD. There were six ECG parameters that remained significant in the final model, namely the bundle branch block (BBB), abnormal P waves, QRS duration, QTc duration, TpTe interval and PR interval. The significant ECG parameters were combined into a risk score to enumerate prediction ability towards SCD. From our ECG risk score model, subject with =2 ECG abnormalities had more than 3-fold increased risk for SCD (HR 3.739, 95% CI 1.703-8.211, P 0.001) and the risk proportionately increased with increasing ECG abnormalities. CONCLUSION: Our findings suggested that the cumulative ECG risk score model was independently associated with SCD and particularly effective for LVEF <40% where risk stratification model remained scarce. So, we would like to propose for a prospective study to further evaluate our study outcome. [ABSTRACT FROM AUTHOR]

Objective To observe the electrocardiogram (ECG) changes of programmed death receptor 1 (PD-1)/programmed death receptor-ligand 1 (PD-L1) immune checkpoint inhibitors before and after immunotherapy of patients during clinical antitumor process, and to explore the occurrence and influencing factors of cardiotoxicity of immune checkpoint inhibitors. Methods A total of 93 patients with locally advanced or metastatic solid tumors confirmed by pathological diagnosis in Cancer Hospital of Chinese Academy of Medical Sciences from October 1, 2019 to September 30, 2020 were selected and treated with PD-1/PD-L1 inhibitor monotherapy. Groups were divided according to immunotherapy regimen: Group A (drug code: 609A), 16 patients were given 1 mg/kg of the drug for 21 days; Group B (drug code: HX008), 23 patients were treated with 200mg for 21 days; Group C (drug code: GB226), 28 patients were treated with 3mg/kg for 14 days; Group D (drug code: LP002), 26 patients were treated with 900mg for 14 days. The patients were monitored and followed up for 10 cycles. The ECG results of each group were recorded, and the correlation between ECG abnormality and cardiotoxicity was analyzed. Results A total of 75 patients showed abnormal ECG that met the diagnostic criteria. There was no significant difference in abnormal ECG rate after immunotherapy in group A (P＞0.05), while the incidence of adverse cardiac events increased after immunotherapy in group B (P＜0.05), and the abnormal ECG rate increased significantly after chemotherapy in group C and group D. There was statistical difference before and after immunotherapy (P＜0.001). The number of abnormal cases in group A (8 cases, 50.0%, 8/16) was significantly lower than that of group B (20 cases, 87.0%, 20/23). The number of abnormal cases in group C and group D was 24 (85.7%) and 23 (88.4%), respectively, without statistical difference (P＞0.05), but their abnormal rates of ECG were higher than that in group A. The incidence of electrical adverse events in immunotherapy center of patients with underlying diseases was 1.93 times higher than that of patients without underlying diseases. The incidence of central electrical adverse events during immunotherapy in group B, C and D was 6.667, 6.000 and 7.667 times higher than that in group A, respectively. Conclusions The high sensitivity of early ECG changes induced by immune checkpoint inhibitors enables early prediction of related cardiotoxicity. The presence or absence of comorbid underlying disease and drug dosage are correlated with the occurrence of adverse cardiac events, and these early changes provide a evidence for clinical treatment and prevention. [ABSTRACT FROM AUTHOR]

With a growing interest in head-mounted display (HMD)-based virtual reality (VR) environments, there have been lots of studies enhancing user experience (UX) in the context. In particular, the study of motion sickness (MS) symptoms, which are a major barrier to providing a positive UX, is of high importance. This study investigated the effects of degree of freedom (DOF) and field of view (FOV) of HMD on MS symptoms. A user experiment was designed based on a 2 × 2 mixed design with DOF as a between-subject design variable (3-DOF and 6-DOF) and FOV as a within-subject design variable (Narrow and Wide). Participants experienced VR game content in four conditions in random order. MS symptoms were captured using heart rate variability (HRV) and virtual reality sickness questionnaire (VRSQ) at the pre- and post-task moment. The results showed that MS symptoms occurred more in the 3-DOF condition than in the 6-DOF. Further, MS symptoms in DOF conditions were adjusted depending on the FOV. In the Wide condition, we found a significant difference in MS symptoms depending on the DOF, but in the Narrow condition, no significant differences were found. Through this finding, we were able to not only show the effectiveness of HRV as MS measures but also provide meaningful design insights into HMD-based VR practices. [ABSTRACT FROM AUTHOR]

Validation of a Wrist-Worn Optical Heart Rate Monitor During Maximal Treadmill Exercise Testing. JEPonline 2024;27(5): 50-56. Heart rate (HR) watches using photoplethysmography (PPG) are a popular, portable, and practical alternative to electrocardiogram (ECG) for HR monitoring. The purpose of this study was to determine validity for a PPM watch for measuring HR during incremental treadmill exercise using ECG as the criterion. Twenty-two subjects (13 men, 9 women; 35.8 ± 6.3 yr) performed a Bruce treadmill protocol exercise test. HR was recorded at rest and each minute with the Mio Alpha PPM and ECG simultaneously. HR was compared between methods using paired-samples t-tests. Validity coefficients were determined using Pearson correlations. HR had a significant correlation between methods overall (r = 0.97). Significant correlations were observed for rest and each stage (r values from 0.67 to 0.96). HR was significantly different between methods for rest (ECG = 66 ± 13 b·min-1, Mio = 68 ± 16 b·min-1), stage 3 (ECG = 144 ± 13 b·min-1, Mio = 143 ± 13 b·min-1), stage 4 (ECG = 168 ± 13 b·min-1, Mio = 164 ± 14 b·min-1), and stage 5 (ECG = 178 ± 12 b·min-1, Mio = 173 ± 18 b·min-1). There was a strong agreement between the HR methods and mean differences did not exceed 3 b·min-1 except for stage 5. Therefore, a PPM watch may provide accurate HR monitoring at most exercise intensities. [ABSTRACT FROM AUTHOR]

Aims: Arterial hypertension (aHTN) plays a fundamental role in the pathogenesis and prognosis of heart failure with preserved ejection fraction (HFpEF). The risk of heart failure increases with therapy‐resistant arterial hypertension (trHTN), defined as inadequate blood pressure (BP) control ≥140/90 mmHg despite taking ≥3 antihypertensive medications including a diuretic. This study investigates the effects of the BP lowering baroreflex activation therapy (BAT) on cardiac function and morphology in patients with trHTN with and without HFpEF. Methods: Sixty‐four consecutive patients who had been diagnosed with trHTN and received BAT implantation between 2012 and 2016 were prospectively observed. Office BP, electrocardiographic and echocardiographic data were collected before and after BAT implantation. Results: Mean patients' age was 59.1 years, 46.9% were male, and mean body mass index (BMI) was 33.2 kg/m2. The prevalence of diabetes mellitus was 38.8%, atrial fibrillation was 12.2%, and chronic kidney disease (CKD) stage ≥3 was 40.8%. Twenty‐eight patients had trHTN with HFpEF, and 21 patients had trHTN without HFpEF. Patients with HFpEF were significantly older (64.7 vs. 51.6 years, P < 0.0001), had a lower BMI (30.0 vs. 37.2 kg/m2, P < 0.0001), and suffered more often from CKD‐stage ≥3 (64 vs. 20%, P = 0.0032). After BAT implantation, mean office BP dropped in patients with and without HFpEF (from 169 ± 5/86 ± 4 to 143 ± 4/77 ± 3 mmHg [P = 0.0019 for systolic BP and 0.0403 for diastolic BP] and from 170 ± 5/95 ± 4 to 149 ± 6/88 ± 5 mmHg [P = 0.0019 for systolic BP and 0.0763 for diastolic BP]), while a significant reduction of the intake of calcium‐antagonists, α2‐agonists and direct vasodilators, as well as a decrease in average dosage of ACE‐inhibitors and α2‐agonists could be seen. Within the study population, a decrease in heart rate from 74 ± 2 to 67 ± 2 min−1 (P = 0.0062) and lengthening of QRS‐time from 96 ± 3 to 106 ± 4 ms (P = 0.0027) and QTc‐duration from 422 ± 5 to 432 ± 5 ms (P = 0.0184) were detectable. The PQ duration was virtually unchanged. In patients without HF, no significant changes of echocardiographic parameters could be seen. In patients with HFpEF, posterior wall diameter decreased significantly from 14.0 ± 0.5 to 12.7 ± 0.3 mm (P = 0.0125), left ventricular mass (LVM) declined from 278.1 ± 15.8 to 243.9 ± 13.4 g (P = 0.0203), and e′ lateral increased from 8.2 ± 0.4 to 9.0 ± 0.4 cm/s (P = 0.0471). Conclusions: BAT reduced systolic and diastolic BP and was associated with morphological and functional improvement of HFpEF. [ABSTRACT FROM AUTHOR]

Aims: The Selvester scoring system has been derived from ECG parameters for estimating infarct size. However, there is still a lack of evidence for Selvester score as an alternative to cardiac magnetic resonance (CMR) myocardial injury makers for risk stratification and prediction of left ventricular function (LVF) recovery among patients with ST‐segment elevation myocardial infarction (STEMI). Methods and results: This multicentre observational study enrolled 328 STEMI patients (88.4% men, 57.3 ± 10.6 years of age) undergoing CMR examination 1 week post‐reperfusion therapy. Patients with baseline left ventricular ejection fraction (LVEF) < 50% underwent a follow‐up CMR 6 months later, categorized into baseline normal LVF (ejection fraction [EF] ≥ 50% at baseline, n = 155); recovered LVF (EF < 50% at baseline and ≥50% after 6 months, n = 69); and reduced LVF (EF < 50% at baseline and after 6 months, n = 104). The median follow‐up was 4 (3–4) years for all patients, with 61 patients experiencing major adverse cardiovascular event (MACEs). Patients with reduced LVF had a higher risk of MACEs than those with baseline normal LVF (P = 0.01), while the recovered LVF group had no significant difference (P > 0.05). A Selvester score >10 doubled the risk of MACEs in patients with systolic dysfunction (1.91 [1.02 to 3.58], P = 0.04). Additionally, Selvester score, baseline LVEF, transmural infarction, and peak CK‐MB were independent predictors of recovered LVF, with Selvester score providing incremental predictive value to peak CK‐MB in predicting recovered LVF (∆AUC = 0.07, P < 0.05). Conclusions: The Selvester score improves risk stratification among STEMI patients beyond LVEF and provide independent and incremental information to clinical parameters in predicting recovered LVF. [ABSTRACT FROM AUTHOR]

This paper is concerned with an end-to-end electrocardiogram (ECG) signal transformation from a continuous-wave (CW) radar signal using a specialized deep learning model. For this purpose, the presented deep learning model is designed using convolutional neural networks (CNNs) and bidirectional long short-term memory (Bi-LSTM) with a maximum-overlap discrete wavelet transform (MODWT) layer and an adaptive neuro-fuzzy network (ANFN) layer. The proposed method has the advantage of developing existing deep networks and machine learning to reconstruct signals through CW radars to acquire ECG biological information in a non-contact manner. The fully connected (FC) layer of the CNN is replaced by an ANFN layer suitable for resolving black boxes and handling complex nonlinear data. The MODWT layer is activated via discrete wavelet transform frequency decomposition with maximum-overlap to extract ECG-related frequency components from radar signals to generate essential information. In order to evaluate the performance of the proposed model, we use a dataset of clinically recorded vital signs with a synchronized reference sensor signal measured simultaneously. As a result of the experiment, the performance is evaluated by the mean squared error (MSE) between the measured and reconstructed ECG signals. The experimental results reveal that the proposed model shows good performance in comparison to the existing deep learning model. From the performance comparison, we confirm that the ANFN layer preserves the nonlinearity of information received from the model by replacing the fully connected layer used in the conventional deep learning model. [ABSTRACT FROM AUTHOR]

Predicting the onset of cardiovascular disease (CVD) has been a hot topic for researchers for years, and recently, the concept of transfer learning has been gaining traction in this field. Transfer learning (TL) is a process that involves transferring information gained from one task or domain to another related task or domain. This paper comprehensively reviews recent advancements in pre-trained TL models for CVD, focusing on electrocardiogram (ECG) signals. Forty-three articles were chosen from Scopus and Google Scholar sources and reviewed, focusing on the type of CVD detected, the database used, the ECG input format, and the pre-training model used for transfer learning. The results show that more than 80% of the studies utilize 2-dimensional (2D) ECG input from the two most utilized available ECG datasets: MIT-BIH arrhythmia (ARR) and MIT-BIH normal sinus rhythm. alexnet, visual geometry group (VGG), and residual network (ResNet) are among the pre-trained TL models with the highest number used among reviewed articles. Additionally, the development of pre-trained TL models over time has made it possible to detect CVD with ECG signals. It can also address limited data problems, promote the development of more dependable and resilient detection systems, and aid medical professionals in diagnosing CVD and other diseases. [ABSTRACT FROM AUTHOR]

Background: Previous research highlighted variability in electrocardiogram (ECG) readings across patient positions, particularly in the context of COVID‐19 patients with pulmonary complications requiring prone positioning as part of the treatment. Objective: This study aimed to elucidate the effects of prone positioning on ECG parameters and explore its association with the severity of COVID‐19. Methods: A prospective cohort study involved 60 patients diagnosed with COVID‐19 and presenting pulmonary complications. ECGs were recorded in both supine and prone positions, and analyzed for various parameters including heart rate, QRS axis, and QTc interval. Clinical severity was assessed using APACHE II scores and SpO2/FiO2 ratios. Results: Prone positioning led to an increase in heart rate (mean difference: 2.100, 95% CI: 0.471–3.729, p = 0.012), with minor shifts in the QRS axis. Heart rate and QRS axis demonstrated strong positive correlations between positions, with Pearson's correlation coefficients of 0.927 and 0.894, respectively. The study also found a significant association between prolonged QTc intervals in the prone position and elevated APACHE II scores, with a relative risk of 10.75 (95% CI: 1.82–63.64, p = 0.008). Conclusions: The prone positioning caused minor yet significant changes in heart rate and QRS axis. The correlation of prolonged QTc intervals in the prone position with higher APACHE II scores suggests the prognostic relevance of prone ECG in COVID‐19 patients. However, further research is needed to fully understand the clinical implications and mechanisms of these findings. [ABSTRACT FROM AUTHOR]

This exploratory study investigates changes in the autonomic cardiac system of young analog astronauts in a hostile, confined, and isolated environment. It uses linear and nonlinear indices of heart rate variability (HRV) during a Mars analog mission to assess how HRV varies under day and night stressors. This study is guided by the hypothesis that significant HRV changes occur based on adaptation days, aiming to offer insights into autonomic nervous system (ANS) adaptation to environmental stressors. Over five days in August 2022, five analog astronauts faced adverse conditions in the Mojave Desert, simulating Martian conditions. Electrocardiograms were recorded daily for five minutes during morning and evening sessions to extract short-term RR time series. HRV parameters were analyzed using both time- and frequency-domain indices and nonlinear measures. Significant differences in HRV parameters across days highlight the mission environment's impact on autonomic cardiac function. Morning measurements showed significant changes in average RR intervals and heart rate, indicating ANS adaptation. Nonlinear indices such as detrended fluctuation analysis and approximate entropy also showed significant differences, reflecting shifts in autonomic function. The Borg scale indicated reduced perceived exertion over time, aligning with HRV changes. Increased vagal activity during Mars analog adaptation under confinement/isolation may be crucial for cardiovascular adaptation and survival in future space flights. [ABSTRACT FROM AUTHOR]

The paper showcases a consistent diagnostic process that fully automates the categorization of ECG images for cardiac diseases, including potential COVID-19-related complications. We propose a CNN-based model as the central component of this system. It has a customized attention mechanism and also uses advanced data augmentation and pre-processing techniques such as adaptive brightness, accurate resizing, and selective cropping. In this approach, we were concerned about the great variability in clinical ECG images, which can have adverse effects on data classification. This led us to design augmentation methods for this problem. We demonstrated the validity of the model by applying it with the help of the dataset, which has 1937 ECG images showing different heart abnormalities and a satisfactory classification score of 98.73%. Putting cardiovascular conditions at the core of AI applications demonstrates its ability to provide accurate treatment decisions. A well-proven automated system would be a milestone in the cardiovascular diagnostics community that would improve the efficiency and accuracy of disease diagnosis. [ABSTRACT FROM AUTHOR]

Alcohol, a widely commercialized psychotropic drug, and the benzodiazepine Flunitrazepam, an anxiolytic widely prescribed for patients with anxiety and insomnia problems, are well known drugs and both act on the central nervous system. The misuse and the association of these two drugs are public health concerns in several countries and could cause momentary, long-lasting and even lethal neurophysiological problems due to the potentiation of their adverse effects in synergy. The present study observed the result of the association of these drugs on electrophysiological responses in the brain, heart, and respiratory rate in Wistar rats. 8 experimental groups were determined: control, one alcohol group (20% at a dose of 1 ml/100 g VO), three Flunitrazepam groups (doses 0.1; 0.2 and 0.3 mg/kg) and three alcohol-Flunitrazepam groups (20% at a dose of 1 ml/100 g VO of alcohol, combined with 0.1; 0.2 and 0.3 mg/kg of Flunitrazepam, respectively). The results showed that there was a more pronounced reduction in alpha and theta wave power in the alcohol-Flunitrazepam groups, a decrease in the power of beta oscillations and greater sedation. There was a progressive decrease in respiratory rate linked to the increase of Flunitrazepam dose in the alcohol-Flunitrazepam associated administration. It was observed alteration in heart rate and Q-T interval in high doses of Flunitrazepam. Therefore, we conclude that the association alcohol-Flunitrazepam presented deepening of depressant synergistic effects according to the increase in the dose of the benzodiazepine, and this could cause alterations in low frequency brain oscillations, breathing, and hemodynamics of the patient. [ABSTRACT FROM AUTHOR]

Premature ventricular contractions (PVCs) are a common arrhythmia characterized by ectopic excitations within the ventricles. Accurately estimating the ablation site using an electrocardiogram (ECG) is crucial for the initial classification of PVC origins, typically focusing on the right and left ventricular outflow tracts. However, finer classification, specifically identifying the left cusp (LC), anterior cusp (AC), and right cusp (RC), is essential for detailed preoperative planning. This study aims to improve the accuracy of cardiac waveform source estimation and classification in 27 patients with PVCs originating from the pulmonary valve. We utilized an anatomical human model and electromagnetic simulations to estimate wave source positions from 12-lead ECG data. Time-series source points were identified for each measured ECG waveform, focusing on the moment when the distance between the estimated wave source and the pulmonary valve was minimal. Computational analysis revealed that the distance between the estimated wave source and the pulmonary valve was reduced to less than 1 cm, with LC localization achieving errors under 5 mm. Additionally, 74.1% of the subjects were accurately classified into the correct origin (LC, AC, or RC), with each origin demonstrating the highest percentage of subjects corresponding to the targeted excitation origin. Our findings underscore the novel potential of this source localization method as a valuable complement to traditional waveform classification, offering enhanced diagnostic precision and improved preoperative planning for PVC ablation procedures. [ABSTRACT FROM AUTHOR]

ABSTRACT Cardiac arrhythmia refers to irregular heartbeats caused by anomalies in electrical transmission in the heart muscle, and it is an important threat to cardiovascular health. Conventional monitoring and diagnosis still depend on the laborious visual examination of electrocardiogram (ECG) devices, even though ECG signals are dynamic and complex. This paper discusses the need for an automated system to assist clinicians in efficiently recognizing arrhythmias. The existing machine‐learning (ML) algorithms have extensive training cycles and require manual feature selection; to eliminate this, we present a novel deep learning (DL) architecture. Our research introduces a novel approach to ECG classification by combining the vision transformer (ViT) and the capsule network (CapsNet) into a hybrid model named ViT‐Cap. We conduct necessary preprocessing operations, including noise removal and signal‐to‐image conversion using short‐time Fourier transform (SIFT) and continuous wavelet transform (CWT) algorithms, on both normal and abnormal ECG data obtained from the MIT‐BIH database. The proposed model intelligently focuses on crucial features by leveraging global and local attention to explore spectrogram and scalogram image data. Initially, the model divides the images into smaller patches and linearly embeds each patch. Features are then extracted using a transformer encoder, followed by classification using the capsule module with feature vectors from the ViT module. Comparisons with existing conventional models show that our proposed model outperforms the original ViT and CapsNet in terms of classification accuracy for both binary and multi‐class ECG classification. The experimental findings demonstrate an accuracy of 99% on both scalogram and spectrogram images. Comparative analysis with state‐of‐the‐art methodologies confirms the superiority of our framework. Additionally, we configure a field‐programmable gate array (FPGA) to implement the proposed model for real‐time arrhythmia classification, aiming to enhance user‐friendliness and speed. Despite numerous suggestions for high‐performance FPGA accelerators in the literature, our FPGA‐based accelerator utilizes optimization of loop parallelization, FP data, and multiply accumulation (MAC) unit. Our accelerator architecture achieves a 57% reduction in processing time and utilizes fewer resources compared to a floating‐point (FlP) design. [ABSTRACT FROM AUTHOR]

Aim: This study was conducted to develop an electrocardiogram education program that incorporates an HTML webpage and blended learning methods to enhance electrocardiogram interpretation skills. Through continual and efficient education, the program aims to assist nurses in providing appropriate care and treatment to patients. Design: Pre-post design study. Methods: We developed an electrocardiogram interpretation HTML webpage based on an electrocardiogram interpretation algorithm and implemented an 18-week (2023.5.15 ~ 2023.9.22) electrocardiogram education program, which included daily 5-minute training sessions. Twenty-seven ward nurses were provided with the URL (https://ecgweb.github.io/ECGwebEN) to the electrocardiogram interpretation HTML webpage and shared one electrocardiogram case daily for self-interpretation. Electrocardiogram interpretation performance and confidence were evaluated through questionnaires at three phases: before the program, after 6 weeks of basic electrocardiogram and arrhythmia education, and after 12 weeks of application of the electrocardiogram interpretation HTML webpage and case-based lecture education. The statistical tests used were repeated-measures ANOVA or the Wilcoxon signed-rank test. Results: The average score for electrocardiogram interpretation performance before the electrocardiogram education program was 11.89(SD = 3.50), after 6 weeks of basic electrocardiogram and arrhythmia education it was 14.15(SD = 3.68), and after 12 weeks of application of the electrocardiogram interpretation HTML webpage and case-based lecture education, it was 15.56(SD = 3.04). This shows that electrocardiogram interpretation performance significantly improved over time (p <.001). Additionally, post-hoc analysis revealed significant differences in electrocardiogram interpretation performance at each stage, i.e., before, during, and after the application of an electrocardiogram education program. Furthermore, the electrocardiogram interpretation confidence questionnaire score (pre-Median 18, IQR = 5; post-Median 23, IQR = 3) was improved significantly after the completion of the 18-week education program (p <.001). Conclusions: Based on the results of this study, we believe that an electrocardiogram education program using HTML webpage, and a blended teaching method would be very beneficial for maintaining and improving electrocardiogram interpretation skills of clinical nurses. Such a program can help nurses interpret electrocardiograms more effectively and assist them in making important decisions in patient care. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular events following anti-malarial treatment are reported infrequently; only a few studies have reported adverse outcomes. This case presentation emphasizes cardiological assessment of Brugada syndrome, presenting as life-threatening arrhythmia during anti-malarial treatment. Without screening and untreated, this disease may lead to sudden cardiac death. Case presentation: This is a case of 23-year-old male who initially presented with palpitations followed by syncope and shortness of breath with a history of malaria. He had switched treatment from quinine to dihydroartemisinin-piperaquine (DHP). Further investigations revealed the ST elevation electrocardiogram pattern typical of Brugada syndrome, confirmed with flecainide challenge test. Subsequently, anti-malarial treatment was stopped and an Implantable Cardioverter Defibrillator (ICD) was inserted. Conclusions: Another possible cause of arrhythmic events happened following anti-malarial consumption. This case highlights the possibility of proarrhytmogenic mechanism of malaria infection and anti-malarial drug resulting in typical manifestations of Brugada syndrome. [ABSTRACT FROM AUTHOR]

Electrophysiological aspects of cardiac amyloidosis (CA) are still poorly explored compared to other aspects of the disease. However, electrocardiogram (ECG) abnormalities play an important role in CA diagnosis and prognosis and the management of arrhythmias is a crucial part of CA treatment. Low voltages and a pseudonecrosis pattern with poor R-wave progression in precordial leads are especially common findings. These are useful for CA diagnosis and risk stratification, especially when combined with clinical or echocardiographic findings. Both ventricular and supraventricular arrhythmias are common in CA, especially in transthyretin amyloidosis (ATTR), and their prevalence is related to disease progression. Sustained and non-sustained ventricular tachycardias' prognostic role is still debated, and, to date, there is a lack of specific indications for implantable cardiac defibrillator (ICD). On the other hand, atrial fibrillation (AF) is the most common supraventricular arrhythmia with a prevalence of up to 88% of ATTR patients. Anticoagulation should be considered irrespective of CHADsVA score. Furthermore, even if AF seems to not be an independent prognostic factor in CA, its treatment for symptom control is still crucial. Finally, conduction disturbances and bradyarrhythmias are also common, requiring pacemaker implantation in up to 40% of patients. [ABSTRACT FROM AUTHOR]

There are two widely used methods to measure the cardiac cycle and obtain heart rate measurements: the electrocardiogram (ECG) and the photoplethysmogram (PPG). The sensors used in these methods have gained great popularity in wearable devices, which have extended cardiac monitoring beyond the hospital environment. However, the continuous monitoring of ECG signals via mobile devices is challenging, as it requires users to keep their fingers pressed on the device during data collection, making it unfeasible in the long term. On the other hand, the PPG does not contain this limitation. However, the medical knowledge to diagnose these anomalies from this sign is limited by the need for familiarity, since the ECG is studied and used in the literature as the gold standard. To minimize this problem, this work proposes a method, PPG2ECG, that uses the correlation between the domains of PPG and ECG signals to infer from the PPG signal the waveform of the ECG signal. PPG2ECG consists of mapping between domains by applying a set of convolution filters, learning to transform a PPG input signal into an ECG output signal using a U-net inception neural network architecture. We assessed our proposed method using two evaluation strategies based on personalized and generalized models and achieved mean error values of 0.015 and 0.026, respectively. Our method overcomes the limitations of previous approaches by providing an accurate and feasible method for continuous monitoring of ECG signals through PPG signals. The short distances between the infer-red ECG and the original ECG demonstrate the feasibility and potential of our method to assist in the early identification of heart diseases. [ABSTRACT FROM AUTHOR]

Global stress is widespread in today's post-pandemic world of political and economic uncertainty. Vibroacoustic technology is a vibrotactile intervention with multiple uses, but its impact on stress lacks interpretation. This research assessed if the vibroacoustic technology of a Vibroacoustic Sound Massage (VSM) can reduce psychological, physiological, and cognitive stress. The Perceived Stress Scale (PSS-10) and electrocardiogram (ECG) and electroencephalogram (EEG) biosignals were used to quantify results. Participants were divided into Low-Stress and High-Stress groups. The ECG results show VSM increased parasympathetic activity for all participants, with the Low-Stress group being more affected. The EEG results indicate increased concentration, reduced arousal, and increased relaxation, with participant well-being non-significantly affected, though variability in this metric was homogenised after VSM. Together, these results validate VSM as an effective support tool for stress management; however, further research is required. [ABSTRACT FROM AUTHOR]

Monitoring heart rate (HR) through photoplethysmography (PPG) signals is a challenging task due to the complexities involved, even during routine daily activities. These signals can indeed be heavily contaminated by significant motion artifacts resulting from the subjects' movements, which can lead to inaccurate heart rate estimations. In this paper, our objective is to present an innovative necklace sensor that employs low-computational-cost algorithms for heart rate estimation in individuals performing non-abrupt movements, specifically drivers. Our solution facilitates the acquisition of signals with limited motion artifacts and provides acceptable heart rate estimations at a low computational cost. More specifically, we propose a wearable sensor necklace for assessing a driver's well-being by providing information about the driver's physiological condition and potential stress indicators through HR data. This innovative necklace enables real-time HR monitoring within a sleek and ergonomic design, facilitating seamless and continuous data gathering while driving. Prioritizing user comfort, the necklace's design ensures ease of wear, allowing for extended use without disrupting driving activities. The collected physiological data can be transmitted wirelessly to a mobile application for instant analysis and visualization. To evaluate the sensor's performance, two algorithms for estimating the HR from PPG signals are implemented in a microcontroller: a modified version of the mountaineer's algorithm and a sliding discrete Fourier transform. The goal of these algorithms is to detect meaningful peaks corresponding to each heartbeat by using signal processing techniques to remove noise and motion artifacts. The developed design is validated through experiments conducted in a simulated driving environment in our lab, during which drivers wore the sensor necklace. These experiments demonstrate the reliability of the wearable sensor necklace in capturing dynamic changes in HR levels associated with driving-induced stress. The algorithms integrated into the sensor are optimized for low computational cost and effectively remove motion artifacts that occur when users move their heads. [ABSTRACT FROM AUTHOR]

Depression is a significant risk factor for other serious health conditions, such as heart failure, dementia, and diabetes. In this study, a quantitative method was developed to detect depressive states in individuals using electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Data were obtained from 59 people affiliated with the high-specialized medical center of Bajio T1, which consists of medical professionals, administrative personnel, and service workers. Data were analyzed using the Beck Depression Inventory (BDI-II) to discern potential false positives. The statistical analyses performed elucidated distinctive features with variable behavior in response to diverse physical stimuli, which were adeptly processed through a machine learning classification framework. The method achieved an accuracy rate of up to 92% in the identification of depressive states, substantiating the potential of biophysical data in increasing the diagnostic process of depression. The results suggest that this method is innovative and has significant potential. With additional refinements, this approach could be utilized as a screening tool in psychiatry, incorporated into everyday devices for preventive diagnostics, and potentially lead to alarm systems for individuals with suicidal thoughts. [ABSTRACT FROM AUTHOR]