Electrocardiogram Readings Research Articles

Comparison between the ECG Outcomes of Metoprolol and Bisoprolol.

Beta-blockers are essential agents in the management of cardiovascular diseases, such as heart failure, acute myocardial infarction (MI), and cardiac arrhythmias. However, there are diurnal variations in the cardioprotective effects of the subgroups as a result of their different pharmacokinetic, pharmacodynamic, and pharmacogenetic profiles. We aimed to compare metoprolol and bisoprolol in terms of electrocardiogram (ECG) outcomes. A retrospective cross-sectional study was conducted at Prince Sultan Cardiac Center. The trial included 404 patients who met the inclusion criteria (204 in the metoprolol arm and 200 in the bisoprolol arm). Using case record forms that had already been created, information, such as patient demographics, medical histories, and treatment histories, was taken from their medical files. The most recent ECG records were also gathered. The ethical approval for this study was obtained from Qassim ethical committee (approval number: 45-44-902). There was no significant difference found between the patients in both arms in terms of baseline characteristics, age, or sex. In this retrospective cross-sectional study, we have compared the effects of metoprolol and bisoprolol beta blockers on ECG changes. The findings have indicated no difference between metoprolol and bisoprolol groups in terms of all ECG readings, particularly PR/ms, QTC-ms, and ventricular rate. Further studies are required to confirm these findings.

Accuracy of the Apple Watch in Detecting Atrial Fibrillation Among Patients Undergoing 24-Hour Holter Monitoring: A Prospective, Pragmatic Study

BACKGROUND: As smartwatches with atrial fibrillation detection features gain popularity, it is important to assess the accuracy of these devices to guide decision-making. OBJECTIVES: Our study aimed to assess the sensitivity and specificity of the irregular rhythm notification and the electrocardiogram (ECG)–based detection features of a commonly used smart wearable device (Apple Watch) in detecting atrial fibrillation. METHODS: This was a prospective, pragmatic study conducted in Perpetual Succour Hospital–Cebu Heart Institute from August 2023 to January 2024. To assess the irregular rhythm notification feature, participants were asked to wear an Apple Watch alongside a 24-hour Holter monitor to verify notifications. For the ECG-based detection feature, participants had to tap the crown of the Apple Watch for 30 seconds to get a single-lead ECG similar to a lead I ECG tracing. They were instructed to get manual ECGs hourly, or more often while awake. Irregular rhythm notifications and ECG readings were then compared with that of the 24-hour Holter monitor. Sensitivity and specificity were then computed. RESULTS: A total of 140 participants consented to join after full study disclosure. The irregular rhythm notification feature of the Apple Watch exhibited a low sensitivity of 21.4% but achieved a high specificity of 100% in detecting atrial fibrillation. Meanwhile, the ECG-based detection feature, analyzed from 1295 manually taken ECGs with interpretable sinus rhythm or atrial fibrillation, demonstrated a high level of agreement with the Holter monitor, with a sensitivity of 100% and a specificity of 99.1%. CONCLUSION: The low sensitivity of the irregular rhythm notification feature of the Apple Watch in detecting atrial fibrillation cautions against relying on it as a primary screening tool. However, the high concordance of manually taken Apple Watch ECGs positions the device as a robust tool for detecting atrial fibrillation through manual ECG detection. KEYWORDS: Apple Watch, atrial fibrillation, smartwatches

An explainable AI for trustworthy detection of atrial fibrillation on reduced lead ECGs in mobile applications

Abstract Introduction Artificial intelligence (AI), particularly deep learning (DL), has demonstrated high performance in various diagnostic problems, including the detection of paroxysmal atrial fibrillation (AF) from electrocardiograms (ECGs). In mobile applications, these approaches can help to detect AF at an early stage and thus prevent possible secondary diseases. However, due to their black-box nature, DL approaches lack explainability and interpretability. Furthermore, 12-lead ECGs are the clinical standard, but mobile devices usually only provide 1–3 leads, which are similar but not identical to Einthoven leads and differ in morphology. Purpose We apply a recently introduced explainable ECG analysis architecture (xECGArch) to single-lead ECGs to verify its performance in mobile applications. xECGArch can learn rhythmic and morphological characteristics using two parallel convolutional neural networks of different dimensions (Fig. 1). When combined with methods from explainable AI, the classification can be traced back to rhythmic and morphological characteristics. Methods We used a subset of 9 854 ECGs (n(AF) = n(non-AF) = 4 927) from 4 public databases (Chapman-Shaoxing, CPSC2018, Georgia, and PTB-XL). Noise was removed using a cascade of high-pass filtering at 0.3 Hz and discrete wavelet transform. For each lead, we trained xECGArch on single-lead ECGs of 10 s length to classify AF vs. non-AF using 90% of the ECGs with 5-fold cross-validation and tested it on the remaining randomly selected ECGs (n(AF) = 507, n(non-AF) = 479). Consistent data splitting was ensured for all leads. Model explanations were generated using deep Taylor decomposition. Results Both models achieved minimum 91.1% accuracy for each ECG lead (Fig. 1). On lead I, the morphology model outperformed the rhythm model with 92.8% vs. 91.5% accuracy, while the rhythm model slightly outperformed the morphology model on leads II and III with 94.2%–95.0% accuracy vs. 91.1%–94.0%. Both models achieved maximum accuracy of 95.0% (rhythm) and 94.0% (morphology) on lead II. By combining both models, xECGArch reached 93.7%–95.1% accuracy. The model explanations confirm that the rhythm model primarily considers QRS complexes as relevant, while the morphology model focuses on fibrillatory waves (Fig. 1). Conclusions Both rhythm and morphology models achieved reliable results across all leads, competing with the state-of-the-art for automatic AF detection from single-lead ECGs. The combination of both models in xECGArch further increased the accuracy. xECGArch is based on the medical reading of ECGs by considering rhythm and morphology characteristics and is therefore interpretable by design. The model explanations are consistent with the diagnostic criteria for AF (Fibrillatory waves and absolute arrhythmia) across leads. With high accuracy on different single-lead ECGs and improved interpretability, xECGArch provides a trustworthy method for ECG analysis in mobile applications.

Deep learning assists early-detection of hypertension-mediated heart change on ECG signals.

Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated heart change, it often fails to detect early subtle structural changes. Electrocardiogram(ECG) represents electrical activity of heart muscle, affected by the changes in heart's structure. It is crucial to explore whether ECG can capture slight signals of hypertension-mediated heart change. However, reading ECG records is complex and some signals are too subtle to be captured by cardiologist's visual inspection. In this study, we designed a deep learning model to predict hypertension on ECG signals and then to identify hypertension-associated ECG segments. From The First Affiliated Hospital of Xiamen University, we collected 210,120 10-s 12-lead ECGs using the FX-8322 manufactured by FUKUDA and 812 ECGs using the RAGE-12 manufactured by NALONG. We proposed a deep learning framework, including MML-Net, a multi-branch, multi-scale LSTM neural network to evaluate the potential of ECG signals to detect hypertension, and ECG-XAI, an ECG-oriented wave-alignment AI explanation pipeline to identify hypertension-associated ECG segments. MML-Net achieved an 82% recall and an 87% precision in the testing, and an 80% recall and an 82% precision in the independent testing. In contrast, experienced clinical cardiologists typically attain recall rates ranging from 30 to 50% by visual inspection. The experiments demonstrate that ECG signals are sensitive to slight changes in heart structure caused by hypertension. ECG-XAI detects that R-wave and P-wave are the hypertension-associated ECG segments. The proposed framework has the potential to facilitate early diagnosis of heart change.

A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.

Comparison of Standard and Prone-Position Electrocardiograms in COVID-19 Patients With Pulmonary Complications: Correlations and Implications.

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. This study aimed to elucidate the effects of prone positioning on ECG parameters and explore its association with the severity of COVID-19. 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. 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). 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.

Incidence and risk factors associated with atrioventricular block in the general population: the atherosclerosis risk in communities study and Cardiovascular Health Study

ObjectivesTo identify risk factors correlated with atrioventricular block (AVB) in the general population.MethodsParticipants in the Atherosclerosis Risk in Communities study (ARIC) and the Cardiovascular Health study (CHS) were enrolled. The presence of AVB was confirmed at an electrocardiogram (ECG) reading center using Minnesota ECG Classification. Cox proportional hazards models were performed to investigate potential risk factors of AVB, after adjustment for age, sex, race and traditional cardiovascular risk factors.ResultsDuring the 17 years of follow-up, a total of 731 high-degree AVB cases were identified. Age and sex-standardized rate of AVB was 2.79 and 2.35 per 1000 person-years in the white and the black population, respectively. With the increase of the geriatric population, the incidence of high-degree AVB will increase from 378,816 in 2020 to 535,076 in 2060, and most increment would occur among the elderly. Older age, male sex, the white race, overweight, comorbidities, declined forced vital capacity (FVC), elevated inflammation biomarkers, left bundle branch block and bifascicular block were independently associated with the incidence of high-degree AVB.ConclusionTo conclude, older age, male sex, white population, overweight, combined diabetes or chronic kidney disease, impaired FVC, elevated inflammation biomarkers, left bundle branch block and bifascicular block were independent predictors for high-degree AVB. The next 40 years would witness a dramatic increase in the incidence of high-degree AVB.

The effect of transcutaneous auricular vagus nerve stimulation on cardiovascular function in subarachnoid hemorrhage patients: a safety study.

Subarachnoid hemorrhage (SAH) is characterized by intense central inflammation, leading to substantial post-hemorrhagic complications such as vasospasm and delayed cerebral ischemia. Given the anti-inflammatory effect of transcutaneous auricular vagus nerve stimulation (taVNS) and its ability to promote brain plasticity, taVNS has emerged as a promising therapeutic option for SAH patients. However, the effects of taVNS on cardiovascular dynamics in critically ill patients, like those with SAH, have not yet been investigated. Given the association between cardiac complications and elevated risk of poor clinical outcomes after SAH, it is essential to characterize the cardiovascular effects of taVNS to ensure this approach is safe in this fragile population. Therefore, we assessed the impact of both acute taVNS and repetitive taVNS on cardiovascular function in this study. In this randomized clinical trial, 24 SAH patients were assigned to either a taVNS treatment or a Sham treatment group. During their stay in the intensive care unit, we monitored patient electrocardiogram (ECG) readings and vital signs. We compared long-term changes in heart rate, heart rate variability, QT interval, and blood pressure between the two groups. Additionally, we assessed the effects of acute taVNS by comparing cardiovascular metrics before, during, and after the intervention. We also explored acute cardiovascular biomarkers in patients exhibiting clinical improvement. We found that repetitive taVNS did not significantly alter heart rate, QT interval, blood pressure, or intracranial pressure. However, taVNS increased overall heart rate variability and parasympathetic activity compared to the sham treatment. The increase in parasympathetic activity was most pronounced from 2-4 days after initial treatment (Cohen's d = 0.50). Acutely, taVNS increased heart rate, blood pressure, and peripheral perfusion index without affecting the corrected QT interval, intracranial pressure, or heart rate variability. The acute post-treatment elevation in heart rate was more pronounced in patients who experienced a decrease of more than one point in their Modified Rankin Score at the time of discharge. Our study found that taVNS treatment did not induce adverse cardiovascular effects, such as bradycardia or QT prolongation, supporting its development as a safe immunomodulatory treatment approach for SAH patients. The observed acute increase in heart rate after taVNS treatment may serve as a biomarker for SAH patients who could derive greater benefit from this treatment. NCT04557618.

Pre-trained Bi-LSTM model for automated classification of ventricular arrhythmias using 1-D and 2-D ECG

Number of cardiac conditions have been associated with abnormal heartbeat (arrhythmia) such as ventricular fibrillation (Vfib), ventricular flutter (Vfl), and ventricular tachycardia (Vta). This is a difficult and essential job for timely clinical assessment and identification of these potentially life-threatening heart arrhythmias. With the aid of a one-dimensional electrocardiogram (ECG) signal and its associated two-dimensional image, the suggested method provides a strategy for the detection of time-frequency interpretation (Vfib, Vfl, and Vta). A four-stage cascaded Savitzky-Golay (SG) filter is used after a 2-stage median filter to preprocess the ECG signal. This technique employs z-score normalisation after brief (2 sec) ECG readings. The classification of these ECG segments (1-D) and associated time-frequency representation pictures (2-D) was explored separately using a bi-directional long short-term memory-based network. Eight distinct categorization scenarios were examined, and then an average accuracy of 99.67% for 1-D ECG and 99.87% for 2-D ECG signal was attained.

Factors Leading to Late Presentation of ST-Segment Elevation Myocardial Infarction for Thrombolytic Therapy

Background: ST-segment Elevation Myocardial Infarction (STEMI) is a major cardiac emergency caused by total coronary artery obstruction, requiring immediate intervention to save myocardium and avoid sequelae. Streptokinase-assisted thrombolysis is still a practical and affordable option even as primary PCI develops. A number of reasons contribute to late presentation for thrombolysis, which highlights the need of removing obstacles to timely treatment in STEMI cases. Objectives: The main purpose of this study was to identify factors that lead to late presentation of patients with STEMI.Materials and Methods: This cross-sectional study was conducted for seven months between 15 June 2021 and 15 January 2022 in the Cardiology Department of Ayub Teaching Hospital, Abbottabad. A total of 142 patients who presented at least after 12 hours of their initial symptom of chest pain were included in the study using Non-probability purposive sampling technique. Results: According to study results, the following reasons may contribute to a patient's delayed presentation after a STEMI: lack of symptom awareness (30.3%), absence of an initial ECG (19.7%), patient reluctance (23.3%), remote residence (9.8%), and misreading of the ECG (16.9%). For prompt STEMI management and better results, removing these obstacles is essential.Conclusion: This study highlights the main factors that could lead to late hospital presentation of patients with STEMI for thrombolytic therapy. For prompt action and better results, it is imperative to address problems such misinterpreting ECG readings, delayed ECG assessment, patient resistance, and geographic barriers.Keywords: Electrocardiogram (ECG), Non-ST elevation Myocardial Infarction (NSTEMI), ST-segment elevation Myocardial Infarction (STEMI), Streptokinase

Electrophysiological and biochemical effect of zinc oxide nanoparticles on heart functions of male Wistar rats

Zinc oxide nanoparticles (ZnO NPs) are one of the most abundantly used nanomaterials in cosmetics and topical products, and nowadays, they are explored in drug delivery and tissue engineering. Some recent data evidenced that they are responsible for cardiotoxic effects and systemic toxicity. The present study aimed to investigate the toxic effect of ZnO NPs (39 nm) on the heart of Wistar rats and to perform a dose–response relationship using three different dose levels (25, 50, 100 mg/kg bw) of ZnO NPs on the electrocardiogram (ECG) readings, the levels of biochemical function parameters of heart, and the oxidative stress and antioxidant biomarkers. Furthermore, zinc concentration level and histopathological examination of heart tissues were determined. ZnO NPs showed a dose-dependent effect, as the 100 mg/kg bw ZnO NPs treated group showed the most significant changes in ECGs parameters: R–R distance, P–R interval, R and T amplitudes, and increased levels of heart enzymes Creatine Kinase- MB (CK-MB) and Lactate dehydrogenase (LDH). On the other hand, elevated zinc concentration levels, oxidative stress biomarkers MDA and NO, and decreased GSH levels were found also in a dose-dependent manner, the results were supported by impairment in the histopathological structure of heart tissues. While the dose of 100 mg/kg bw of ZnO bulk group showed no significant effects on heart function. The present study concluded that ZnO NPs could induce cardiac dysfunctions and pathological lesions mainly in the high dose.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.

Assessment of current pediatric electrocardiogram (ECG) interpretation practices

BackgroundDespite improvements in digital electrocardiograms (ECGs), current standard of care requires physician confirmation. Mismatched expectations between ordering providers and ECG readers, often pediatric cardiologists and electrophysiologists (EPs), are common, especially since there are no standardized practices for pediatric ECG reading. ObjectivesThe aim of this study was to understand current practices in pediatric ECG reading. MethodsAn electronic survey was sent to members of the Pediatric and Congenital Electrophysiology Society (PACES). Participation was optional; results were recorded from 12/19/22–1/9/23. Only complete and non-duplicate entries were included. ResultsA total of 127 responses were received, 93 were analyzed. Most responses were from centers in North America (n = 65, 70 %), including the US (n = 58, 62 %), Canada (n = 6, 6 %), and Mexico (n = 1, 1 %). The remaining were from Europe (n = 18, 19 %), Asia (n = 7, 8 %), Australia (n = 2, 2 %), and South America (n = 1, 1 %). Most (n = 46, 49 %) were from small centers (0–25 ECGs read per day), 27 respondents (29 %) were from medium centers (26–50 ECGs read per day), 20 respondents (22 %) were from large centers (>50 ECGs read per day). The majority (n = 65, 70 %) reported >3 readers/day for inpatient and emergency department ECGs. 49 % (n = 46) of centers read ECGs >2 times/day on weekdays with more variable practice on weekends. For critical/time sensitive findings, most centers (n = 90, 97 %) used verbal communication +/− the EMR. There was consensus (≥50 % agreement) that the following findings are critical/time sensitive: QTc >500 ms, T-wave alternans, narrow complex tachycardia, wide complex tachycardia, pre-excited atrial fibrillation, focal ischemic changes, second degree heart block type II, complete heart block, and pacemaker malfunction. ConclusionReading practices are variable. Critical/time sensitive findings are most often communicated verbally, however, there is no agreed upon standard. There was consensus in critical/time-sensitive findings. Improved understanding of common practices and resource allocation may lead to increased consistency in pediatric ECG reading.

Comparison of Safety and Diagnostic Efficacy of Iohexol 240 mgI/mL, Iopamidol 250 mgI/mL, and Iodixanol 270 mgI/mL in Cerebral Angiography: A Prospective, Multicenter Study.

This multicenter prospective study aimed to evaluate the quality and diagnostic effectiveness of cerebral angiography images obtained using low-concentration iodinated contrast agents (iohexol 240 mgI/mL, iopamidol 250 mgI/mL, and iodixanol 270 mgI/mL) and to assess the safety thereof. The study addresses the need for safer contrast agent alternatives without compromising the diagnostic quality of identifying cerebrovascular disease. Conducted in 5 medical centers in South Korea, we enrolled patients aged 19 years or older who were referred for diagnostic cerebral angiography under non-emergency conditions, excluding those with specific health conditions and sensitivities. The study design included a prospective, observational approach with a 1-way analysis of variance (ANOVA) for sample size calculation, aiming for a total sample of 231 participants for adequate power. Image quality was evaluated using a 4-level scale by 2 independent, blinded radiologists, and adverse reactions were monitored both immediately and up to 7 days post-procedure. Statistical analysis involved 1-way ANOVA and Kruskal-Wallis tests to assess the image quality and safety profiles of the contrast agents. Among 266 patients screened, 243 were included in the final analysis. The evaluation revealed no statistically significant differences in image quality among the 3 types of low-concentration contrast agents. Adverse events were observed in 28.8% of patients, with 27.2% experiencing acute reactions, primarily mild reactions, and 3.3% experiencing delayed reactions. The overall safety profile showed no significant changes in vital signs or electrocardiogram readings before and after contrast agent injection. Using low-concentration iodinated contrast agents for cerebral angiography provides image quality comparable to that of conventional high-concentration agents, with no significant increase in adverse events, suggesting a safer alternative for patients.

Prevalence and patterns of post-COVID-19 symptoms in recovered patients of Delhi, India: a population-based study.

Post-coronavirus disease 2019 (COVID-19) symptoms were widely reported. However, data on post-COVID-19 conditions following infection with the Omicron variant remained scarce. This prospective study was conducted to understand the prevalence, patterns, and duration of symptoms in patients who had recovered from COVID-19. A prospective study was conducted across 11 districts of Delhi, India, among individuals who had recovered from COVID-19. Study participants were enrolled, and then returned for post-recovery follow-up at 3 months and 6 months interval. The mean age of study participants was 42.07 years, with a standard deviation of 14.89 years. The majority of the participants (79.7%) reported experiencing post-COVID-19 symptoms. The most common symptoms included joint pain (36.0%), persistent dry cough (35.7%), anxiety (28.4%), and shortness of breath (27.1%). Other symptoms were persistent fatigue (21.6%), persistent headache (20.0%), forgetfulness (19.7%), and limb weakness (18.6%). The longest duration of symptom was observed to be anxiety (138.75±54.14 days), followed by fatigue (137.57±48.33 days), shortness of breath (131.89±60.21 days), and joint pain/swelling (131.59±58.76 days). At the first follow-up visit, 2.2% of participants presented with abnormal electrocardiogram readings, but no abnormalities were noticed during the second follow-up. Additionally, 4.06% of participants exhibited abnormal chest X-ray findings at the first followup, which decreased to 2.16% by the second visit. The most frequently reported post-COVID-19 symptoms were joint pain, dry cough, anxiety and shortness of breath. These clinical symptoms persisted for up to 6 months, with evidence of multi-system involvement. Consequently, findings highlighted the need for long-term follow-up during the post-COVID-19 period.

A Narrative Review of In‐Textile Sensors in Human Health Applications

AbstractSensors have become more versatile and sophisticated in recent years to fulfill the increasing demands for human health applications. Physiological information such as electrocardiogram, pulse rate, and respiration are essential indications of personal health, often collected as vitals, which are typically collected from medical‐grade electrocardiogram (ECG) machines. In‐textile sensors are a fast‐growing sub‐category of wearable sensors embedded in smart textiles to acquire physiological information and movement index and provide harmful chemical warnings without compromising the comfortable nature of clothing. Recent literature has shown that integrating new materials has greatly improved the stability, specificity, and selectivity of in‐textile sensors. For example, polyvinylidene fluoride nanofiber produced a highly stretchable sensor to measure ECG readings during movement without losing data quality. This review discusses a group of nanomaterial‐based in‐textile sensors for consumer use in the home, workplace, and healthcare environments. This review will focus on exploring and analyzing the latest developments in these nanomaterial‐based e‐textiles due to their ability to be more easily integrated for daily use and their great potential for medical applications. Future work will be necessary to incorporate recycled materials, improve the method of powering these sensors, and ultimately refine the designs to be appropriate for more sustainable use.

Assessment of cardiac adverse events following COVID-19 vaccination by speckle tracking echocardiography

Cardiac discomfort has been reported periodically in COVID-19-vaccinated individuals. Thus, this study aimed to evaluate the role of myocardial strains in the early assessment of the clinical presentations after COVID-19 vaccination. Totally, 121 subjects who received at least one dose of vaccine within 6 weeks underwent laboratory tests, electrocardiogram (ECG), and echocardiogram. Two-dimensional speckle tracking echocardiography (2D-STE) was implemented to analyze changes in the left ventricular myocardium. After vaccination, 66 individuals (55.4 ± 17.4 years) developed cardiac discomforts, such as chest tightness, palpitations, dyspnea, and chest pain. The ECG readings exhibited both premature ventricular contractions and premature atrial contractions (n = 24, 36.4%), while none of the individuals in the control group manifested signs of cardiac arrhythmia. All had normal serum levels of creatine phosphokinase, creatine kinase myocardial band, troponin, N-terminal pro b-type natriuretic peptide, platelets, and D-dimer. Left ventricular ejection fraction in the symptomatic group (71.41% ± 7.12%) and the control group (72.18% ± 5.11%) (p = 0.492) were normal. Use of 2D-STE presented global longitudinal strain (GLS) and global circumferential strain (GCS) was reduced in the symptomatic group (17.86% ± 3.22% and 18.37% ± 5.22%) compared to the control group (19.54% ± 2.18% and 20.73% ± 4.09%) (p = 0.001 and p = 0.028). COVID-19 vaccine-related cardiac adverse effects can be assessed early by 2D-STE. The prognostic implications of GLS and GCS enable the evaluation of subtle changes in myocardial function after vaccination.

Cardiomyocyte-specific deletion of adhesion G protein-coupled receptor F5 (ADGRF5) increases susceptibility to stress and arrhythmia

G-Protein Coupled Receptors (GPCR) represent the largest family of cell surface receptors in the human body and serve as targets for more than 30% of all approved drugs. Adhesion GPCRs (AGPCR) are an understudied GPCR family containing numerous extracellular domains that interact with extracellular proteins, including many known mediators of cardiac remodeling responses to pathologic stress. However, their relative expression in the heart and impact on cardiac function normally or during disease has been largely unstudied. To address this, we evaluated RNASeq-attained AGPCR expression from left ventricular (LV) samples of adult mice, of which Adgrf5 was one of the most highly expressed. Notably, decreased ADGRF5 expression is observed across numerous failing human LV tissue GEO datasets, and C57BL6/J mice subjected to transaortic constriction (TAC) surgery to induce pressure overload-induced HF also displayed reduced LV Adgrf5 expression by 12 weeks post-TAC. Analysis of published single cell (sc)-RNASeq datasets confirmed Adgrf5 expression in cardiomyocytes (CM), which also decreased following TAC in mice and in CM from failing human hearts. To investigate the impact of CM-specific ADGRF5 on cardiac function and remodeling normally or during disease progression, we crossed floxed ADGRF5 (ADGRF5f/f) and αMHC-Cre mice to generate constitutive, CM-specific ADGRF5 knockout mice (F5cmKO). These mice display normal cardiac structure and function at 12 weeks of age versus αMHC-Cre mice, as assessed via echocardiography, gravimetrics and immunohistochemistry. However, F5cmKO mice develop cardiac dysfunction, maladaptive remodeling, and increased mortality over time, even in the absence of pathologic insult, suggesting a homeostatic function of CM-specific ADGRF5. Following TAC surgery, F5cmKO mice display similar outcomes including enhanced cardiac dysfunction with accelerated remodeling and increased mortality. To attain mechanistic insight into these outcomes, we performed RNA-sequencing on LV tissue of 12-week-old F5cmKO mice, identifying and subsequently validating Scn1b as significantly upregulated in F5cmKO CM. Scn1b encodes for the β subunits of voltage-gated sodium channels (Scnβ1/β1B), which regulate gating and kinetics of Nav1.5 as well as adhesive CM-CM interactions, disruption of which could lead to arrhythmias. Indeed, conscious electrocardiogram (ECG) readings via telemetry indicated F5cmKO mice had a high burden of premature ventricular contractions (PVC), including ventricular tachycardia associated with cardiac enlargement. Using a combination of luciferase and fluorescence resonance energy transfer (FRET) reporters and gene expression analyses in neonatal rat ventricular myocytes (NRVM), we confirmed that ADGRF5 controls Scn1b expression in a Gαq/11 protein-dependent manner. In all, we have discovered that loss of CM-specific ADGRF5 leads to increased Scn1b expression with enhanced susceptibility to stress, arrythmias and sudden death, thus may provide a novel GPCR target to promote the maintenance of cardiac rhythm. AHA Predoctoral Fellowship 834906 to J.M.E. P01 HL147841 to D.T. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Investigating the Effectiveness of Continuous Quality Improvement Methods in Preventing the Phenomenon of Pseudo-Poor Electrocardiogram Procedure by Nurses in an Emergency Care Unit

Objective: To investigate the effect of applying continuous quality improvement methods on preventing the phenomenon of pseudo-poor electrocardiogram (ECG) procedures by nurses in the emergency care unit. Methods: The study was conducted in Shangluo Central Hospital Shaanxi from August 2020 to August 2021, and 200 emergency patients who received ECG during this period were selected for the comparative study, and grouped into two groups: the control group was given routine management, while the experimental group was managed by continuous quality improvement. The two groups were compared in terms of pseudo-differences in ECG procedures. Results: The patients in the experimental group had a higher rate of qualified ECG readings and procedures, and a lower rate of pseudo-errors and nursing disputes, all of which were significantly different from the control group (P < 0.05), which is significant, and the experimental group had a better effect. Conclusion: When ECG examination is carried out in the emergency care unit, the application of continuous quality improvement management methods is conducive to improving the quality of ECG examination, reducing the incidence of pseudo-discrepancy, and providing a reliable reference for clinical diagnosis and treatment.

Arrhythmia classification for non-experts using infinite impulse response (IIR)-filter-based machine learning and deep learning models of the electrocardiogram.

Arrhythmias are a leading cause of cardiovascular morbidity and mortality. Portable electrocardiogram (ECG) monitors have been used for decades to monitor patients with arrhythmias. These monitors provide real-time data on cardiac activity to identify irregular heartbeats. However, rhythm monitoring and wave detection, especially in the 12-lead ECG, make it difficult to interpret the ECG analysis by correlating it with the condition of the patient. Moreover, even experienced practitioners find ECG analysis challenging. All of this is due to the noise in ECG readings and the frequencies at which the noise occurs. The primary objective of this research is to remove noise and extract features from ECG signals using the proposed infinite impulse response (IIR) filter to improve ECG quality, which can be better understood by non-experts. For this purpose, this study used ECG signal data from the Massachusetts Institute of Technology Beth Israel Hospital (MIT-BIH) database. This allows the acquired data to be easily evaluated using machine learning (ML) and deep learning (DL) models and classified as rhythms. To achieve accurate results, we applied hyperparameter (HP)-tuning for ML classifiers and fine-tuning (FT) for DL models. This study also examined the categorization of arrhythmias using different filters and the changes in accuracy. As a result, when all models were evaluated, DenseNet-121 without FT achieved 99% accuracy, while FT showed better results with 99.97% accuracy.