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BACKGROUND: Timely diagnosis and treatment for ST-elevation myocardial infarction (STEMI) requires a coordinated response from multiple providers. Rapid intervention is key to reducing mortality and morbidity. Activation of the cardiac catheterization laboratory may occur through verbal communication and may also involve the secure sharing of electrocardiographic images between frontline health care providers and interventional cardiologists. To improve this response, we developed a quick, easy-to-use, privacy-compliant smartphone app, that is SMART AMI-ACS (Strategic Management of Acute Reperfusion and Therapies in Acute Myocardial Infarction Acute Coronary Syndromes), for real-time verbal communication and sharing of electrocardiographic images among health care providers in Ontario, Canada. The app further provides information about diagnosis, management, and risk calculators for patients presenting with acute coronary syndrome. OBJECTIVE: This study aims to integrate the app into workflow processes to improve communication for STEMI activation, resulting in decreased treatment times, improved patient outcomes, and reduced unnecessary catheterization laboratory activation and transfer. METHODS: Implementation of the app will be guided by the Reach, Effectiveness, Acceptability, Implementation, and Maintenance (RE-AIM) framework to measure impact. The study will use quantitative registry data already being collected through the SMART AMI project (STEMI registry), the use of the SMART AMI app, and quantitative and qualitative survey data from physicians. Survey questions will be based on the Consolidated Framework for Implementation Research. Descriptive quantitative analysis and thematic qualitative analysis of survey results will be conducted. Continuous variables will be described using either mean and SD or median and IQR values at pre- and postintervention periods by the study sites. Categorical variables, such as false activation, will be described as frequencies (percentages). For each outcome, an interrupted time series regression model will be fitted to evaluate the impact of the app. RESULTS: The primary outcomes of this study include the usability, acceptability, and functionality of the app for physicians. This will be measured using electronic surveys to identify barriers and facilitators to app use. Other key outcomes will measure the implementation of the app by reviewing the timing-of-care intervals, false "avoidable" catheterization laboratory activation rates, and uptake and use of the app by physicians. Prospective evaluation will be conducted between April 1, 2022, and March 31, 2023. However, for the timing- and accuracy-of-care outcomes, registry data will be compared from January 1, 2019, to March 31, 2023. Data analysis is expected to be completed in Fall 2024, with the completion of a paper for publication anticipated by the end of 2024. CONCLUSIONS: Smartphone technology is well integrated into clinical practice and widely accessible. The proposed solution being tested is secure and leverages the accessibility of smartphones. Emergency medicine physicians can use this app to quickly, securely, and accurately transmit information ensuring faster and more appropriate decision-making for STEMI activation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05290389; https://clinicaltrials.gov/study/NCT05290389. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/55506.
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Electrocardiografía , Servicios Médicos de Urgencia , Aplicaciones Móviles , Infarto del Miocardio con Elevación del ST , Teléfono Inteligente , Humanos , Electrocardiografía/instrumentación , Electrocardiografía/métodos , Infarto del Miocardio con Elevación del ST/diagnóstico , Infarto del Miocardio con Elevación del ST/terapia , Infarto del Miocardio con Elevación del ST/fisiopatología , Servicios Médicos de Urgencia/métodos , OntarioRESUMEN
The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of cardiac conditions. The advancement of deep learning has led to the emergence of automated multi-lead ECG diagnostic networks, which have become essential in the fields of biomedical engineering and clinical cardiac disease diagnosis. Intelligent ECG diagnosis techniques encompass Recurrent Neural Networks (RNN), Transformers, and Convolutional Neural Networks (CNN). While CNN is capable of extracting local spatial information from images, it lacks the ability to learn global spatial features and temporal memory features. Conversely, RNN relies on time and can retain significant sequential features. However, they are not proficient in extracting lengthy dependencies of sequence data in practical scenarios. The self-attention mechanism in the Transformer model has the capability of global feature extraction, but it does not adequately prioritize local features and cannot extract spatial and channel features. This paper proposes STFAC-ECGNet, a model that incorporates CAMV-RNN block, CBMV-CNN block, and TSEF block to enhance the performance of the model by integrating the strengths of CNN, RNN, and Transformer. The CAMV-RNN block incorporates a coordinated adaptive simplified self-attention module that adaptively carries out global sequence feature retention and enhances spatial-temporal information. The CBMV-CNN block integrates spatial and channel attentional mechanism modules in a skip connection, enabling the fusion of spatial and channel information. The TSEF block implements enhanced multi-scale fusion of image spatial and sequence temporal features. In this study, comprehensive experiments were conducted using the PTB-XL large publicly available ECG dataset and the China Physiological Signal Challenge 2018 (CPSC2018) database. The results indicate that STFAC-ECGNet surpasses other cutting-edge techniques in multiple tasks, showcasing robustness and generalization.
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Arritmias Cardíacas , Electrocardiografía , Redes Neurales de la Computación , Electrocardiografía/métodos , Humanos , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/fisiopatología , Aprendizaje Profundo , Algoritmos , Procesamiento de Señales Asistido por ComputadorRESUMEN
The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.
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Arritmias Cardíacas , Electrocardiografía , Redes Neurales de la Computación , Humanos , Electrocardiografía/métodos , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/fisiopatología , Aprendizaje Profundo , Procesamiento de Señales Asistido por Computador , AlgoritmosRESUMEN
Atrial fibrillation (AF) is a life-threatening heart condition, and its early detection and treatment have garnered significant attention from physicians in recent years. Traditional methods of detecting AF heavily rely on doctor's diagnosis based on electrocardiograms (ECGs), but prolonged analysis of ECG signals is very time-consuming. This paper designs an AF detection model based on the Inception module, constructing multi-branch detection channels to process raw ECG signals, gradient signals, and frequency signals during AF. The model efficiently extracted QRS complex and RR interval features using gradient signals, extracted P-wave and f-wave features using frequency signals, and used raw signals to supplement missing information. The multi-scale convolutional kernels in the Inception module provided various receptive fields and performed comprehensive analysis of the multi-branch results, enabling early AF detection. Compared to current machine learning algorithms that use only RR interval and heart rate variability features, the proposed algorithm additionally employed frequency features, making fuller use of the information within the signals. For deep learning methods using raw and frequency signals, this paper introduced an enhanced method for the QRS complex, allowing the network to extract features more effectively. By using a multi-branch input mode, the model comprehensively considered irregular RR intervals and P-wave and f-wave features in AF. Testing on the MIT-BIH AF database showed that the inter-patient detection accuracy was 96.89%, sensitivity was 97.72%, and specificity was 95.88%. The proposed model demonstrates excellent performance and can achieve automatic AF detection.
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Algoritmos , Fibrilación Atrial , Electrocardiografía , Redes Neurales de la Computación , Procesamiento de Señales Asistido por Computador , Fibrilación Atrial/diagnóstico , Fibrilación Atrial/fisiopatología , Humanos , Electrocardiografía/métodos , Aprendizaje Automático , Frecuencia Cardíaca , Aprendizaje ProfundoRESUMEN
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.
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Algoritmos , Muerte Súbita Cardíaca , Electrocardiografía , Redes Neurales de la Computación , Humanos , Electrocardiografía/métodos , Muerte Súbita Cardíaca/prevención & control , Frecuencia Cardíaca , Sensibilidad y Especificidad , Aprendizaje Profundo , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/fisiopatología , Procesamiento de Señales Asistido por ComputadorRESUMEN
Zebrafish and their mutant lines have been extensively used in biomedical investigations, cardiovascular studies, and drug screening. In the current study, the commercial version of the novel system, Zebra II, is presented. The protocol demonstrates electrocardiogram (ECG) acquisition and analysis from multiple zebrafish within controllable working environments. The device is composed of an external and independent perfusion system, a 4-point electrode, temperature sensors, and an embedded electronic system. In previous studies, the device prototype underwent validation against the established iWORX system through several tests, demonstrating similar data quality and ECG response to drug interventions. Following this, the study delved into examining the impact of anesthetic drugs and temperature fluctuations on zebrafish ECG, necessitating instant data evaluation. Thanks to the apparatus's capacity for consistent delivery of anesthetics and drugs, it was possible to extend ECG data collection up to 1 h, markedly longer than the 5 min duration supported by current systems. This paper introduces a pioneering, cloud-based, automated analysis utilizing data from four zebrafish, offering an efficient method for conducting combination experiments and significantly reducing time and effort. The system proved effective in capturing and analyzing ECG, especially in detecting drug-induced arrhythmias in wild-type zebrafish. Additionally, the capability to gather data across multiple channels facilitated the execution of randomized controlled trials with zebrafish models. The developed ECG system overcomes existing limitations, showing the potential to greatly expedite drug discovery and cardiovascular research involving zebrafish.
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Electrocardiografía , Pez Cebra , Pez Cebra/fisiología , Animales , Electrocardiografía/métodos , Electrocardiografía/instrumentaciónRESUMEN
The dorsal root ganglia (DRG), housing primary sensory neurons, transmit somatosensory and visceral afferent inputs to the dorsal horn of the spinal cord. They play a pivotal role in both physiological and pathological states, including neuropathic and visceral pain. In vivo calcium imaging of DRG enables real-time observation of calcium transients in single units or neuron ensembles. Accumulating evidence indicates that DRG neuronal activities induced by somatic stimulation significantly affect autonomic and visceral functions. While lumbar DRG calcium imaging has been extensively studied, thoracic segment DRG calcium imaging has been less explored due to surgical exposure and stereotaxic fixation challenges. Here, we utilized in vivo calcium imaging at the thoracic1 dorsal root ganglion (T1-DRG) to investigate changes in neuronal activity resulting from somatic stimulations of the forelimb. This approach is crucial for understanding the somato-cardiac reflex triggered by peripheral nerve stimulations (PENS), such as acupuncture. Notably, synchronization of cardiac function was observed and measured by electrocardiogram (ECG), with T-DRG neuronal activities, potentially establishing a novel paradigm for somato-visceral reflex in the thoracic segments.
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Calcio , Electrocardiografía , Ganglios Espinales , Animales , Ganglios Espinales/fisiología , Calcio/metabolismo , Calcio/análisis , Electrocardiografía/métodos , Ratones , Nervios Periféricos/fisiología , Miembro Anterior/inervación , Miembro Anterior/fisiologíaRESUMEN
Palpitations are a common symptom described by patients as a feeling of a racing or fluttering heart, a pounding chest, irregular or skipped heartbeats, or a pounding sensation in the neck. They are associated with a low mortality rate; however, recurrent palpitations have been shown to impair quality of life and increase health care use. Common triggers are cardiac disorders, endocrine and metabolic disorders, medication or illicit drug use, or psychosomatic disorders. A detailed history, physical examination, directed laboratory studies, and 12-lead electrocardiography are often sufficient to identify the etiology of palpitations. Additional testing may be indicated to include echocardiography, cardiac stress testing, electrocardiogram monitoring, or electrophysiologic studies to distinguish whether symptoms correlate with cardiac arrhythmia or structural or ischemic heart disease. Management of palpitations is based on the suspected etiology. In most cases of cardiac-induced palpitations, the treatment can include reassurance, education, trigger avoidance, or use of atrioventricular nodal blockers. Tachyarrhythmias may require cardiac ablation. Patients who have palpitations with no arrhythmia causality and no cardiac disease should be reassured; however, screening for psychosomatic disorders should be considered. Wearable smart devices with ambulatory electrocardiogram monitoring technologies are currently available to consumers; these tools have shown diagnostic accuracy for detection of arrhythmias, allowing patients to have greater participation in their health care. Am Fam Physician. 2024; 110(3):259-269.
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Arritmias Cardíacas , Dispositivos Electrónicos Vestibles , Humanos , Arritmias Cardíacas/terapia , Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/etiología , Electrocardiografía Ambulatoria/instrumentación , Electrocardiografía Ambulatoria/métodos , Electrocardiografía/métodos , Electrocardiografía/instrumentaciónRESUMEN
BACKGROUND: Manually derived electrocardiographic (ECG) parameters were not associated with mortality in mechanically ventilated COVID-19 patients in earlier studies, while increased high-sensitivity cardiac troponin-T (hs-cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were. To provide evidence for vectorcardiography (VCG) measures as potential cardiac monitoring tool, we investigated VCG trajectories during critical illness. METHODS: All mechanically ventilated COVID-19 patients were included in the Maastricht Intensive Care Covid Cohort between March 2020 and October 2021. Serum hs-cTnT and NT-proBNP concentrations were measured daily. Conversion of daily 12-lead ECGs to VCGs by a MATLAB-based script provided QRS area, T area, maximal QRS amplitude, and QRS duration. Linear mixed-effect models investigated trajectories in serum and VCG markers over time between non-survivors and survivors, adjusted for confounders. RESULTS: In 322 patients, 5461 hs-cTnT, 5435 NT-proBNP concentrations and 3280 ECGs and VCGs were analyzed. Non-survivors had higher hs-cTnT concentrations at intubation and both hs-cTnT and NT-proBNP significantly increased compared with survivors. In non-survivors, the following VCG parameters decreased more when compared to survivors: QRS area (-0.27 (95% CI) (-0.37 to -0.16, p < .01) µVs per day), T area (-0.39 (-0.62 to -0.16, p < .01) µVs per day), and maximal QRS amplitude (-0.01 (-0.01 to -0.01, p < .01) mV per day). QRS duration did not differ. CONCLUSION: VCG-derived QRS area and T area decreased in non-survivors compared with survivors, suggesting that an increase in myocardial damage and tissue loss play a role in the course of critical illness and may drive mortality. These VCG markers may be used to monitor critically ill patients.
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COVID-19 , Electrocardiografía , Fragmentos de Péptidos , Troponina T , Vectorcardiografía , Humanos , Masculino , Femenino , COVID-19/complicaciones , COVID-19/fisiopatología , Electrocardiografía/métodos , Persona de Mediana Edad , Fragmentos de Péptidos/sangre , Troponina T/sangre , Vectorcardiografía/métodos , Estudios de Cohortes , Anciano , Péptido Natriurético Encefálico/sangre , Respiración Artificial/métodos , Biomarcadores/sangre , Países Bajos , SARS-CoV-2RESUMEN
Tissue hysteresivity is an important marker for determining the onset and progression of respiratory diseases, calculated from forced oscillation lung function test data. This study aims to reduce the number and duration of required measurements by combining multivariate data from various sensing devices. We propose using the Forced Oscillation Technique (FOT) lung function test in both a low-frequency prototype and the commercial RESMON device, combined with continuous monitoring from the Equivital (EQV) LifeMonitor and processed by artificial intelligence (AI) algorithms. While AI and deep learning have been employed in various aspects of respiratory system analysis, such as predicting lung tissue displacement and respiratory failure, the prediction or forecasting of tissue hysteresivity remains largely unexplored in the literature. In this work, the Long Short-Term Memory (LSTM) model is used in two ways: (1) to estimate the hysteresivity coefficient η using heart rate (HR) data collected continuously by the EQV sensor, and (2) to forecast η values by first predicting the heart rate from electrocardiogram (ECG) data. Our methodology involves a rigorous two-hour measurement protocol, with synchronized data collection from the EQV, FOT, and RESMON devices. Our results demonstrate that LSTM networks can accurately estimate the tissue hysteresivity parameter η, achieving an R2 of 0.851 and a mean squared error (MSE) of 0.296 for estimation, and forecast η with an R2 of 0.883 and an MSE of 0.528, while significantly reducing the number of required measurements by a factor of three (i.e., from ten to three) for the patient. We conclude that our novel approach minimizes patient effort by reducing the measurement time and the overall ambulatory time and costs while highlighting the potential of artificial intelligence methods in respiratory monitoring.
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Inteligencia Artificial , Mecánica Respiratoria , Humanos , Mecánica Respiratoria/fisiología , Frecuencia Cardíaca/fisiología , Algoritmos , Pruebas de Función Respiratoria/métodos , Pruebas de Función Respiratoria/instrumentación , Pronóstico , Monitoreo Fisiológico/métodos , Monitoreo Fisiológico/instrumentación , Electrocardiografía/métodosRESUMEN
Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices.
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Algoritmos , Fibrilación Atrial , Colaboración de las Masas , Electrocardiografía , Fibrilación Atrial/diagnóstico , Fibrilación Atrial/fisiopatología , Humanos , Colaboración de las Masas/métodos , Electrocardiografía/métodos , Dispositivos Electrónicos VestiblesRESUMEN
Low-cost, portable devices capable of accurate physiological measurements are attractive tools for coaches, athletes, and practitioners. The purpose of this study was primarily to establish the validity and reliability of Movesense HR+ ECG measurements compared to the criterion three-lead ECG, and secondarily, to test the industry leader Garmin HRM. Twenty-one healthy adults participated in running and cycling incremental test protocols to exhaustion, both with rest before and after. Movesense HR+ demonstrated consistent and accurate R-peak detection, with an overall sensitivity of 99.7% and precision of 99.6% compared to the criterion; Garmin HRM sensitivity and precision were 84.7% and 87.7%, respectively. Bland-Altman analysis compared to the criterion indicated mean differences (SD) in RR' intervals of 0.23 (22.3) ms for Movesense HR+ at rest and 0.38 (18.7) ms during the incremental test. The mean difference for Garmin HRM-Pro at rest was -8.5 (111.5) ms and 27.7 (128.7) ms for the incremental test. The incremental test correlation was very strong (r = 0.98) between Movesense HR+ and criterion, and moderate (r = 0.66) for Garmin HRM-Pro. This study developed a robust peak detection algorithm and data collection protocol for Movesense HR+ and established its validity and reliability for ECG measurement.
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Electrocardiografía , Carrera , Humanos , Masculino , Adulto , Electrocardiografía/métodos , Carrera/fisiología , Femenino , Frecuencia Cardíaca/fisiología , Reproducibilidad de los Resultados , Ciclismo/fisiología , Prueba de Esfuerzo/métodos , Adulto JovenRESUMEN
Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other vital signs. However, a major hurdle lies in the privacy concerns surrounding these datasets, which often contain sensitive patient information. Balancing accurate AFib prediction with robust user privacy remains a critical challenge to address. We suggest Federated Learning , a privacy-preserving machine learning technique, to address this privacy barrier. Our approach makes use of FL by presenting Fed-CL, a advanced method that combines Long Short-Term Memory networks and Convolutional Neural Networks to accurately predict AFib. In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. This combined approach within the proposed system aims to equip healthcare professionals with timely alerts and valuable insights. Ultimately, the goal is to facilitate early detection of AFib risk and enable preventive care for susceptible individuals.
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Fibrilación Atrial , Aprendizaje Profundo , Electrocardiografía , Fibrilación Atrial/diagnóstico , Fibrilación Atrial/fisiopatología , Humanos , Electrocardiografía/métodos , Frecuencia Cardíaca/fisiología , Redes Neurales de la Computación , Aprendizaje Automático , Procesamiento de Señales Asistido por ComputadorRESUMEN
Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancements in diagnosis, prediction, treatment, and outcomes. This article aims to provide a basic understanding of AI enabled ECG technology. Specific conditions and findings will be discussed, followed by reviewing associated terminology and methodology. In the appendix, definitions of AUC versus accuracy are explained. The application of deep learning models enables detecting diseases from normal electrocardiograms at accuracy not previously achieved by technology or human experts. Results with AI enabled ECG are encouraging as they considerably exceeded current screening models for specific conditions (i.e., atrial fibrillation, left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy). This could potentially lead to a revitalization of the utilization of the ECG in the insurance domain. While we are embracing the findings with this rapidly evolving technology, but cautious optimism is still necessary at this point.
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Inteligencia Artificial , Electrocardiografía , Humanos , Electrocardiografía/métodos , Aprendizaje Profundo , Fibrilación Atrial/diagnósticoRESUMEN
AIM: Atrial fibrillation (AF) is a rhythm disorder characterized by very rapid and disorganized atrial-derived electrical activations with uncoordinated atrial contractions. Very short periods of AF-like activity (micro-AF) may be precursors of undetected, silent episodes of atrial fibrillation. Here, we examined the relationship between natriuretic peptide concentrations and echocardiography findings in patients with micro-AF. MATERIAL AND METHODS: The electrocardiograms (ECGs) of patients complaining of palpitations were recorded with a 24hour Holter monitor, and the patients were consecutively included in the study. Micro-AF was defined as sudden, irregular atrial tachycardia lasting less than 30 sec with episodes of ≥5 consecutive supraventricular depolarizations with the absolute absence of p-waves. After a G-power test, patients were consecutively included in the study: 45 patients in the micro-AF group and 45 patients in the control group. Laboratory parameters, ECG and echocardiographic findings of the two groups were compared. RESULTS: N-terminal pro B-type natriuretic peptide (Pro-BNP) and serum troponin T concentrations were higher in the micro-AF group, (375.5±63.6 pg / ml vs. 63.1±56.8 pg / ml, p<0.001; 13±11.4 ng / dl vs. 4.4±2.4 ng / dl, p<0.001 respectively.) Each 1 pg / ml increase in serum Pro-BNP increased the risk of micro-AF by 1.8 %. In the ROC analysis, the cut-off value of Pro-BNP for the diagnosis of micro-AF was 63.4 pg / ml, with a sensitivity of 91.1 % and a specificity of 73.3 %. Atrial electro-mechanical delay durations were significantly higher in the micro-AF group. To predict micro-AF, the inter-annulus plane electromechanical delay time (inter-annulus plane AEMD) had a cut-off value of 18.5 sec, with a sensitivity of 93.3 % and a specificity of 91.1 %. Left intra-annulus plane electro-mechanical delay time (intra-annulus AEMD LEFT) had a cut-off value of 11.5 sec with a 95.6 % sensitivity and 75.6 % specificity. In the ECG evaluation, maximum P wave duration (Pmax) (113±10.2âms vs. 98±10.4âms; p<0.001), minimum P wave duration (Pmin) (73.8±5.5âms vs.70±6.3âms; p<0.001) and P wave dispersion (PWD) (39.1±7.9âms vs.28±7.6âms; p<0.001) were longer in the micro-AF group. CONCLUSIONS: Micro-AF in patients may be predicted by evaluating ECG, echocardiographic, and serum natriuretic peptide data.
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Fibrilación Atrial , Ecocardiografía , Péptido Natriurético Encefálico , Humanos , Fibrilación Atrial/fisiopatología , Fibrilación Atrial/diagnóstico , Femenino , Masculino , Persona de Mediana Edad , Ecocardiografía/métodos , Péptido Natriurético Encefálico/sangre , Fragmentos de Péptidos/sangre , Electrocardiografía Ambulatoria/métodos , Anciano , Biomarcadores/sangre , Troponina T/sangre , Electrocardiografía/métodosRESUMEN
BACKGROUND: Right ventricular systolic dysfunction is associated with poor prognosis and increased mortality rates. Our objective was to investigate ECG changes in patients with this condition, focusing on the right-sided precordial leads. METHODS: In this cross-sectional study, 60 patients with right ventricular dysfunction were included from April 2020 to April 2021. Cardiac structure and function were assessed using 2D transthoracic echocardiography. Standard 12-lead electrocardiograms and right-sided precordial ECGs (V3R-V4R) were obtained and analyzed for QRS complex configuration, ST-segment elevation, and T-wave morphology. RESULTS: In our study, the majority were male (70.0%) with a mean age of 58.76 years. The most common initial diagnoses were pulmonary thromboembolism (43.3%), chronic obstructive pulmonary disease (26.7%), and pulmonary hypertension (25.0%). The predominant ECG finding in the right-sided precordial leads (V3R, V4R) was a deep negative T wave (90.0%). Patients with severe right ventricular systolic dysfunction often exhibited a qR pattern (41.2%), whereas those with nonsevere dysfunction showed rS and QS patterns (55.8%). Approximately 41.0% of severe RV dysfunction cases had ST segment depression in the right-sided precordial leads, and 28.0% of patients displayed signs of right atrial abnormality. CONCLUSION: The study found that qR, rS, and QS patterns were more prevalent in V3R and V4R leads among patients with severe and nonsevere right ventricular systolic dysfunction. The most common ECG feature observed was deep T-wave inversion in these leads. The study recommends using right-sided precordial leads in all patients with RV systolic dysfunction for early detection and risk stratification.
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Electrocardiografía , Disfunción Ventricular Derecha , Humanos , Estudios Transversales , Masculino , Disfunción Ventricular Derecha/fisiopatología , Disfunción Ventricular Derecha/diagnóstico por imagen , Disfunción Ventricular Derecha/diagnóstico , Femenino , Persona de Mediana Edad , Electrocardiografía/métodos , Anciano , Ecocardiografía/métodosRESUMEN
We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal analysis, addressing both waveform delineation and beat type classification tasks. For beat type classification, we integrated two novel schemes into the deep learning model, significantly enhancing its performance. The first scheme is an adaptive beat segmentation method that determines the optimal duration for each heartbeat based on RR-intervals, mitigating segmenting errors from conventional fixed-period segmentation. The second scheme incorporates relative heart rate information of the target beat compared to neighboring beats, improving the model's ability to accurately detect premature atrial contractions (PACs) that are easily confused with normal beats due to similar morphology. Extensive evaluations on the PhysioNet QT Database, MIT-BIH Arrhythmia Database, and real-world wearable device data demonstrated the proposed approach's superior capabilities over existing methods in both tasks. The proposed approach achieved sensitivities of 99.81% for normal beats, 99.08% for premature ventricular contractions, and 97.83% for PACs in beat type classification. For waveform delineation, we achieved F1-scores of 0.9842 for non-waveform, 0.9798 for P-waves, 0.9749 for QRS complexes, and 0.9848 for T-waves. It significantly outperforms existing methods in PAC detection while maintaining high performance across both tasks. The integration of aforementioned two schemes into the deep learning model improved the accuracy of normal sinus rhythms and arrhythmia detection.
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Aprendizaje Profundo , Electrocardiografía , Frecuencia Cardíaca , Procesamiento de Señales Asistido por Computador , Humanos , Electrocardiografía/métodos , Frecuencia Cardíaca/fisiología , Bases de Datos Factuales , Arritmias Cardíacas/fisiopatología , Arritmias Cardíacas/diagnósticoRESUMEN
In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection and classification in the processing and analysis of ECG images in real time by integrating three advanced modules. The feature extraction module in our work with a multi-branch structure during training can capture a wide range of features to ensure efficient inference and rich feature extraction. The attention mechanism module utilized in our proposed network can dynamically focus on the most relevant spatial and channel-wise features to improve detection accuracy and computational efficiency. Then, the extracted features can be refined for efficient spatial feature processing and robust feature fusion. Several sets of experiments have been carried out to test the validity of the proposed Mamba-RAYOLO and these indicate that our method has made significant improvements in the detection and classification of ECG images. The research offers a promising framework for more accurate and efficient medical ECG diagnostics.
Asunto(s)
Algoritmos , Aprendizaje Profundo , Electrocardiografía , Procesamiento de Señales Asistido por Computador , Electrocardiografía/métodos , Humanos , Redes Neurales de la Computación , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
Arrhythmias range from mild nuisances to potentially fatal conditions, detectable through electrocardiograms (ECGs). With advancements in wearable technology, ECGs can now be monitored on-the-go, although these devices often capture noisy data, complicating accurate arrhythmia detection. This study aims to create a new deep learning model that utilizes generative adversarial networks (GANs) for effective noise removal and ResNet for precise arrhythmia classification from wearable ECG data. We developed a deep learning model that cleans ECG measurements from wearable devices and detects arrhythmias using refined data. We pretrained our model using the MIT-BIH Arrhythmia and Noise databases. Least squares GANs were used for noise reduction, maintaining the integrity of the original ECG signal, while a residual network classified the type of arrhythmia. After initial training, we applied transfer learning with actual ECG data. Our noise removal model significantly enhanced data clarity, achieving over 30 dB in a signal-to-noise ratio. The arrhythmia detection model was highly accurate, with an F1-score of 99.10% for noise-free data. The developed model is capable of real-time, accurate arrhythmia detection using wearable ECG devices, allowing for immediate patient notification and facilitating timely medical response.
Asunto(s)
Arritmias Cardíacas , Electrocardiografía , Procesamiento de Señales Asistido por Computador , Relación Señal-Ruido , Dispositivos Electrónicos Vestibles , Arritmias Cardíacas/diagnóstico , Humanos , Electrocardiografía/métodos , Algoritmos , Aprendizaje Profundo , Redes Neurales de la ComputaciónRESUMEN
Heart Rate Variability (HRV) refers to the capability of the heart rhythm to vary at different times, typically reflecting the regulation of the heart by the autonomic nervous system. In recent years, with advancements in Electrocardiogram (ECG) signal processing technology, HRV features reflect various aspects of cardiac activity, such as variability in heart rate, cardiac health status, and responses. We extracted key features of HRV and used them to develop and evaluate an automatic recognition model for cardiac diseases. Consequently, we proposed the HRV Heart Disease Recognition (HHDR) method, employing the Spectral Magnitude Quantification (SMQ) technique for feature extraction. Firstly, the HRV signals are extracted through electrocardiogram signal processing. Then, by analyzing parts of the HRV signal within various frequency ranges, the SMQ method extracts rich features of partial information. Finally, the Random Forest (RF) classification computational method is employed to classify the extracted information, achieving efficient and accurate cardiac disease recognition. Experimental results indicate that this method surpasses current technologies in recognizing cardiac diseases, with an average accuracy rate of 95.1% for normal/diseased classification, and an average accuracy of 84.8% in classifying five different disease categories. Thus, the proposed HHDR method effectively utilizes the local information of HRV signals for efficient and accurate cardiac disease recognition, providing strong support for cardiac disease research in the medical field.