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1.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2606-2609, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018540

RESUMO

Over the last few years, the use of cardiac mapping for effective diagnosis and treatment of arrhythmias has increased significantly. In the clinical environment, electroanatomical mapping (EAM) is performed during the electrophysiological procedures using proprietary systems such as CARTO, EnSite Precision, RHYTHMIA, etc. These systems generate the 3D model of patient-specific atria with the electrical activity (i.e., intracardiac electrograms (iEGMs)) displayed on it, for further identification of the sources of arrhythmia and for guiding cardiac ablation therapy. Recently, several novel techniques were developed to perform iEGMs analysis to more accurately identify the arrhythmogenic sites. However, there is a difficulty in incorporating the results of iEGMs analysis back to EAM systems due to their proprietary constraints. This created a hurdle in the further development of novel techniques to help navigate patient-specific clinical ablation therapy. Thus, we developed an open source software, VIEgram1, that allows researchers to visualize the results of the various iEGMs analysis on a patient-specific 3D atria model. It eliminates the dependency of the academic environment on the proprietary EAM systems, thereby making the process of retrospective mapping extremely convenient and time efficient. Here, we demonstrate the features of VIEgram such as visual inspection of iEGMs, flexibility in implementing custom iEGMs analysis techniques and interpolation schemes, and spatial analysis.


Assuntos
Ablação por Cateter , Técnicas Eletrofisiológicas Cardíacas , Arritmias Cardíacas/diagnóstico , Átrios do Coração , Humanos , Estudos Retrospectivos
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2748-2751, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018575

RESUMO

Neural respiratory drive as measured by the electromyography allows the study of the imbalance between the load on respiratory muscles and its capacity. Surface respiratory electromyography (sEMG) is a non-invasive tool used for indirectly assessment of NRD. It also provides a way to evaluate the level and pattern of respiratory muscle activation. The prevalence of electrocardiographic activity (ECG) in respiratory sEMG signals hinders its proper evaluation. Moreover, the occurrence of abnormal heartbeats or cardiac arrhythmias in respiratory sEMG measures can make even more challenging the NRD estimation. Respiratory sEMG can be evaluated using the fixed sample entropy (fSampEn), a technique which is less affected by cardiac artefacts. The aim of this work was to investigate the performance of the fSampEn, the root mean square (RMS) and the average rectified value (ARV) on respiratory sEMG signals with supraventricular arrhythmias (SVA) for NRD estimation. fSampEn, ARV and RMS parameters increased as the inspiratory load increased during the test. fSampEn was less influenced by ECG with SVAs for the NRD estimation showing a greater response to respiratory sEMG, reflected with a higher percentage increase with increasing load (228 % total increase, compared to 142 % and 135 % for ARV and RMS, respectively).


Assuntos
Eletrocardiografia , Músculos Respiratórios , Arritmias Cardíacas/diagnóstico , Eletromiografia , Entropia , Humanos
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 332-336, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017996

RESUMO

Electrocardiogram (ECG) signal is the most commonly used non-invasive tool in the assessment of cardiovascular diseases. Segmentation of the ECG signal to locate its constitutive waves, in particular the R-peaks, is a key step in ECG processing and analysis. Over the years, several segmentation and QRS complex detection algorithms have been proposed with different features; however, their performance highly depends on applying preprocessing steps which makes them unreliable in realtime data analysis of ambulatory care settings and remote monitoring systems, where the collected data is highly noisy. Moreover, some issues still remain with the current algorithms in regard to the diverse morphological categories for the ECG signal and their high computation cost. In this paper, we introduce a novel graph-based optimal changepoint detection (GCCD) method for reliable detection of Rpeak positions without employing any preprocessing step. The proposed model guarantees to compute the globally optimal changepoint detection solution. It is also generic in nature and can be applied to other time-series biomedical signals. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed method achieves overall sensitivity Sen = 99.76, positive predictivity PPR = 99.68, and detection error rate DER = 0.55 which are comparable to other state-of-the-art approaches.1 2.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Algoritmos , Arritmias Cardíacas/diagnóstico , Bases de Dados Factuais , Humanos
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4470-4474, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018987

RESUMO

We present an enhanced R-peak detection technique that incorporates both waveform shape recognition and threshold sensitivity enhancement. Waveform shape recognition was achieved with signal processing and Gaussian curve parameterization; threshold sensitivity was accomplished with the famous Pan-Tompkins algorithm. We tested all 48 records in MIT-BIH Arrhythmia Database to validate the proposed method. Our method achieved 97.41% sensitivity against a tolerance window of 10% averaged R-R interval, which improves the current state-of-the-art Pan-Tompkins algorithm by 1%. More importantly, we demonstrate that our approach outperforms the Pan-Tompkins' algorithm in 81% of the records in MIT-BIH Arrhythmia Database.Clinical relevance: High sensitivity R-peak detection is substantial in various cardiovascular disease diagnosis.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Algoritmos , Arritmias Cardíacas/diagnóstico , Humanos , Distribuição Normal
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5012-5015, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019112

RESUMO

Accurate assessment of pacemaker function or malfunction is essential to make clinical interpretations on pacemaker therapy and patient symptoms. This article presents an innovative approach for detecting pacemaker pulses at sampling frequency as low as 125Hz. The proposed method is validated in wide range of simulated clinical ECG conditions such as arrhythmia (sinus rhythms, supraventricular rhythms, and AV blocks), pulse amplitudes (~100µV to ~3mV), pulse durations (~100µs to ~2ms), pacemaker modes and types (fixed-rate or on-demand single chamber, dual chamber, and bi-ventricular pacing), and physiological noise (tremor). The proposed algorithm demonstrates clinically acceptable detection accuracies with sensitivity and PPV of 98.1 ± 4.4 % and 100 %, respectively. In conclusion, the approach is well suited for integration in long-term wearable ECG sensor devices operating at a low sample frequency to monitor pacemaker function.Clinical Relevance- The proposed system enables real-time long-term continuous assessment of the proper functioning of implanted pacemaker and progression of treatment for cardiac conditions using battery-powered wearable ECG monitors.


Assuntos
Marca-Passo Artificial , Arritmias Cardíacas/diagnóstico , Estimulação Cardíaca Artificial , Eletrocardiografia , Frequência Cardíaca , Humanos
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 288-291, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017985

RESUMO

Machine learning has become increasingly useful in various medical applications. One such case is the automatic categorization of ECG voltage data. A method of categorization is proposed that works in real time to provide fast and accurate classifications of heart beats. This proposed method uses machine learning principles to allow for results to be determined based on a training dataset. The goal of this project is to develop a method of automatically classifying heartbeats that can be done on a low level and run on portable hardware.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Arritmias Cardíacas/diagnóstico , Doença do Sistema de Condução Cardíaco , Humanos , Redes Neurais de Computação
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 292-295, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017986

RESUMO

Arrhythmia is a serious cardiovascular disease, and early diagnosis of arrhythmia is critical. In this study, we present a waveform-based signal processing (WBSP) method to produce state-of-the-art performance in arrhythmia classification. When performing WBSP, we first filtered ECG signals, searched local minima, and removed baseline wandering. Subsequently, we fit the processed ECG signals with Gaussians and extracted the parameters. Afterwards, we exploited the products of WBSP to accomplish arrhythmia classification with our proposed machine learning-based and deep learning-based classifiers. We utilized MIT-BIH Arrhythmia Database to validate WBSP. Our best classifier achieved 98.8% accuracy. Moreover, it reached 96.3% sensitivity in class V and 98.6% sensitivity in class Q, which both share one of the best among the related works. In addition, our machine learning-based classifier accomplished identifying four waveform components essential for automated arrhythmia classification: the similarity of QRS complex to a Gaussian curve, the sharpness of the QRS complex, the duration of and the area enclosed by P-wave.Clinical relevance- Early diagnosis and automated classification of arrhythmia is clinically essential.


Assuntos
Aprendizado Profundo , Eletrocardiografia , Arritmias Cardíacas/diagnóstico , Humanos , Aprendizado de Máquina , Processamento de Sinais Assistido por Computador
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 296-299, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017987

RESUMO

Recent developments in the field of deep learning has shown a rise in its use for clinical applications such as electrocardiogram (ECG) analysis and cardiac arrhythmia classification. Such systems are essential in the early detection and management of cardiovascular diseases. However, due to privacy concerns and also the lack of resources, there is a gap in the data available to run such powerful and data-intensive models. To address the lack of annotated, high-quality ECG data for heart disease research, ECG data generation from a small set of ECG to obtain huge annotated data is seen as an effective solution. Generative Feature Matching Network (GFMN) was shown to resolve few drawbacks of commonly used generative adversarial networks (GAN). Based on this, we developed a deep learning model to generate ECGs that resembles real ECG by feature matching with the existing data.Clinical relevance- This work addresses the lack of a large quantity of good quality, publicly available annotated ECG data required to build deep learning models for cardiac signal processing research. We can use the model presented in this paper to generate ECG signals of a target rhythm pattern and also subject-specific ECG morphology that could improve their cardiac health monitoring while maintaining privacy.


Assuntos
Arritmias Cardíacas , Cardiopatias , Arritmias Cardíacas/diagnóstico , Doença do Sistema de Condução Cardíaco , Eletrocardiografia , Humanos , Processamento de Sinais Assistido por Computador
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 300-303, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017988

RESUMO

Cardiac arrhythmia is a prevalent and significant cause of morbidity and mortality among cardiac ailments. Early diagnosis is crucial in providing intervention for patients suffering from cardiac arrhythmia. Traditionally, diagnosis is performed by examination of the Electrocardiogram (ECG) by a cardiologist. This method of diagnosis is hampered by the lack of accessibility to expert cardiologists. For quite some time, signal processing methods had been used to automate arrhythmia diagnosis. However, these traditional methods require expert knowledge and are unable to model a wide range of arrhythmia. Recently, Deep Learning methods have provided solutions to performing arrhythmia diagnosis at scale. However, the black-box nature of these models prohibit clinical interpretation of cardiac arrhythmia. There is a dire need to correlate the obtained model outputs to the corresponding segments of the ECG. To this end, two methods are proposed to provide interpretability to the models. The first method is a novel application of Gradient-weighted Class Activation Map (Grad-CAM) for visualizing the saliency of the CNN model. In the second approach, saliency is derived by learning the input deletion mask for the LSTM model. The visualizations are provided on a model whose competence is established by comparisons against baselines. The results of model saliency not only provide insight into the prediction capability of the model but also aligns with the medical literature for the classification of cardiac arrhythmia.Clinical relevance- Adapts interpretability modules for deep learning networks in ECG arrhythmia classfication, allowing for better clinical interpretation.


Assuntos
Algoritmos , Arritmias Cardíacas , Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Humanos , Redes Neurais de Computação , Processamento de Sinais Assistido por Computador
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 304-307, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017989

RESUMO

Electrocardiograph (ECG) is one of the most critical physiological signals for arrhythmia diagnosis in clinical practice. In recent years, various algorithms based on deep learning have been proposed to solve the heartbeat classification problem and achieved saturated accuracy in intrapatient paradigm, but encountered performance degradation in inter-patient paradigm due to the drastic variation of ECG signals among different individuals. In this paper, we propose a novel unsupervised domain adaptation scheme to address this problem. Specifically, we first propose a robust baseline model called Multi-path Atrous Convolutional Network (MACN) to tackle ECG heartbeat classification. Further, we introduce Cluster-aligning loss and Cluster-separating loss to align the distributions of training and test data and increase the discriminability, respectively. The proposed method requires no expert annotations but a short period of unlabelled data in new records. Experimental results on the MIT-BIH database demonstrate that our scheme effectively intensifies the baseline model and achieves competitive performance with other state-of-the-arts.


Assuntos
Eletrocardiografia , Processamento de Sinais Assistido por Computador , Algoritmos , Arritmias Cardíacas/diagnóstico , Frequência Cardíaca , Humanos
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 308-311, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33017990

RESUMO

Lacking sufficient training samples of different heart rhythms is a common bottleneck to obtain arrhythmias classification models with high accuracy using artificial neural networks. To solve this problem, we propose a novel data augmentation method based on short-time Fourier transform (STFT) and generative adversarial network (GAN) to obtain evenly distributed samples in the training dataset. Firstly, the one-dimensional electrocardiogram (ECG) signals with a fixed length of 6 s are subjected to STFT to obtain the coefficient matrices, and then the matrices of different heart rhythm samples are used to train GAN models respectively. The generated matrices are later employed to augment the training dataset of classification models based on four convolutional neural networks (CNNs). The result shows that the performances of the classification networks are all improved after we adopt the data enhancement strategy. The proposed method is helpful in augmentation and classification of biomedical signals, especially in detecting multiple arrhythmias, since adequate training samples are usually inaccessible in these studies.


Assuntos
Arritmias Cardíacas , Redes Neurais de Computação , Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Análise de Fourier , Humanos
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 353-356, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018001

RESUMO

Bundle branch block (BBB) is one of the most common cardiac disorder, and can be detected by electro-cardiogram (ECG) signal in clinical practice. Conventional methods adopted some kinds of hand-craft features, whose discriminative power is relatively low. On the other hand, these methods were based on the supervised learning, which required the high cost heartbeat annotation in the training. In this paper, a novel end-to-end deep network was proposed to classify three types of heartbeat: right BBB (RBBB), left BBB (LBBB) and others with a multiple instance learning based training strategy. We trained the proposed method on the China Physiological Signal Challenge 2018 database (CPSC) and tested on the MIT-BIH Arrhythmia database (AR). The proposed method achieved an accuracy of 78.58%, and sensitivity of 84.78% (LBBB), 51.23% (others) and 99.72% (RBBB), better than the baseline methods. Experimental results show that our method would be a good choice for the BBB classification on the ECG dataset with record-level labels instead of heartbeat annotations.


Assuntos
Bloqueio de Ramo , Eletrocardiografia , Arritmias Cardíacas/diagnóstico , Bloqueio de Ramo/diagnóstico , China , Frequência Cardíaca , Humanos
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 357-360, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018002

RESUMO

Automatic electrocardiogram (ECG) analysis for pacemaker patients is crucial for monitoring cardiac conditions and the effectiveness of cardiac resynchronization treatment. However, under the condition of energy-saving remote monitoring, the low-sampling-rate issue of an ECG device can lead to the miss detection of pacemaker spikes as well as incorrect analysis on paced rhythm and non-paced arrhythmias. To solve the issue, this paper proposed a novel system that applies the compressive sampling (CS) framework to sub-Nyquist acquire and reconstruct ECG, and then uses multi-dimensional feature-based deep learning to identify paced rhythm and non-paced arrhythmias. Simulation testing results on ECG databases and comparison with existing approaches demonstrate its effectiveness and outstanding performance for pacemaker ECG analysis.


Assuntos
Compressão de Dados , Marca-Passo Artificial , Arritmias Cardíacas/diagnóstico , Aprendizado Profundo , Eletrocardiografia , Humanos
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 580-583, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018055

RESUMO

Recently, classification from compressed physiological signals in compressed sensing has been successfully applied to cardiovascular disease monitoring. However, in real-time wearable electrocardiogram (ECG) monitoring, it is very difficult to directly obtain the heartbeats information from compressed ECG signals. Thus arrhythmia classification from compressed ECG signals has to be handled in fixed-length segments instead of individual heartbeats. An inevitable issue is that a fixed-length ECG segment may contain multiple different types of arrhythmia. As a result, it is not appropriate to represent the multi-type real arrhythmia with a single label. In this paper, we first introduce multiple labels into fixed-length compressed ECG segments to challenge the arrhythmia classification issue. Then, we propose a deep learning model, which can directly classify multiple different types of arrhythmia from fixed-length compressed ECG segments with the advantages of low time cost for data processing and relatively high classification accuracy at a high compression ratio. Experimental results on the MIT-BIH arrhythmia database show that the exact match rate of our proposed method has reached 96.03% at CR(Compression Ratio)=70%, 94.99% at CR=80% and 93.19% at CR=90%.


Assuntos
Compressão de Dados , Dispositivos Eletrônicos Vestíveis , Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Frequência Cardíaca , Humanos
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 621-624, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018064

RESUMO

The use of fetal heart rate (FHR) recordings for assessing fetal wellbeing is an integral component of obstetric care. Recently, non-invasive fetal electrocardiography (NI-FECG) has demonstrated utility for accurately diagnosing fetal arrhythmias via clinician interpretation. In this work, we introduce the use of data-driven entropy profiling to automatically detect fetal arrhythmias in short length FHR recordings obtained via NI-FECG. Using an open access dataset of 11 normal and 11 arrhythmic fetuses, our method (TotalSampEn) achieves excellent classification performance (AUC = 0.98) for detecting fetal arrhythmias in a short time window (i.e. under 10 minutes). We demonstrate that our method outperforms SampEn (AUC = 0.72) and FuzzyEn (AUC = 0.74) based estimates, proving its effectiveness for this task. The rapid detection provided by our approach may enable efficient triage of concerning FHR recordings for clinician review.


Assuntos
Monitorização Fetal , Frequência Cardíaca Fetal , Arritmias Cardíacas/diagnóstico , Entropia , Feminino , Humanos , Gravidez , Processamento de Sinais Assistido por Computador
16.
Ann Saudi Med ; 40(5): 365-372, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32954790

RESUMO

Evidence of cardiovascular complications associated with the COVID-19 global pandemic continues to evolve. These include direct and indirect myocardial injury with subsequent acute myocardial ischemia, and cardiac arrhythmia. Some results from a limited number of trials of antiviral medications, along with chloroquine/hydroxychloroquine and azithromycin, have been beneficial. However, these pharmacotherapies may cause drug-induced QT prolongation leading to ventricular arrhythmias and sudden cardiac death. Mitigation of the potential risk in these susceptible patients may prove exceptionally challenging. The Saudi Heart Rhythm Society established a task force to perform a review of this subject based on has recently published reports, and studies and recommendations from major medical organizations. The objective of this review is to identify high-risk patients, and to set clear guidelines for management of patients receiving these pharmacotherapies.


Assuntos
Arritmias Cardíacas/induzido quimicamente , Infecções por Coronavirus/tratamento farmacológico , Pneumonia Viral/tratamento farmacológico , Antagonistas Adrenérgicos beta/uso terapêutico , Comitês Consultivos , Antivirais/efeitos adversos , Arritmias Cardíacas/diagnóstico , Azitromicina/efeitos adversos , Betacoronavirus , Cloroquina/efeitos adversos , Inibidores do Citocromo P-450 CYP2D6/efeitos adversos , Combinação de Medicamentos , Interações Medicamentosas , Monitoramento de Medicamentos , Eletrocardiografia , Humanos , Hidroxicloroquina/efeitos adversos , Síndrome do QT Longo/induzido quimicamente , Síndrome do QT Longo/diagnóstico , Lopinavir/efeitos adversos , Pandemias , Medição de Risco , Ritonavir/efeitos adversos , Arábia Saudita , Torsades de Pointes/induzido quimicamente , Torsades de Pointes/diagnóstico
17.
Pediatr Clin North Am ; 67(5): 801-810, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32888682

RESUMO

Syncope and palpitations are common complaints for patients presenting to their primary care provider. They represent symptoms that most often have a benign etiology but rarely can be the first warning sign of a serious condition, such as arrhythmias, structural heart disease, or noncardiac disease. The history, physical examination, and noninvasive testing can, in most cases, distinguish benign from pathologic causes. This article introduces syncope and palpitations, with emphasis on the differential diagnoses, initial presentation, diagnostic strategy, and various management strategies.


Assuntos
Arritmias Cardíacas/diagnóstico , Eletrocardiografia , Sistema de Condução Cardíaco/fisiopatologia , Frequência Cardíaca/fisiologia , Síncope/diagnóstico , Arritmias Cardíacas/fisiopatologia , Criança , Diagnóstico Diferencial , Humanos , Síncope/fisiopatologia
18.
JACC Clin Electrophysiol ; 6(8): 1053-1066, 2020 08.
Artigo em Inglês | MEDLINE | ID: mdl-32819525

RESUMO

Coronavirus disease 2019 (COVID-19) has presented substantial challenges to patient care and impacted health care delivery, including cardiac electrophysiology practice throughout the globe. Based upon the undetermined course and regional variability of the pandemic, there is uncertainty as to how and when to resume and deliver electrophysiology services for arrhythmia patients. This joint document from representatives of the Heart Rhythm Society, American Heart Association, and American College of Cardiology seeks to provide guidance for clinicians and institutions reestablishing safe electrophysiological care. To achieve this aim, we address regional and local COVID-19 disease status, the role of viral screening and serologic testing, return-to-work considerations for exposed or infected health care workers, risk stratification and management strategies based on COVID-19 disease burden, institutional preparedness for resumption of elective procedures, patient preparation and communication, prioritization of procedures, and development of outpatient and periprocedural care pathways.


Assuntos
Arritmias Cardíacas/diagnóstico , Arritmias Cardíacas/terapia , Cardiologia , Infecções por Coronavirus/epidemiologia , Assistência à Saúde , Técnicas Eletrofisiológicas Cardíacas , Pneumonia Viral/epidemiologia , Assistência Ambulatorial , American Heart Association , Betacoronavirus , Técnicas de Laboratório Clínico , Infecções por Coronavirus/diagnóstico , Infecções por Coronavirus/prevenção & controle , Tomada de Decisão Compartilhada , Pessoal de Saúde , Humanos , Programas de Rastreamento , Política Organizacional , Pandemias/prevenção & controle , Seleção de Pacientes , Equipamento de Proteção Individual/provisão & distribução , Pneumonia Viral/diagnóstico , Pneumonia Viral/prevenção & controle , Retorno ao Trabalho , Medição de Risco , Telemedicina , Estados Unidos/epidemiologia
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