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1.
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
2.
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
3.
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
4.
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
5.
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
6.
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
7.
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
8.
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
9.
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
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 418-421, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018017

RESUMO

The multi-label electrocardiogram (ECG) classification is to automatically predict a set of concurrent cardiac abnormalities in an ECG record, which is significant for clinical diagnosis. Modeling the cardiac abnormality dependencies is the key to improving classification performance. To capture the dependencies, we proposed a multi-label classification method based on the weighted graph attention networks. In the study, a graph taking each class as a node was mapped and the class dependencies were represented by the weights of graph edges. A novel weights generation method was proposed by combining the self-attentional weights and the prior learned co-occurrence knowledge of classes. The algorithm was evaluated on the dataset of the Hefei Hi-tech Cup ECG Intelligent Competition for 34 kinds of ECG abnormalities classification. And the micro-f 1 and the macro-f 1 of cross validation respectively were 91.45% and 44.48%. The experiment results show that the proposed method can model class dependencies and improve classification performance.


Assuntos
Arritmias Cardíacas , Eletrocardiografia , Algoritmos , Atenção , Humanos , Projetos de Pesquisa
11.
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
12.
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
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2011-2014, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018398

RESUMO

Image registration represents one of the fundamental techniques in medical imaging and image-guided interventions. In this paper, we present a Convolutional Neural Network (CNN) framework for deformable transesophageal US/CT image registration, for the cardiac arrhythmias, and guidance therapy purposes. The framework consists of a CNN, a spatial transformer, and a resampler. The CNN expects concatenated pairs of moving and fixed images as its input, and estimates as output the parameters for the spatial transformer, which generates the displacement vector field that allows the resampler to wrap the moving image into the fixed image. In our method, we train the model to maximize standard image matching objective functions that are based on the image intensities. The network can be applied to perform non-rigid registration of a pair of CT/US images directly in one pass, avoiding so the time consuming computation of the classical iterative method.


Assuntos
Arritmias Cardíacas , Redes Neurais de Computação , Arritmias Cardíacas/diagnóstico por imagem , Humanos , Tomografia Computadorizada por Raios X
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2434-2437, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018498

RESUMO

Dialysis causes blood flow defects in the heart that may augment electrophysiological heterogeneity in the form of increased number of ischemic zones in the human left ventricle. We computationally tested whether a larger number of ischemic zones aggravate arrhythmia using a 2D electrophysiological model of the human ventricle.A human ventricle cardiomyocyte model capable of simulating ischemic action potentials was adapted in this study. The cell model was incorporated into a spatial 2D model consisting of known number of ischemic zones. Inter-cellular gap junction coupling within ischemic zones was reduced to simulate slow conduction. Arrhythmia severity was assessed by inducing a re-entry, and quantifying the ensuing breakup and tissue pacing rates.Ischemia elevated the isolated cardiomyocyte's resting potential and reduced its action potential duration. In the absence of ischemic zones, the propensity in the 2D model to induce multiple re-entrant waves was low. The inclusion of ischemic zones provided the substrate for initiation of re-entrant waves leading to fibrillation. Dominant frequency, which measured the highest rate of pacing in the tissue, increased drastically with the inclusion of multiple ischemic zones. Re-entrant wave tip maximum numbers increased from 1 tip (no ischemic zone) to 34 tips when a large number (20) of ischemic zones were included. Computational limiting factors of our platform were identified using software profiling.Clinical significance. Dialysis may promote deleterious arrhythmias by increasing tissue level action potential dispersion.


Assuntos
Arritmias Cardíacas , Diálise Renal , Arritmias Cardíacas/etiologia , Eletrofisiologia Cardíaca , Simulação por Computador , Humanos , Isquemia
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2504-2507, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018515

RESUMO

A potential treatment option for chronic and severe motility disorders such as gastroparesis is the implantation of a Gastric Electrical Stimulator (GES), which is designed to modulate the bio-electric slow waves. However, the effectiveness of current GESs remains uncertain since they do not work in a closed-loop by sensing, processing, and modulating the dysrhythmic patterns. This work presents the design of a GES model working in closed-loop with the network of the Interstitial Cells of Cajal (ICC). A pre-existing two-dimensional ICC network is enhanced by proposing an extracellular potential generation model, which can precisely capture the timing behaviour of slow wave propagation pattern of the simulated ICC network. The GES senses the extracellular potential, detects bradygastric patterns and finally modulates the activity to ensure normal conduction. The GES is designed to be practical for ease of validation and implementation.


Assuntos
Gastroparesia , Marca-Passo Artificial , Arritmias Cardíacas , Eletricidade , Gastroparesia/terapia , Humanos , Masculino , Próteses e Implantes
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2585-2588, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018535

RESUMO

Increase in transmural dispersion of repolarisation along with a diminished QT interval have been known to aid in the development of arrhythmia during KCNQ1-linked short QT syndrome type 2 (SQTS2). However, the percentage by which action potential duration (APD) shortens in the different cell types that make up the ventricular wall are not fully understood. In this study, the percentage of APD shortening of M-cells was varied to determine the conditions under which re-entry occurs during SQTS2. A 2D transmural section of the heart with anisotropic properties is considered. Slight modifications to the TP06 equations are used to simulate the electrophysiology of the endocardial (endo), midmyocardial (M) and epicardial (epi) cells. A discrete network of 250×100 cells are interconnected using gap junction conductances and from this, a pseudo ECG is generated. On pacing the tissue with premature beats in the midst of normal pacing pulses and on including SQTS, it is observed that re-entry is sustained for a longer duration when the APD shortening in M-cells is more compared to the epi or endo cells while the percentage reduction in APD of M-cells is about 5% to 7% lesser than that in epi and endo cells. Further, when the percentage reduction in APD of M-cells is similar to epi or endo cells, no re-entry is generated. This analysis highlights the key role of percentage reduction in APD of M-cells compared to epi and endo cells in maintaining the re-entrant waves.


Assuntos
Arritmias Cardíacas , Sistema de Condução Cardíaco , Potenciais de Ação , Coração , Humanos
17.
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
18.
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
19.
BMC Public Health ; 20(1): 1524, 2020 Oct 08.
Artigo em Inglês | MEDLINE | ID: mdl-33032561

RESUMO

BACKGROUND: Arrhythmia is a common cardiovascular event that is associated with increased cardiovascular health risks. Previous studies that have explored the association between air pollution and arrhythmia have obtained inconsistent results, and the association between the two in China is unclear. METHODS: We collected daily data on air pollutants and meteorological factors from 1st January 2014 to 31st December 2016, along with daily outpatient visits for arrhythmia in Hangzhou, China. We used a quasi-Poisson regression along with a distributed lag nonlinear model to study the association between air pollution and arrhythmia morbidity. RESULTS: The results of the single-pollutant model showed that each increase of 10 µg/m3 of Fine particulate matter (PM2.5), Coarse particulate matter (PM10), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), and Ozone (O3) resulted in increases of 0.6% (- 0.9, 2.2%), 0.7% (- 0.4, 1.7%), 11.9% (4.5, 19.9%), 6.7% (3.6, 9.9%), and - 0.9% (- 2.9, 1.2%), respectively, in outpatient visits for arrhythmia; each increase of 1 mg/m3 increase of carbon monoxide (CO) resulted in increase of 11.3% (- 5.9, 31.6%) in arrhythmia. The short-term effects of air pollution on arrhythmia lasted 3 days, and the most harmful effects were observed on the same day that the pollution occurred. Results of the subgroup analyses showed that SO2 and NO2 affected both men and women, but differences between the sexes were not statistically significant. The effect of SO2 on the middle-aged population was statistically significant. The effect of NO2 was significant in both the young and middle-aged population, and no significant difference was found between them. Significant effects of air pollution on arrhythmia were only detected in the cold season. The results of the two-pollutants model and the single-pollutant model were similar. CONCLUSIONS: SO2 and NO2 may induce arrhythmia, and the harmful effects are primarily observed in the cold season. There is no evidence of PM2.5, PM10, CO and O3 increasing arrhythmia risk. Special attention should be given to sensitive populations during the high-risk period.


Assuntos
Poluição do Ar/efeitos adversos , Assistência Ambulatorial/estatística & dados numéricos , Arritmias Cardíacas/epidemiologia , Arritmias Cardíacas/terapia , Poluentes Atmosféricos/análise , Poluição do Ar/análise , China/epidemiologia , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Estações do Ano
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