Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

Ma, Fengying; Zhang, Jingyao; Liang, Wei; Xue, Jingyu

doi:10.1155/2020/9159158

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…variable, in our case satisfaction [43]. The principle of adjusting weights is to reduce the errors and optimise the classification outcomes of the network model [109].…”

Section: Plos Onementioning

confidence: 99%

Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling

et al. 2021

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Many individuals visit rural telemedicine centres to obtain safe and effective health remedies for their physical and emotional illnesses. This study investigates the antecedents of patients’ satisfaction relating to telemedicine adoption in rural public hospitals settings in Bangladesh through the adaptation of Expectation Disconfirmation Theory extended by Social Cognitive Theory. This research advances a theoretically sustained prediction model forecasting patients’ satisfaction with telemedicine to enable informed decision making. A research model explores four potential antecedents: expectations, performance, disconfirmation, and enjoyment; that significantly contribute to predicting patients’ satisfaction concerning telemedicine adoption in Bangladesh. This model is validated using two-staged structural equation modeling and artificial neural network approaches. The findings demonstrate the determinants of patients’ satisfaction with telemedicine. The presented model will assist medical practitioners, academics, and information systems practitioners to develop high-quality decisions in the future application of telemedicine. Pertinent implications, limitations and future research directions are endorsed securing long-term telemedicine sustainability.

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“…variable, in our case satisfaction [43]. The principle of adjusting weights is to reduce the errors and optimise the classification outcomes of the network model [109].…”

Section: Plos Onementioning

confidence: 99%

Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling

et al. 2021

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“…The one-stage classifier is incapable of classifying the arrhythmia types correctly. However, as mentioned earlier, because of the nature of the data, the way it has been used and the methods that apply to it, a fair comparison between the proposed two-stage classifier and other literature background is difficult [32][33][34][35][36][37][38][39][40].…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

et al. 2021

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Atrial fibrillation (AF) and ventricular arrhythmia (Arr) are among the most common and fatal cardiac arrhythmias in the world. Electrocardiogram (ECG) data, collected as part of the UK Biobank, represents an opportunity for analysis and classification of these two diseases in the UK. The main objective of our study is to investigate a two-stage model for the classification of individuals with AF and Arr in the UK Biobank dataset. The current literature addresses heart arrhythmia classification very extensively. However, the data used by most researchers lack enough instances of these common diseases. Moreover, by proposing the two-stage model and separation of normal and abnormal cases, we have improved the performance of the classifiers in detection of each specific disease. Our approach consists of two stages of classification. In the first stage, features of the ECG input are classified into two main classes: normal and abnormal. At the second stage, the features of the ECG are further categorised as abnormal and further classified into two diseases of AF and Arr. A diverse set of ECG features such as the QRS duration, PR interval and RR interval, as well as covariates such as sex, BMI, age and other factors, are used in the modelling process. For both stages, we use the XGBoost Classifier algorithm. The healthy population present in the data, has been undersampled to tackle the class imbalance present in the data. This technique has been applied and evaluated using an ECG dataset from the UKBioBank ECG taken at rest repository. The main results of our paper are as follows: The classification performance for the proposed approach has been measured using F1 score, Sensitivity (Recall) and Specificity (Precision). The results of the proposed system are 87.22%, 88.55% and 85.95%, for average F1 Score, average sensitivity and average specificity, respectively. Contribution and significance: The performance level indicates that automatic detection of AF and Arr in participants present in the UK Biobank is more precise and efficient if done in a two-stage manner. Automatic detection and classification of AF and Arr individuals this way would mean early diagnosis and prevention of more serious consequences later in their lives.

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“…To estimate the performance of heartbeat classification, the performance of the model is usually evaluated with accuracy, specificity, and sensitivity [26][27][28]. ey are defined as follows:…”

Section: Performance Evaluationmentioning

confidence: 99%

An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model

Zhang

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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Atrial fibrillation (AF) is a common abnormal heart rhythm disease. Therefore, the development of an AF detection system is of great significance to detect critical illnesses. In this paper, we proposed an automatic recognition method named CNN-LSTM to automatically detect the AF heartbeats based on deep learning. The model combines convolutional neural networks (CNN) to extract local correlation features and uses long short-term memory networks (LSTM) to capture the front-to-back dependencies of electrocardiogram (ECG) sequence data. The CNN-LSTM is feeded by processed data to automatically detect AF signals. Our study uses the MIT-BIH Atrial Fibrillation Database to verify the validity of the model. We achieved a high classification accuracy for the heartbeat data of the test set, with an overall classification accuracy rate of 97.21%, sensitivity of 97.34%, and specificity of 97.08%. The experimental results show that our model can robustly detect the onset of AF through ECG signals and achieve stable classification performance, thereby providing a suitable candidate for the automatic classification of AF.

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Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

Cited by 22 publications

References 23 publications

Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling

Forecasting care seekers satisfaction with telemedicine using machine learning and structural equation modeling

A Novel Two-Stage Heart Arrhythmia Ensemble Classifier

An Automatic System for Atrial Fibrillation by Using a CNN-LSTM Model

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