An arrhythmia classification algorithm using C-LSTM in physiological parameters monitoring system under internet of health things environment

Lü, Weijia; Jiang, Jiehui; Ma, Liang; Chen, Hao; Wu, Huiqun; Gong, Ming; Jiang, Xiaojuan; Fan, Mengkang

doi:10.1007/s12652-021-03456-7

Cited by 13 publications

(11 citation statements)

References 27 publications

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“…The former utilizes commonly used feature extraction techniques 31 and three classifiers. The latter includes 1D CNN and LSTM 32 models, which have achieved state-of-the-art performance in several ECG datasets. 33 , 34 , 35 Our method is compared with these alternative models and is found to fail to comprehensively outperform end-to-end models for diagnosing aggressive arrhythmias.…”

Section: Discussionmentioning

confidence: 99%

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

Huang

Liu

et al. 2023

Patterns

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Section: Discussionmentioning

confidence: 99%

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

Huang

Liu

et al. 2023

Patterns

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“…The algorithm developed in this study was able to estimate the ECG signals of an arrhythmia-impacted individual from the IoT wearable device. In literature [20], a new cloud-based arrhythmia identification utilizing the Recurrent Neural Network (RNN) (NC-RNN) approach was developed for conducting the ECG analysis using wearable sensors in a smart city background. The ECG signal, gathered from the wearable sensor, was made to undergo a three-stage diagnostic phase.…”

Section: Related Workmentioning

confidence: 99%

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment

Almasoud,

Mengash,

Eltahir

et al. 2023

Sensors

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In recent years, the rapid progress of Internet of Things (IoT) solutions has offered an immense opportunity for the collection and dissemination of health records in a central data platform. Electrocardiogram (ECG), a fast, easy, and non-invasive method, is generally employed in the evaluation of heart conditions that lead to heart ailments and the identification of heart diseases. The deployment of IoT devices for arrhythmia classification offers many benefits such as remote patient care, continuous monitoring, and early recognition of abnormal heart rhythms. However, it is challenging to diagnose and manually classify arrhythmia as the manual diagnosis of ECG signals is a time-consuming process. Therefore, the current article presents the automated arrhythmia classification using the Farmland Fertility Algorithm with Hybrid Deep Learning (AAC-FFAHDL) approach in the IoT platform. The proposed AAC-FFAHDL system exploits the hyperparameter-tuned DL model for ECG signal analysis, thereby diagnosing arrhythmia. In order to accomplish this, the AAC-FFAHDL technique initially performs data pre-processing to scale the input signals into a uniform format. Further, the AAC-FFAHDL technique uses the HDL approach for detection and classification of arrhythmia. In order to improve the classification and detection performance of the HDL approach, the AAC-FFAHDL technique involves an FFA-based hyperparameter tuning process. The proposed AAC-FFAHDL approach was validated through simulation using the benchmark ECG database. The comparative experimental analysis outcomes confirmed that the AAC-FFAHDL system achieves promising performance compared with other models under different evaluation measures.

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“…Lu et al [17] presented Convolutional Neural Network (CNN) -Long short-term memory network (LSTM) namely C-LSTM based network model to classify the arrhythmia. At first, the ECG signals were encoded and morphological features were extracted by using a deep CNN.…”

Section: A Arrhythmia Classification With Iotmentioning

confidence: 99%

“…The classification of arrhythmia is affected when the classifier is processed under the following conditions: missing of indirect features [17] and overfitting issue [18] [20]. Different categories of features are required to be extracted for improving the classification [19].…”

Section: Problem Identificationmentioning

confidence: 99%

Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment

Nijaguna,

Lal,

Divakarachari

et al. 2023

IEEE Access

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An Electrocardiogram (ECG) is a non-invasive test that is broadly utilized for monitoring and diagnosing the cardiac arrhythmia. An irregularity of the heartbeat is generally defined as arrhythmia, which potentially causes the fatal difficulties that creates an instantaneous life risk. Therefore, the arrhythmia classification is a challenging task because of the overfitting issue caused by high dimensional feature space of ECG signal. In this research, the incorporation of the Internet of Medical Things (IoMT) is developed with artificial intelligence to provide the health monitoring for people who are having arrhythmia. In this work, the time, time-frequency, entropy, nonlinearity features of ECG and deep features of ECG from Convolutional Neural Network (CNN) are extracted to obtain different categories of ECG signal features. The Selective Opposition (SO) strategy based Artificial Rabbits Optimization (SOARO) is proposed for selecting the optimal feature subset from the overall features to avoid the overfitting issue. The chosen features are used to improve the classification done by Auto Encoder (AE). Further, the Shapley additive explanations (SHAP) based model is used to interpret the classified output from AE. The MIT-BIH arrhythmia database is used for evaluating the proposed SOARO-AE. The performance of the proposed SOARO-AE is evaluated by using the accuracy, sensitivity, specificity, recall and F1-Measure. The existing researches such as C-LSTM, DL-LAC-CNN, CNN-DNN, MC-ECG, FC and MEAHA-CNN are used to evaluate the SOARO-AE method. The accuracy of SOARO-AE is 98.89% which is high when compared to the C-LSTM, DL-LAC-CNN, CNN-DNN, FC and MEAHA-CNN.

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An arrhythmia classification algorithm using C-LSTM in physiological parameters monitoring system under internet of health things environment

Cited by 13 publications

References 27 publications

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

A cardiologist-like computer-aided interpretation framework to improve arrhythmia diagnosis from imbalanced training datasets

Automated Arrhythmia Classification Using Farmland Fertility Algorithm with Hybrid Deep Learning Model on Internet of Things Environment

Feature Selection Using Selective Opposition Based Artificial Rabbits Optimization for Arrhythmia Classification on Internet of Medical Things Environment

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