Capsule network assisted electrocardiogram classification model for smart healthcare

Jin, Ying; Qi, Huamei; Wu, Jia Rong

doi:10.1016/j.bbe.2022.03.006

Cited by 27 publications

(14 citation statements)

References 28 publications

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“…Jiao et al [ 22 ] introduced a capsule network-assisted electrocardiogram (ECG) classification model for smart healthcare systems. The defined model is mostly used for identifying the type of cardiovascular disease.…”

Section: Related Workmentioning

confidence: 99%

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Zhou

2022

BioMed Research International

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The blockage of blood in the vessels results in heart attacks and cardiac arrests which are referred to as myocardial infarction. Early detection of such infarction is feasible through percutaneous coronary intervention (PCI) based on electrocardiogram (ECG) monitoring. The variations in blood flow and clot are precisely observed through periodic ECG monitoring and previous correlations. This article introduces a concentrated value assessment model (CVAM) for determining PCI levels in treating myocardial infarction. The ECG observations from the previous observation sessions are accumulated and organized for validating the infarction rate. This requires the accompanying concentrated data like a heartbeat, blood pressure, and flow rate observed in different sessions. Based on the session observation and normal data correlation, the PCI level is recommended for the patient. In this analysis process, the value shift due to blocks and high and low blood pressure is accounted for through the deep learning paradigm. This paradigm correlates the above factors with the ECG values for precisely determining PCI from the last known concentration. The learning paradigm is trained based on session and normal observation data through different intervals. This model is validated using the metrics precision, analysis rate, diagnosis recommendation, and complexity.

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Section: Related Workmentioning

confidence: 99%

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Zhou

2022

BioMed Research International

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“…Active learning has been widely combined with deep learning models due to its significant reduction in labeling costs [ 16 – 19 ]. Yang et al [ 10 ]combined active learning with a fully convolutional neural network for segmentation tasks on lymph node ultrasound images and finally achieved and trained using only 50% of the labeled samples.…”

Section: Related Workmentioning

confidence: 99%

“…Rather than using the representative strategy alone, a hybrid strategy combining representative and uncertainty strategies is used more often [ 29 – 34 ]. Yang et al [ 16 ] trained a cluster of models by replacing the labeled data, using the output variance of each model to measure the uncertainty, and using the intermediate output layer of the convolutional neural network as the representation of the image. The similarity of the representation was used as a metric of similarity between images.…”

Section: Related Workmentioning

confidence: 99%

Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System

Zhuang

Dai

2022

Computational Intelligence and Neuroscience

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Assessing the extent of cancer spread by histopathological analysis of sentinel axillary lymph nodes is an important part of breast cancer staging. With the maturity and prevalence of deep learning technology, building auxiliary medical systems can help to relieve the burden of pathologists and increase the diagnostic precision and accuracy during this process. However, such histopathological images have complex patterns that are difficult for ordinary people to understand and require professional medical practitioners to annotate. This increases the cost of constructing such medical systems. To reduce the cost of annotating and improve the performance of the model as much as possible, in other words, using as few labeled samples as possible to obtain a greater performance improvement, we propose a deep learning framework with a three-stage query strategy and novel model update strategy. The framework first trains an auto-encoder with all the samples to obtain a global representation in a low-dimensional space. In the query stage, the unlabeled samples are first selected according to uncertainty, and then, coreset-based methods are employed to reduce sample redundancy. Finally, distribution differences between labeled samples and unlabeled samples are evaluated and samples that can quickly eliminate the distribution differences are selected. This method achieves faster iterative efficiency than the uncertainty strategies, representative strategies, or hybrid strategies on the lymph node slice dataset and other commonly used datasets. It reaches the performance of training with all data, but only uses 50% of the labeled. During the model update process, we randomly freeze some weights and only train the task model on new labeled samples with a smaller learning rate. Compared with fine-tuning task model on new samples, large-scale performance degradation is avoided. Compared with the retraining strategy or the replay strategy, it reduces the training cost of updating the task model by 79.87% and 90.07%, respectively.

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“…It is the basis for further quantitative analysis or tissue classification. Segmentation is usually achieved by drawing a region of interest (ROI) within the tumor margin [ 19 , 20 , 21 ]. Compared to manual segmentation, automated methods are generally faster, more objective, and provide more accurate results [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

et al. 2022

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Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.

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Capsule network assisted electrocardiogram classification model for smart healthcare

Cited by 27 publications

References 28 publications

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Application Value of Remote ECG Monitoring in Early Diagnosis of PCI for Acute Myocardial Infarction

Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System

Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement

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