Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering

Chen, Hanjie; Das, Saptarshi; Morgan, John M.; Maharatna, Koushik

doi:10.1016/j.compbiomed.2021.105180

Cited by 16 publications

(6 citation statements)

References 40 publications

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“…We divide the occlusion area into four sub-areas and then gradually restore it. At present, the attention mechanism is widely used in various fields [ 9 , 10 , 11 , 12 ]. Attention mechanisms can better extract the important content of the image and improve the feature expression ability of the network.…”

Section: Introductionmentioning

confidence: 99%

Multi-Step Structure Image Inpainting Model with Attention Mechanism

Cai

Yang

2023

Sensors

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The proliferation of deep learning has propelled image inpainting to an important research field. Although the current image inpainting model has made remarkable achievements, the two-stage image inpainting method is easy to produce structural errors in the rough stage because of insufficient treatment of the rough inpainting stage. To address this problem, we propose a multi-step structured image inpainting model combining attention mechanisms. Different from the previous two-stage inpainting model, we divide the damaged area into four sub-areas, calculate the priority of each area according to the priority, specify the inpainting order, and complete the rough inpainting stage several times. The stability of the model is enhanced by the multi-step method. The structural attention mechanism strengthens the expression of structural features and improves the quality of structure and contour reconstruction. Experimental evaluation of benchmark data sets shows that our method effectively reduces structural errors and improves the effect of image inpainting.

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Section: Introductionmentioning

confidence: 99%

Multi-Step Structure Image Inpainting Model with Attention Mechanism

Cai

Yang

2023

Sensors

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“…Carrillo‐Alarcón arrhythmia beat and arrhythmic episode respectively is an irregular heartbeat that may be single or a group of heartbeats. The arrhythmia is caused due to a slow or fast heartbeat that leads to an irregular rhythm that leads to sudden mortality and stroke (Chen et al, 2022; Rai & Chatterjee, 2022). For a healthy heart, Arrhythmia may take place with minimum consequences based on respiratory behaviour.…”

Section: Introductionmentioning

confidence: 99%

“…The arrhythmia is caused due to a slow or fast heartbeat that leads to an irregular rhythm that leads to sudden mortality and stroke (Chen et al, 2022;Rai & Chatterjee, 2022). For a healthy heart, Arrhythmia may take place with minimum consequences based on respiratory behaviour.…”

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confidence: 99%

IoT based arrhythmia classification using the enhanced hunt optimization‐based deep learning

Kumar

Dutt

et al. 2023

Expert Systems

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The advancement of information technology, the Internet of Things (IoT), and several miniaturize equipment's enhances the healthcare field that provides real‐time patient monitoring, which helps to provide medication anywhere and anytime. However, accurate detection is still a challenging task for which an effective classification model is introduced in this research. The proposed method is the Enhanced Hunt optimization based Deep convolutional neural network (Enhanced Hunt based‐Deep CNN), in which the Enhanced Hunt optimization algorithm (EHOA) is developed by fusing the hunting habit of the predator and the herding characteristics of herding dog for enhancing the global optimal convergence. Here, the ECG signal from the individuals is collected using the IoT network and stored in the Hospital server, which is accessed by the doctor when requested, the classification is performed using the Enhanced Hunt based‐Deep CNN and the performance revealed the effectiveness with the accuracy, sensitivity, and specificity of 95.33%, 94.92%, and 97.57%.

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“…Moreover, the reconstructed phase portraits reveal various morphologies associated with normal and pathological ECGs. Thus, several studies employed the PSR with m = 2 to detect QRS complex [ 28 ], recognize ventricular extrasystoles [ 29 ], and classify the type of ventricular arrhythmia [ 30 ] because the 2D reconstruction displays a concise phase space trajectory and lends itself more readily to feature extraction. In addition, PSR transforms the temporal patterns of the ECG signal (P, QRS, and T waves) to specific spatial loops in phase space without the need of characteristic wave detections, extending its application to individual identification [ 12 , 13 , 22 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

Chan

Chang

Hsu

et al. 2023

Sensors

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Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively.

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Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering

Cited by 16 publications

References 40 publications

Multi-Step Structure Image Inpainting Model with Attention Mechanism

Multi-Step Structure Image Inpainting Model with Attention Mechanism

IoT based arrhythmia classification using the enhanced hunt optimization‐based deep learning

Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

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