ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Jing, Enbiao; Zhang, Haiyang; Li, Zhigang; Liu, Yazhi; Ji, Zhanlin; Ганчев, Иван

doi:10.1155/2021/6649970

Cited by 67 publications

(25 citation statements)

References 39 publications

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“…A basic sequential deep learning custom network was trained with 6 blocks with each block including layers of 2D convolution, batch normalization, rectified linear unit, max pooling, and dropout layers along with 1 fully connected and softmax layer ( Figure 3 ). Additionally, publicly available residual neural network (RESNET18) architecture 20 , 21 was also trained using the dataset to serve as a comparison to a widely used deep learning model. The MATLAB (The MathWorks, Inc) deep learning toolbox (version 9.11 - R2021b) was used to train the 2 networks.…”

Section: Methodsmentioning

confidence: 99%

An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors

et al. 2023

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Section: Methodsmentioning

confidence: 99%

An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors

et al. 2023

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“…The spectrogram image to be forwarded to the ResNet-based classification [36] process is AF i PS . The resnet method is mainly used to detect the arrhythmia disease.…”

Section: Resnet Modelmentioning

confidence: 99%

Enhanced Arrhythmia Classification System From Ecg Signals via Hybrid Optimization-Based Improved 3dcnn-Resnet

Munawar¹,

Geetha²,

Srinvas³

2023

INDJCSE

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Arrhythmia is characterized by aberrant electrical activity of the heart, which can be identified the changes in the Electrocardiogram (ECG). Automatic ECG detection is needed because it is mainly used for detecting arrhythmias. Although several algorithms are implemented for the automatic classification of cardiac arrhythmias based on the characteristics of the ECG, their stratification rate is very less because of the unreliable features of signal characteristics or limited generalization capability of the classifier and it is still difficult to diagnose the arrhythmia disease automatically. At this work, they propose a new hybrid deep learning technique for the classification of arrhythmia from the ECG signal. Initially, the wanted ECG signal is collected from the standard websites and then it is assigned to the preprocessing technique. The preprocessing techniques includes the artifacts removal and peak detection techniques noise removal for the elimination of the unwanted distortions and the noise present in the signal then the resultant signal is fed to the Short-time Fourier transform (STFT) to achieve the spectrogram signals and then the spectrogram signal. Thus, the resultant spectrogram signal is given to the hybrid deep learning architecture that includes the 3DCNN-ResNet for diagnosing the arrhythmia disease. Here, the parameter optimizations take place using the hybrid Artificial Showering Dolphin Swarm Optimization (ASDSO) to increase the classification performance. It classifies the signal into five prominent classes that is Premature Ventricular Contraction (V), Right Bundle Branch Block (RBBB or R), Normal Sinus Rhythm (N), Left Bundle Branch Block (LBBB or L), and Atrial Premature Beat (A). The success of the proposed model is validated through diverse benchmark datasets with the performance validation like recall, precision, accuracy, f-measure and some negative measures.

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“…ResNet-18 belongs to the ResNet-xx family of networks. The ResNet-18 network consists of 18 deep layers divided into five convolutional layers for extracting deep feature maps, a ReLU layer, one average pooling layer for reducing image dimensions and a fully connected layer for converting feature maps from 2D to 1D and classifying all inputted images represented by feature vectors into their appropriate class [33]. The softMax activation is a function that classifies the dataset into two classes, ASD and TD.…”

Section: Cnn Resnet-18 Modelmentioning

confidence: 99%

Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques

et al. 2022

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Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively.

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ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Cited by 67 publications

References 39 publications

An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors

An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors

Enhanced Arrhythmia Classification System From Ecg Signals via Hybrid Optimization-Based Improved 3dcnn-Resnet

Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques

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