Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Eltrass, Ahmed S.; Tayel, Mazhar B.; Ammar, Abeer I.

doi:10.1007/s00521-022-06889-z

Cited by 29 publications

(10 citation statements)

References 97 publications

(126 reference statements)

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“…The morphological-based arrhythmias detection and classification method were designed [34] and morphological filtering to produce a fresh classification method. A novel hybrid strategy [44] employing deep neural networks combined with linear and nonlinear features collected from ECG and heart rate variability (HRV) was developed for the classification of ECG in multi-classes. It was the first method to combine linear and nonlinear properties using ECG and HRV data.…”

Section: Automatic Diseases Detectionmentioning

confidence: 99%

“…• The existing automatic ECG-based heart disease detection techniques [34][35][36][37][38][39][40][41][42][43][44][45][46][47] directly perform the automatic feature extraction and classification. As ECG signals are vulnerable to different types of noises, a robust and effective ECG denoising technique is required before applying automatic feature extraction and classification.…”

Section: Research Gap Analysismentioning

confidence: 99%

“…• The adaptive and accurate ECG wave segmentation is still a research problem considering the robustness and accuracy of beats extraction [26][27][28][29][30][31][32][33]. The beats segmentation in the recent automatic deep learning-based methods were poorly designed [34][35][36][37][38][39][40][41][42][43][44][45][46][47].…”

Section: Research Gap Analysismentioning

confidence: 99%

“…• The lack of appropriate mechanisms to handle the 2D high-dimensional deep learning features in existing methods [34][35][36][37][38][39][40][41][42][43][44][45] leads to classification unreliability and higher training time, i.e., high computational complexity.…”

Section: Research Gap Analysismentioning

confidence: 99%

See 3 more Smart Citations

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

Goyal

2022

Preprint

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Since the last decade, the Electrocardiogram (ECG) tool has received medical experts and researchers' attention for accurate and fast diagnosis of cardiovascular diseases (CVD). The automatic detection and classification of 1D ECG-based heart disease have become more realistic and efficient solutions using the deep learning technique such as Convolutional Neural Network (CNN). As CNN is designed mainly for 2D or 3D applications therefore, designing CNN to process 1D ECG becomes challenging. We proposed a novel framework for automatic CVD detection and classification from the raw ECG signals using 1D-CNN deep learning technique. The framework consists of pre-processing, automatic feature extraction, feature optimization, and classification. In pre-processing, raw ECG signals are filtered to remove the baseline drift and power line interference using median and notch filters respectively. The pre-processed ECG signals are then used to extract the QRS and ST waves using the dynamic thresholding in the wavelet transfer domain. The fusion of QRS and ST waves have fed to automatic 1D-CNN that consists of layers i.e,1D convolutional layer, ReLU layer, and max-pooling layers. The 1D-CNN is proposed in this paper to extract features automatically with little computing complexity. The high-dimensional raw CNN features are optimized by applying a feature selection and scaling approach. For classification, different soft computing techniques such as Long- Short Term Memory (LSTM), Support Vector Machine (SVM), Naïve Bays (NB), Artificial Neural Network (ANN), and k-nearest neighbor (KNN) are applied. The experimental performances of the proposed model have been investigated on a publicly available research dataset and outperformed recent CNN-based techniques.

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Section: Automatic Diseases Detectionmentioning

confidence: 99%

Section: Research Gap Analysismentioning

confidence: 99%

Section: Research Gap Analysismentioning

confidence: 99%

Section: Research Gap Analysismentioning

confidence: 99%

See 2 more Smart Citations

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

Goyal

2022

Preprint

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“…In the context of the Internet, there is hot development based on deep learning and neural networks [1][2][3][4][5], making it penetrate into various fields [6][7][8][9][10]. Because it has many excellent features, such as a huge database as a support, it can learn through layer-by-layer and feature abstraction, and it can imitate the distributed representation of human knowledge data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Nomadic Civilization in Northern Grassland in Plastic Arts Based on Deep Learning

Zhang

2022

Journal of Sensors

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In this era of rapid development, the exchanges between countries are increasing rapidly, which leads to the integration of multiculturalism and its impact on the local culture, making it diluted. Taking the plastic art features of the nomadic civilization in the northern grasslands as an example, the plastic art features of the nomadic civilization are very rich, including color, texture, shape, and local characteristics; the use of traditional methods will lead to poor feature effects, and it is difficult to obtain high-level information. There will also be problems with image recognition. With the hot development of deep learning, for these problems, its advantages and characteristics are introduced and applied to the characteristics of plastic arts, and a deep and shallow network is constructed as its input and feature recognition, which solves the problem of image feature recognition. At the same time, the convolution idea is introduced to enlarge its features, which is more conducive to feature recognition, extraction, and analysis. For the neural network model of deep learning, the traditional optimization algorithm is changed to the Adam optimization algorithm, which solves the problem of decreasing accuracy, improves the accuracy of prediction, and makes it more stable. From the final experimental results, it is not difficult to find that the feature algorithm greatly improves the accuracy rate under different noises, and the time consumption of the algorithm operation is also reduced. The traditional algorithm of the deep learning neural network model is changed to the Adam optimization algorithm, which also improves the prediction accuracy and makes it more stable. In the future development, the unsaturated function can be used as the activation function to optimize or change the model feature algorithm to make the model easier to build and have better training effects.

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A novel proposed CNN–SVM architecture for ECG scalograms classification

Özaltın

Yeniay

2022

Soft Comput

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Nowadays, the number of sudden deaths due to heart disease is increasing with the coronavirus pandemic. Therefore, automatic classification of electrocardiogram (ECG) signals is crucial for diagnosis and treatment. Thanks to deep learning algorithms, classification can be performed without manual feature extraction. In this study, we propose a novel convolutional neural networks (CNN) architecture to detect ECG types. In addition, the proposed CNN can automatically extract features from images. Here, we classify a real ECG dataset using our proposed CNN which includes 34 layers. While this dataset is one-dimensional signals, these are transformed into images (scalograms) using continuous wavelet transform (CWT). In addition, the proposed CNN is compared to known architectures: AlexNet and SqueezeNet for classifying ECG images, and we find it more effective than others. This study, which not only performed CWT but also implemented short-time Fourier transform, examines the success in recognizing ECG types for the proposed CNN. Besides, different split methods: training and testing, and cross-validation are applied in this study. Eventually, CWT and cross-validation are the best pre-processing and split methods for the proposed CNN, respectively. Although the results are quite good, we benefit from support vector machines (SVM) to obtain the best algorithm and for detecting ECG types. Essentially, the main aim of the study increases classification results. In this way, the proposed CNN is utilized as deep feature extractor and combined with SVM. As a conclusion of this study, we achieve the highest accuracy of 99.21% from the proposed CNN–SVM when using CWT. Therefore, we can express that this framework can be used as an aid to clinicians for ECG-type identification.

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Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures

Cited by 29 publications

References 97 publications

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods

Analysis of Nomadic Civilization in Northern Grassland in Plastic Arts Based on Deep Learning

A novel proposed CNN–SVM architecture for ECG scalograms classification

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