Automatic ECG Diagnosis Using Convolutional Neural Network

Avanzato, Roberta; Beritelli, Francesco

doi:10.3390/electronics9060951

Cited by 90 publications

(40 citation statements)

References 34 publications

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“…The network classified five different kinds of anomalies with a detection accuracy of 92.7%. Avanzato et al [ 57 ] proposed an automatic heart disease diagnosis system using ECG signals based on the direct application of a 1D-convolutional neural network. The network consisted of three layers in addition to the input layer and the output layer.…”

Section: Resultsmentioning

confidence: 99%

“…Results showed that the model achieved a significant classification accuracy and superior computational efficiency than most of the state-of-the-art methods for ECG signal classification. Avanzato et al [ 57 ] proposed a new neural architecture based on 1D-CNN for the development of automatic heart disease diagnosis systems using ECG signals. The model was performed on 30 s segments and classified three different classes of anomalies.…”

Section: Introductionmentioning

confidence: 99%

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Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

et al. 2021

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The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

et al. 2021

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“…For example, if the input matrix size to be convoluted is 5 × 5 and the convolution kernel size is Based on the above operation rules, the input signal is scanned with a convolution kernel of preset size and a convolution step size [26]; that is, the convolution kernel is used to cover an input signal region of the same size to perform the operation from the input to the corresponding position of the convolution kernel in the region, and the result is taken as a basic element of the convolution output. During the calculation of the convolutional layer, the size of the output image needs to be designed in each layer.…”

Section: Convolution Layermentioning

confidence: 99%

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Song

2020

Information

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The proper evaluation of heart health requires professional medical experience. Therefore, in clinical diagnosis practice, the development direction is to reduce the high dependence of the diagnosis process on medical experience and to more effectively improve the diagnosis efficiency and accuracy. Deep learning has made remarkable achievements in intelligent image analysis technology involved in the medical process. From the aspect of cardiac diagnosis, image analysis can extract more profound and abundant information than sequential electrocardiogram (ECG) signals. Therefore, a new region recognition and diagnosis method model of a two-dimensional ECG (2D-ECG) signal based on an image format is proposed. This method can identify and diagnose each refined waveform in the cardiac conduction cycle reflected in the image format ECG signal, so as to realize the rapid and accurate positioning and visualization of the target recognition area and finally get the analysis results of specific diseases. The test results show that compared with the results obtained by a one-dimensional sequential ECG signal, the proposed model has higher average diagnostic accuracy (98.94%) and can assist doctors in disease diagnosis with better visualization effect.

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“…Deep learning models [ 4 , 5 , 6 , 7 ] are comparatively efficient in performing the classification process from the images and the data. There has been a demand in the field of healthcare diagnosis in precise identification of the abnormality and classifying the category of the disease from the X-ray, Magnetic Resonance Imaging (MRI), Computer Tomography (CT), Positron Emission Tomography (PET) images, and the signal data like the Electrocardiogram (ECG), Electroencephalogram (EEG), and Electromyography(EMG) [ 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The precise identification of the disease category will assist in providing better treatment for patients.…”

Section: Introductionmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

488

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Automatic ECG Diagnosis Using Convolutional Neural Network

Cited by 90 publications

References 34 publications

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

A New Method for Refined Recognition for Heart Disease Diagnosis Based on Deep Learning

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

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