Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Wang, Tao; Lu, Changhua; Sun, Yining; Yang, Mei; Liu, Chun; Ou, Chunsheng

doi:10.3390/e23010119

Cited by 157 publications

(96 citation statements)

References 30 publications

(51 reference statements)

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“…Wang et al [16] presented an automated ECG classification technique depending upon Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). The CWT is utilized for decomposing ECG signals to attain various time frequency modules, and CNN is utilized for extracting features in two-dimensional scalogram consist of aforementioned modules.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Karthik¹,

Madasthu²,

Kavitha³

et al. 2022

Computer Systems Science and Engineering

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Automated biomedical signal processing becomes an essential process to determine the indicators of diseased states. At the same time, latest developments of artificial intelligence (AI) techniques have the ability to manage and analyzing massive amounts of biomedical datasets results in clinical decisions and real time applications. They can be employed for medical imaging; however, the 1D biomedical signal recognition process is still needing to be improved. Electrocardiogram (ECG) is one of the widely used 1-dimensional biomedical signals, which is used to diagnose cardiovascular diseases. Computer assisted diagnostic models find it difficult to automatically classify the 1D ECG signals owing to time-varying dynamics and diverse profiles of ECG signals. To resolve these issues, this study designs automated deep learning based 1D biomedical ECG signal recognition for cardiovascular disease diagnosis (DLECG-CVD) model. The DLECG-CVD model involves different stages of operations such as pre-processing, feature extraction, hyperparameter tuning, and classification. At the initial stage, data pre-processing takes place to convert the ECG report to valuable data and transform it into a compatible format for further processing. In addition, deep belief network (DBN) model is applied to derive a set of feature vectors. Besides, improved swallow swarm optimization (ISSO) algorithm is used for the hyperparameter tuning of the DBN model. Lastly, extreme gradient boosting (XGBoost) classifier is employed to allocate proper class labels to the test ECG signals. In order to verify the improved diagnostic performance of the DLECG-CVD model, a set of simulations is carried out on the benchmark PTB-XL dataset. A detailed comparative study highlighted the betterment of the DLECG-CVD model interms of accuracy, sensitivity, specificity, kappa, Mathew correlation coefficient, and Hamming loss.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Karthik¹,

Madasthu²,

Kavitha³

et al. 2022

Computer Systems Science and Engineering

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“…An automated ECG classification method was proposed based on continuous wavelet transform (CWT) and convolutional neural network (CNN) [12]. CWT was used to decompose ECG signals to obtain different time-frequency components and CNN was used to extract the features of the above time-frequency bands.…”

Section: Relate Workmentioning

confidence: 99%

Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition

Sarshar¹,

Abdossalehi²

2021

RCES

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Today, cardiovascular disease has become an epidemic. Statistics show that one person dies every 33 seconds due to cardiovascular disease. It is estimated that 33% of men and 10% of women have a heart attack before the age of 60. Arrhythmias are abnormal beats that cause the heart to beat too fast or too slow to pump. Automatic electrocardiogram analysis is critical to the diagnosis and treatment of heart patients. There are several learning methods for analyzing ECG signals to diagnose arrhythmias. In the proposed method, the heart rate signals are decomposed into different sub bands using the Tunable Q-Factor Wavelet Transform (TQWT) method, then the features are extracted and modified using classification with the aim of better classifying and separating data in the process of identifying the clinical features of the class. They are classified so that normal people and people with cardiac arrhythmias can be distinguished from their ECG signals. The results showed that the proposed method classifies the ECG signal with 99.25% accuracy. Since accuracy in diagnosing cardiac arrhythmias in medicine is a vital and important factor, so the proposed method can be very effective for the decision of cardiologists.

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“…To our knowledge, convolutional neural network (CNN) works well with ECG recordings from the data acquisition IoT devices. Appropriate ECG signal processing with the CNN learns features using patient needs with abnormalities in arrhythmia and heart failure [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

Mohana

Yakkala

Vimalnath³

et al. 2022

Journal of Healthcare Engineering

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Population at risk can benefit greatly from remote health monitoring because it allows for early detection and treatment. Because of recent advances in Internet-of-Things (IoT) paradigms, such monitoring systems are now available everywhere. Due to the essential nature of the patients being monitored, these systems demand a high level of quality in aspects such as availability and accuracy. In health applications, where a lot of data are accessible, deep learning algorithms have the potential to perform well. In this paper, we develop a deep learning architecture called the convolutional neural network (CNN), which we examine in this study to see if it can be implemented. The study uses the IoT system with a centralised cloud server, where it is considered as an ideal input data acquisition module. The study uses cloud computing resources by distributing CNN operations to the servers with outsourced fitness functions to be performed at the edge. The results of the simulation show that the proposed method achieves a higher rate of classifying the input instances from the data acquisition tools than other methods. From the results, it is seen that the proposed CNN achieves an average accurate rate of 99.6% on training datasets and 86.3% on testing datasets.

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Automatic ECG Classification Using Continuous Wavelet Transform and Convolutional Neural Network

Cited by 157 publications

References 30 publications

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated Deep Learning Based Cardiovascular Disease Diagnosis Using ECG Signals

Automated Cardiovascular Arrhythmia Classification Based on Through Nonlinear Features and Tunable-Q Wavelet Transform (TQWT) Based Decomposition

Application of Internet of Things on the Healthcare Field Using Convolutional Neural Network Processing

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