Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning

Demonbreun, Andrew; Mirsky, Grace M.

doi:10.22489/cinc.2020.138

Cited by 5 publications

(9 citation statements)

References 12 publications

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“…Various teams that participated in the Physionet/Challenge considered the deep learning approach [ 27 , 28 , 29 , 30 ], showing a particular interest in this methodology. For example, the team with the highest score [ 27 ] considered both raw ECG data and ECG features extracted from ECG signals, including age and gender.…”

Section: Resultsmentioning

confidence: 99%

“…A deep neural network with a modified residual neural network architecture was considered in [ 28 ], in which the scatter blocks processed the 12 leads separately. In [ 29 ], wavelet analysis and a convolutional network were used for each single lead, and a single output label was obtained, reducing the diagnostic categories to the individual and the most frequent combinations. In [ 30 ], the authors combined a rule-based model and a squeeze-and-excitation network.…”

Section: Resultsmentioning

confidence: 99%

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Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

2021

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The main objective of this study is to propose relatively simple techniques for the automatic diagnosis of electrocardiogram (ECG) signals based on a classical rule-based method and a convolutional deep learning architecture. The validation task was performed in the framework of the PhysioNet/Computing in Cardiology Challenge 2020, where seven databases consisting of 66,361 recordings with 12-lead ECGs were considered for training, validation and test sets. A total of 24 different diagnostic classes are considered in the entire training set. The rule-based method uses morphological and time-frequency ECG descriptors that are defined for each diagnostic label. These rules are extracted from the knowledge base of a cardiologist or from a textbook, with no direct learning procedure in the first phase, whereas a refinement was tested in the second phase. The deep learning method considers both raw ECG and median beat signals. These data are processed via continuous wavelet transform analysis, obtaining a time-frequency domain representation, with the generation of specific images (ECG scalograms). These images are then used for the training of a convolutional neural network based on GoogLeNet topology for ECG diagnostic classification. Cross-validation evaluation was performed for testing purposes. A total of 217 teams submitted 1395 algorithms during the Challenge. The diagnostic accuracy of our algorithm produced a challenge validation score of 0.325 (CPU time = 35 min) for the rule-based method, and a 0.426 (CPU time = 1664 min) for the deep learning method, which resulted in our team attaining 12th place in the competition.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

2021

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“…The ECG signals are transformed into scaleograms before being trained using the transfer learning SqueezeNet model for picture categorization. As a result, the entire test score is 0.205 and the achieved validation score is 0.214, respectively [7]- [9]. With the use of feature reduction and a rule-based fuzzy classifier, cardiovascular disease was predicted with an accuracy of 76.51% in an experiment conducted on UCI datasets [10].…”

Section: IImentioning

confidence: 99%

ECG Classification using Deep Learning

Atmakuri

2024

IJRASET

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The importance of ECG categorization has grown significantly, as demonstrated by the many contemporary medical applications that address this issue. A plethora of machine learning (ML) tools are already available for the analysis and classification of ECG data. The primary drawbacks of these machine learning outcomes are the utilization of heuristic handcrafted or manufactured features with shallow feature learning architectures, or the acquisition of numerical data using image processing. The use of CNN, which recognizes and captures specific patterns in images, is one suggested solution for this problem utilizing deep learning architecture. Recurrent neural networks (RNN) and their variant LSTM are advantageous in the ECG classification task due to their implicit capacity to effectively capture temporal dependencies and noise while providing interpretability of hidden states. In contrast to human results, these Deep Learning Techniques yield results about the employment of Recurrent Neural Networks with LSTM layers and Convolutional Neural Networks without any featureengineered procedure and competing classification accuracy

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“…Leveraging the transfer learning SqueezeNet model for image classification, the ECG signals are converted to scalograms before going through the training process. By this, the achieved validation score is 0.214 and the full test score is 0.205, respectively [ 17 – 19 ].…”

Section: Literature Reviewmentioning

confidence: 99%

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

Rahman

Asif

Sultan

et al. 2022

Computational Intelligence and Neuroscience

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According to the World Health Organization (WHO) report, heart disease is spreading throughout the world very rapidly and the situation is becoming alarming in people aged 40 or above (Xu, 2020). Different methods and procedures are adopted to detect and diagnose heart abnormalities. Data scientists are working on finding the different methods with the required accuracy (Strodthoff et al., 2021). Electrocardiogram (ECG) is the procedure to find the heart condition in the waveform. For ages, the machine learning techniques, which are feature based, played a vital role in the medical sciences and centralized the data in cloud computing and having access throughout the world. Furthermore, deep learning or transfer learning widens the vision and introduces different transfer learning methods to ensure accuracy and time management to detect the ECG in a better way in comparison to the previous and machine learning methods. Hence, it is said that transfer learning has turned world research into more appropriate and innovative research. Here, the proposed comparison and accuracy analysis of different transfer learning methods by using ECG classification for detecting ECG Arrhythmia (CAA-TL). The CAA-TL model has the multiclassification of the ECG dataset, which has been taken from Kaggle. Some of the healthy and unhealthy datasets have been taken in real-time, augmented, and fused with the Kaggle dataset, i.e., Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH dataset). The CAA-TL worked on the accuracy of heart problem detection by using different methods like ResNet50, AlexNet, and SqueezeNet. All three deep learning methods showed remarkable accuracy, which is improved from the previous research. The comparison of different deep learning approaches with respect to layers widens the research and gives the more clarity and accuracy and at the same time finds it time-consuming while working with multiclassification with massive dataset of ECG. The implementation of the proposed method showed an accuracy of 98.8%, 90.08%, and 91% for AlexNet, SqueezeNet, and ResNet50, respectively.

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Automated Classification of Electrocardiograms Using Wavelet Analysis and Deep Learning

Cited by 5 publications

References 12 publications

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

Potential of Rule-Based Methods and Deep Learning Architectures for ECG Diagnostics

ECG Classification using Deep Learning

ECG Classification for Detecting ECG Arrhythmia Empowered with Deep Learning Approaches

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