Deep learning approach for active classification of electrocardiogram signals

By Mohamad Mahmoud Al Rahhal, Yakoub Bazi, Haikel Salem Alhichri, Naif Alajlan, Farid Melgani, Ronald R. Yager. Information Sciences, 2016

601 citations recorded by the source corpus.

In this paper, we propose a novel approach based on deep learning for active classification of electrocardiogram (ECG) signals. To this end, we learn a suitable feature representation from the raw ECG data in an unsupervised way using stacked denoising autoencoders (SDAEs) with sparsity constraint. After this feature learning phase, we add a softmax regression layer on the top of the resulting hidden representation layer yielding the so-called deep neural network (DNN). During the interaction phase, we allow the expert at each iteration to label the most relevant and uncertain ECG beats in the test record, which are then used for updating the DNN weights. As ranking criteria, the method relies on the DNN posterior probabilities to associate confidence measures such as entropy and Breaking-Ties (BT) to each test beat in the ECG record under analysis. In the experiments, we validate the method on the well-known MIT-BIH arrhythmia database as well as two other databases called INCART, and SVDB, respectively. Furthermore, we follow the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) for class labeling and results presentation. The results obtained show that the newly proposed approach provides significant accuracy improvements with less expert interaction and faster online retraining compared to state-of-the-art methods.

Subfields: Cardiology and Cardiovascular Medicine, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/54f83d9db83a87059e6ebde6304493a2953877a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. M. Zhou
2. Taiyong Li

In Cognitive Neuroscience

1. M. Zhou
2. Xiaojun Chang
3. Peng Xu
4. Taiyong Li
5. Dezhong Yao

Citations in the corpus

• Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning (2021, methodology): “[8] in which deep neural networks are used for choosing the best features and then using them.”
• ECG Classification Using Wavelet Packet Entropy and Random Forests (2016, methodology): “Besides the above-mentioned classifiers, some scholars also use Linear Discriminants [4,30,31], Extreme Leaning Machine [32], Optimum-path forest [1], Active Learning [33], and so on to build classification models.”
• EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations (2019, background): “Besides, through the results and conclusions reported in [43],”
• A Survey of Deep Active Learning (2020, methodology): “For example, [122] proposes an active DL-based ECG signal classification method.”
• Fine-tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally (2017, background): “[1] exploited deep learning for active electrocardiogram classification.”
• Deep-ECG: Convolutional Neural Networks for ECG biometric recognition (2019, background): “Only a few studies have considered the application of deep learning strategies for ECG analysis, but they focus on the classification of heartbeats in healthy and non-healthy [30] using techniques such as CNNs [24, 25, 33], autoencoders [34], or deep belief networks [18].”
• Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques (2021, methodology): “[39] utilized a deep neural network (DNN) model for the classification of ECG signals to study the top set of features.”