A deep convolutional neural network model to classify heartbeats
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2017/10/01
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Indian UGC (journal)
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Refrences39
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Citations672
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U. Rajendra Acharya
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Shu Lih Oh
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Yuki Hagiwara
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Jen Hong Tan
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Muhammad Adam
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Arkadiusz Gertych
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Ru San Tan
Cite
Acharya, U. Rajendra, et al. “A Deep Convolutional Neural Network Model to Classify Heartbeats”. Computers in Biology and Medicine, vol. 89, 2017, pp. 389-96, https://doi.org/10.1016/j.compbiomed.2017.08.022.
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adam, M., Gertych, A., & Tan, R. S. (2017). A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine, 89, 389-396. https://doi.org/10.1016/j.compbiomed.2017.08.022
Acharya, U. Rajendra, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych, and Ru San Tan. “A Deep Convolutional Neural Network Model to Classify Heartbeats”. Computers in Biology and Medicine 89 (2017): 389-96. https://doi.org/10.1016/j.compbiomed.2017.08.022.
1.
Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adam M, Gertych A, et al. A deep convolutional neural network model to classify heartbeats. Computers in Biology and Medicine. 2017;89:389-96.
Journal Categories
Medicine
Medicine (General)
Computer applications to medicine
Medical informatics
Medicine
Medicine (General)
Medical technology
Science
Biology (General)
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Convolutional neural networks for time series classification | 2017 | |||
| ECG Classification Using Wavelet Packet Entropy and Random Forests | Entropy |
| 245 | 2016 |
| Current methods in electrocardiogram characterization | Computers in Biology and Medicine |
| 146 | 2014 |
| Computer aided diagnosis of atrial arrhythmia using dimensionality reduction methods on transform domain representation | Biomedical Signal Processing and Control |
| 75 | 2014 |
| Application of higher order cumulant features for cardiac health diagnosis using ECG signals | International Journal of Neural Systems |
| 2013 |
Refrences Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 8
is the most frequently represented among the references in this article. It primarily includes studies from Biomedical Signal Processing and Control and Information Sciences. The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Point Calculation Detection System Based on In Suit Convolution Transistor for Clinical Diagnosis of Heart Disease | Advanced Functional Materials |
| 2024 | |
Prostate cancer grading framework based on deep transfer learning and Aquila optimizer | Neural Computing and Applications |
| 2024 | |
| Performance evaluation of convolution neural network models for detection of abnormal and ventricular ectopic beat cardiac episodes | Multimedia Tools and Applications |
| 2024 | |
| ADLNet: an adaptive network for arrhythmia classification based on deformable convolution and LSTM | Signal, Image and Video Processing |
| 2024 | |
| An effective hybrid optimal deep learning approach using BI-LSTM and restricted Boltzmann machines whale optimization to detect arrhythmia | Multiscale and Multidisciplinary Modeling, Experiments and Design |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 222
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications and was published in 2017. The most recent citation comes from a 2024 study titled Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads. This article reached its peak citation in 2022, with 156 citations. It has been cited in 262 different journals, 21% of which are open access. Among related journals, the IEEE Access cited this research the most, with 44 citations. The chart below illustrates the annual citation trends for this article.