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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection
Bin Liu, Jikui Liu, Guoqing Wang, et al.
Computers in Biology and Medicine (2014) Vol. 61, pp. 178-184
Closed Access | Times Cited: 123

Showing 1-25 of 123 citing articles:

Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals
U. Rajendra Acharya, Hamido Fujita, Shu Lih Oh, et al.
Information Sciences (2017) Vol. 415-416, pp. 190-198
Closed Access | Times Cited: 842
ECG Heartbeat Classification: A Deep Transferable Representation
Mohammad Kachuee, Shayan Fazeli, Majid Sarrafzadeh
(2018), pp. 443-444
Open Access | Times Cited: 449
Automated detection and localization of myocardial infarction using electrocardiogram: a comparative study of different leads
U. Rajendra Acharya, Hamido Fujita, Vidya K. Sudarshan, et al.
Knowledge-Based Systems (2016) Vol. 99, pp. 146-156
Closed Access | Times Cited: 216
Automated characterization and classification of coronary artery disease and myocardial infarction by decomposition of ECG signals: A comparative study
U. Rajendra Acharya, Hamido Fujita, Muhammad Adam, et al.
Information Sciences (2016) Vol. 377, pp. 17-29
Closed Access | Times Cited: 214
Comprehensive electrocardiographic diagnosis based on deep learning
Oh Shu Lih, Jahmunah Vicnesh, Ru San Tan, et al.
Artificial Intelligence in Medicine (2020) Vol. 103, pp. 101789-101789
Open Access | Times Cited: 190
Detecting and interpreting myocardial infarction using fully convolutional neural networks
Nils Strodthoff, Claas Strodthoff
Physiological Measurement (2018) Vol. 40, Iss. 1, pp. 015001-015001
Open Access | Times Cited: 172
Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram
Salah S. Al‐Zaiti, Lucas Besomi, Zeineb Bouzid, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 152
ML–ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG
Chuang Han, Li Shi
Computer Methods and Programs in Biomedicine (2019) Vol. 185, pp. 105138-105138
Closed Access | Times Cited: 149
Automated detection of coronary artery disease, myocardial infarction and congestive heart failure using GaborCNN model with ECG signals
V. Jahmunah, E. Y. K. Ng, Ru San Tan, et al.
Computers in Biology and Medicine (2021) Vol. 134, pp. 104457-104457
Closed Access | Times Cited: 106
SLC-GAN: An automated myocardial infarction detection model based on generative adversarial networks and convolutional neural networks with single-lead electrocardiogram synthesis
Wenqiang Li, Yuk Ming Tang, Kai Yu, et al.
Information Sciences (2022) Vol. 589, pp. 738-750
Closed Access | Times Cited: 68
Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection
Wenhan Liu, Mengxin Zhang, Yidan Zhang, et al.
IEEE Journal of Biomedical and Health Informatics (2017) Vol. 22, Iss. 5, pp. 1434-1444
Closed Access | Times Cited: 158
Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework
Mohit Kumar, Ram Bilas Pachori, U. Rajendra Acharya
Entropy (2017) Vol. 19, Iss. 9, pp. 488-488
Open Access | Times Cited: 133
Inferior myocardial infarction detection using stationary wavelet transform and machine learning approach
Lakhan Dev Sharma, Ramesh Kumar Sunkaria
Signal Image and Video Processing (2017) Vol. 12, Iss. 2, pp. 199-206
Closed Access | Times Cited: 122
Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities
Ahmed S. Alghamdi, Mohamed Hammad, Hassan Ugail, et al.
Multimedia Tools and Applications (2020) Vol. 83, Iss. 5, pp. 14913-14934
Closed Access | Times Cited: 114
A Novel Approach for Detection of Myocardial Infarction From ECG Signals of Multiple Electrodes
Rajesh Kumar Tripathy, Abhijit Bhattacharyya, Ram Bilas Pachori
IEEE Sensors Journal (2019) Vol. 19, Iss. 12, pp. 4509-4517
Closed Access | Times Cited: 106
Automated Identification of Myocardial Infarction Using Harmonic Phase Distribution Pattern of ECG Data
Deboleena Sadhukhan, Saurabh Pal, Madhuchhanda Mitra
IEEE Transactions on Instrumentation and Measurement (2018) Vol. 67, Iss. 10, pp. 2303-2313
Closed Access | Times Cited: 104
A novel automated diagnostic system for classification of myocardial infarction ECG signals using an optimal biorthogonal filter bank
Manish Sharma, Ru San Tan, U. Rajendra Acharya
Computers in Biology and Medicine (2018) Vol. 102, pp. 341-356
Closed Access | Times Cited: 102
A Review of Automated Methods for Detection of Myocardial Ischemia and Infarction Using Electrocardiogram and Electronic Health Records
Sardar Ansari, Negar Farzaneh, Marlena Duda, et al.
IEEE Reviews in Biomedical Engineering (2017) Vol. 10, pp. 264-298
Open Access | Times Cited: 101
Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal
U. Rajendra Acharya, Hamido Fujita, Vidya K. Sudarshan, et al.
Knowledge-Based Systems (2017) Vol. 132, pp. 156-166
Closed Access | Times Cited: 92
ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation
Zhidong Zhao, Yefei Zhang, Yanjun Deng, et al.
Computers in Biology and Medicine (2018) Vol. 102, pp. 168-179
Closed Access | Times Cited: 88
Hybrid Network with Attention Mechanism for Detection and Location of Myocardial Infarction Based on 12-Lead Electrocardiogram Signals
Lidan Fu, Binchun Lu, Bo Nie, et al.
Sensors (2020) Vol. 20, Iss. 4, pp. 1020-1020
Open Access | Times Cited: 81
PerAE: An Effective Personalized AutoEncoder for ECG-Based Biometric in Augmented Reality System
Le Sun, Zhaoyi Zhong, Zhiguo Qu, et al.
IEEE Journal of Biomedical and Health Informatics (2022) Vol. 26, Iss. 6, pp. 2435-2446
Closed Access | Times Cited: 43
Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments
Sahar A. El Rahman, Ala Saleh Alluhaidan
PLoS ONE (2024) Vol. 19, Iss. 2, pp. e0291084-e0291084
Open Access | Times Cited: 8
Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine
Pengwei Xing, Ran Su, Fei Guo, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 86
Automated interpretable detection of myocardial infarction fusing energy entropy and morphological features
Chuang Han, Shi Li
Computer Methods and Programs in Biomedicine (2019) Vol. 175, pp. 9-23
Closed Access | Times Cited: 73
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