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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:

Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network
U. Rajendra Acharya, Hamido Fujita, Shu Lih Oh, et al.
Future Generation Computer Systems (2017) Vol. 79, pp. 952-959
Closed Access | Times Cited: 251

Showing 1-25 of 251 citing articles:

Deep learning for healthcare applications based on physiological signals: A review
Oliver Faust, Yuki Hagiwara, Jen Hong Tan, et al.
Computer Methods and Programs in Biomedicine (2018) Vol. 161, pp. 1-13
Closed Access | Times Cited: 896
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Özal Yıldırım, Paweł Pławiak, Ru San Tan, et al.
Computers in Biology and Medicine (2018) Vol. 102, pp. 411-420
Closed Access | Times Cited: 716
Automated EEG-based screening of depression using deep convolutional neural network
U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, et al.
Computer Methods and Programs in Biomedicine (2018) Vol. 161, pp. 103-113
Closed Access | Times Cited: 536
A deep learning approach for Parkinson’s disease diagnosis from EEG signals
Shu Lih Oh, Yuki Hagiwara, U. Raghavendra, et al.
Neural Computing and Applications (2018) Vol. 32, Iss. 15, pp. 10927-10933
Closed Access | Times Cited: 522
Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images
U. Raghavendra, Hamido Fujita, Sulatha V. Bhandary, et al.
Information Sciences (2018) Vol. 441, pp. 41-49
Closed Access | Times Cited: 403
A review on deep learning methods for ECG arrhythmia classification
Zahra Ebrahimi, Mohammad Loni, Masoud Daneshtalab, et al.
Expert Systems with Applications X (2020) Vol. 7, pp. 100033-100033
Open Access | Times Cited: 347
A new approach for arrhythmia classification using deep coded features and LSTM networks
Özal Yıldırım, Ulaş Baran Baloğlu, Ru San Tan, et al.
Computer Methods and Programs in Biomedicine (2019) Vol. 176, pp. 121-133
Closed Access | Times Cited: 312
A novel application of deep learning for single-lead ECG classification
Sherin Mary Mathews, Chandra Kambhamettu, Kenneth E. Barner
Computers in Biology and Medicine (2018) Vol. 99, pp. 53-62
Closed Access | Times Cited: 289
Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals
U. Rajendra Acharya, Hamido Fujita, Shu Lih Oh, et al.
Applied Intelligence (2018) Vol. 49, Iss. 1, pp. 16-27
Closed Access | Times Cited: 262
Deep learning in ECG diagnosis: A review
Xinwen Liu, Huan Wang, Zongjin Li, et al.
Knowledge-Based Systems (2021) Vol. 227, pp. 107187-107187
Closed Access | Times Cited: 244
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
Computer-aided diagnosis of atrial fibrillation based on ECG Signals: A review
Yuki Hagiwara, Hamido Fujita, Shu Lih Oh, et al.
Information Sciences (2018) Vol. 467, pp. 99-114
Closed Access | Times Cited: 170
Deep Learning in the Biomedical Applications: Recent and Future Status
Ryad Zemouri, Noureddine Zerhouni, Daniel Racoceanu
Applied Sciences (2019) Vol. 9, Iss. 8, pp. 1526-1526
Open Access | Times Cited: 160
Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds
Jong‐Uk Won, Thomas Czerniawski, Fernanda Leite
Automation in Construction (2020) Vol. 113, pp. 103144-103144
Closed Access | Times Cited: 146
An efficient ECG arrhythmia classification method based on Manta ray foraging optimization
Essam H. Houssein, Ibrahim E. Ibrahim, Nabil Neggaz, et al.
Expert Systems with Applications (2021) Vol. 181, pp. 115131-115131
Closed Access | Times Cited: 120
Automated ECG classification using a non-local convolutional block attention module
Jikuo Wang, Qiao Xu, Changchun Liu, et al.
Computer Methods and Programs in Biomedicine (2021) Vol. 203, pp. 106006-106006
Closed Access | Times Cited: 106
A Hybrid Deep Learning Approach for ECG-Based Arrhythmia Classification
Parul Madan, Vijay Singh, Devesh Pratap Singh, et al.
Bioengineering (2022) Vol. 9, Iss. 4, pp. 152-152
Open Access | Times Cited: 90
Deep Learning in Cardiology
Paschalis Bizopoulos, Dimitrios Koutsouris
IEEE Reviews in Biomedical Engineering (2018) Vol. 12, pp. 168-193
Open Access | Times Cited: 157
Computer Aided detection for fibrillations and flutters using deep convolutional neural network
Hamido Fujita, Dalibor Cimr
Information Sciences (2019) Vol. 486, pp. 231-239
Closed Access | Times Cited: 134
Classification of heart sound signals using a novel deep WaveNet model
Shu Lih Oh, Jahmunah Vicnesh, Chui Ping Ooi, et al.
Computer Methods and Programs in Biomedicine (2020) Vol. 196, pp. 105604-105604
Closed Access | Times Cited: 134
Inter-patient ECG classification with convolutional and recurrent neural networks
Li Guo, Gavin Sim, Bogdan J. Matuszewski
Journal of Applied Biomedicine (2019) Vol. 39, Iss. 3, pp. 868-879
Open Access | Times Cited: 108
Computer-aided diagnosis of congestive heart failure using ECG signals – A review
Jahmunah Vicnesh, Shu Lih Oh, Joel Koh En Wei, et al.
Physica Medica (2019) Vol. 62, pp. 95-104
Closed Access | Times Cited: 107
A deep learning approach for atrial fibrillation signals classification based on convolutional and modified Elman neural network
Jibin Wang
Future Generation Computer Systems (2019) Vol. 102, pp. 670-679
Closed Access | Times Cited: 105
Accurate deep neural network model to detect cardiac arrhythmia on more than 10,000 individual subject ECG records
Özal Yıldırım, Muhammed Talo, Edward J. Ciaccio, et al.
Computer Methods and Programs in Biomedicine (2020) Vol. 197, pp. 105740-105740
Open Access | Times Cited: 103
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