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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:

Parkinson's disease: Cause factors, measurable indicators, and early diagnosis
Shreya Bhat, U. Rajendra Acharya, Yuki Hagiwara, et al.
Computers in Biology and Medicine (2018) Vol. 102, pp. 234-241
Closed Access | Times Cited: 170

Showing 1-25 of 170 citing articles:

Automated Depression Detection Using Deep Representation and Sequence Learning with EEG Signals
Betül Ay, Özal Yıldırım, Muhammed Talo, et al.
Journal of Medical Systems (2019) Vol. 43, Iss. 7
Closed Access | Times Cited: 238
NRF2 as a Therapeutic Target in Neurodegenerative Diseases
Mikah S. Brandes, Nora E. Gray
ASN NEURO (2020) Vol. 12, pp. 175909141989978-175909141989978
Open Access | Times Cited: 211
Rotenone-Induced Model of Parkinson’s Disease: Beyond Mitochondrial Complex I Inhibition
María Teresa Ibarra-Gutiérrez, Norma Serrano‐García, Marisol Orozco‐Ibarra
Molecular Neurobiology (2023) Vol. 60, Iss. 4, pp. 1929-1948
Closed Access | Times Cited: 74
Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders
U. Raghavendra, U. Rajendra Acharya, Hojjat Adeli
European Neurology (2019) Vol. 82, Iss. 1-3, pp. 41-64
Open Access | Times Cited: 131
Automated detection of Parkinson's disease using minimum average maximum tree and singular value decomposition method with vowels
Türker Tuncer, Şengül Doğan, U. Rajendra Acharya
Journal of Applied Biomedicine (2019) Vol. 40, Iss. 1, pp. 211-220
Closed Access | Times Cited: 103
A Multi-Objective Evolutionary Approach Based on Graph-in-Graph for Neural Architecture Search of Convolutional Neural Networks
Yu Xue, Pengcheng Jiang, Ferrante Neri, et al.
International Journal of Neural Systems (2021) Vol. 31, Iss. 09, pp. 2150035-2150035
Open Access | Times Cited: 94
Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease
E. Balaji, D. Brindha, Balakrishnan Ramasamy
Applied Soft Computing (2020) Vol. 94, pp. 106494-106494
Closed Access | Times Cited: 90
PDCNNet: An Automatic Framework for the Detection of Parkinson’s Disease Using EEG Signals
Smith K. Khare, Varun Bajaj, U. Rajendra Acharya
IEEE Sensors Journal (2021) Vol. 21, Iss. 15, pp. 17017-17024
Closed Access | Times Cited: 87
GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals
Hui Wen Loh, Chui Ping Ooi, Elizabeth E. Palmer, et al.
Electronics (2021) Vol. 10, Iss. 14, pp. 1740-1740
Open Access | Times Cited: 87
An attempt to elucidate the role of iron and zinc ions in development of Alzheimer’s and Parkinson’s diseases
Karolina Wojtunik‐Kulesza, Anna Oniszczuk, Monika Waksmundzka‐Hajnos
Biomedicine & Pharmacotherapy (2019) Vol. 111, pp. 1277-1289
Open Access | Times Cited: 84
Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021)
Hui Wen Loh, Wanrong Hong, Chui Ping Ooi, et al.
Sensors (2021) Vol. 21, Iss. 21, pp. 7034-7034
Open Access | Times Cited: 80
Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs
Moises Díaz, Momina Moetesum, Imran Siddiqi, et al.
Expert Systems with Applications (2020) Vol. 168, pp. 114405-114405
Open Access | Times Cited: 70
Detection of Parkinson’s disease using automated tunable Q wavelet transform technique with EEG signals
Smith K. Khare, Varun Bajaj, U. Rajendra Acharya
Journal of Applied Biomedicine (2021) Vol. 41, Iss. 2, pp. 679-689
Closed Access | Times Cited: 70
Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review
Sudip Paul, Mahesh Maindarkar, Sanjay Saxena, et al.
Diagnostics (2022) Vol. 12, Iss. 1, pp. 166-166
Open Access | Times Cited: 51
Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques
Majid Aljalal, Saeed A. Aldosari, Khalil AlSharabi, et al.
Diagnostics (2022) Vol. 12, Iss. 5, pp. 1033-1033
Open Access | Times Cited: 51
Cardiovascular/Stroke Risk Stratification in Parkinson’s Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review
Jasjit S. Suri, Sudip Paul, Mahesh Maindarkar, et al.
Metabolites (2022) Vol. 12, Iss. 4, pp. 312-312
Open Access | Times Cited: 37
Secure medical image transmission using deep neural network in e‐health applications
Ala Abdulsalam Alarood, Muhammad Faheem, Mahmoud Ahmad Al‐Khasawneh, et al.
Healthcare Technology Letters (2023) Vol. 10, Iss. 4, pp. 87-98
Open Access | Times Cited: 32
From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology
Corneliu Toader, Nicolaie Dobrin, Felix-Mircea Brehar, et al.
International Journal of Molecular Sciences (2023) Vol. 24, Iss. 22, pp. 16119-16119
Open Access | Times Cited: 25
Parkinson’s disease diagnosis using deep learning: A bibliometric analysis and literature review
Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Sarminah Samad, et al.
Ageing Research Reviews (2024) Vol. 96, pp. 102285-102285
Closed Access | Times Cited: 11
Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements
Aananya Reddy, Ruhananhad P. Reddy, Aryan Kia Roghani, et al.
Ageing Research Reviews (2024) Vol. 99, pp. 102410-102410
Closed Access | Times Cited: 10
Detecting Parkinson’s disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics
Luka Jovanovic, Robertas Damaševičius, Rade Matić, et al.
PeerJ Computer Science (2024) Vol. 10, pp. e2031-e2031
Open Access | Times Cited: 8
Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls
Yue Wu, Jiehui Jiang, Li Chen, et al.
Annals of Translational Medicine (2019) Vol. 7, Iss. 23, pp. 773-773
Open Access | Times Cited: 66
Improved Overlap-based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson’s Disease
Pattaramon Vuttipittayamongkol, Eyad Elyan
International Journal of Neural Systems (2020) Vol. 30, Iss. 08, pp. 2050043-2050043
Open Access | Times Cited: 66
Emerging concepts of mitochondrial dysfunction in Parkinson’s disease progression: Pathogenic and therapeutic implications
Linchi Rani, Amal Chandra Mondal
Mitochondrion (2019) Vol. 50, pp. 25-34
Closed Access | Times Cited: 59
Review of Research and Development of Supernumerary Robotic Limbs
Yuchuang Tong, Jinguo Liu
IEEE/CAA Journal of Automatica Sinica (2021) Vol. 8, Iss. 5, pp. 929-952
Closed Access | Times Cited: 54
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