Laishuan Wang

584 total citations
32 papers, 356 citations indexed

About

Laishuan Wang is a scholar working on Pediatrics, Perinatology and Child Health, Cognitive Neuroscience and Molecular Biology. According to data from OpenAlex, Laishuan Wang has authored 32 papers receiving a total of 356 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Pediatrics, Perinatology and Child Health, 12 papers in Cognitive Neuroscience and 6 papers in Molecular Biology. Recurrent topics in Laishuan Wang's work include Neonatal and fetal brain pathology (13 papers), EEG and Brain-Computer Interfaces (12 papers) and Infant Health and Development (5 papers). Laishuan Wang is often cited by papers focused on Neonatal and fetal brain pathology (13 papers), EEG and Brain-Computer Interfaces (12 papers) and Infant Health and Development (5 papers). Laishuan Wang collaborates with scholars based in China, Netherlands and Finland. Laishuan Wang's co-authors include Wei Chen, Chunmei Lu, Chen Chen, Saadullah Farooq Abbasi, Xi Long, Bin Yin, Saeed Akbarzadeh, Feng Shu, Wenhao Zhou and Yun Cao and has published in prestigious journals such as IEEE Access, The Journal of Pediatrics and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Laishuan Wang

28 papers receiving 349 citations

Peers

Laishuan Wang
Sanjay Raghav Australia
Sandy Rihana Lebanon
Kiret Dhindsa Canada
Mohamed Salah Khlif Australia
Mithri R. Junna United States
Sofiane Boudaoud France
Shahnawaz Alam India
Jan Peter van Zandwijk Netherlands
Mohammad Shahbakhti Iran
Sanjay Raghav Australia
Laishuan Wang
Citations per year, relative to Laishuan Wang Laishuan Wang (= 1×) peers Sanjay Raghav

Countries citing papers authored by Laishuan Wang

Since Specialization
Citations

This map shows the geographic impact of Laishuan Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Laishuan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laishuan Wang more than expected).

Fields of papers citing papers by Laishuan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Laishuan Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Laishuan Wang. The network helps show where Laishuan Wang may publish in the future.

Co-authorship network of co-authors of Laishuan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Laishuan Wang. A scholar is included among the top collaborators of Laishuan Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Laishuan Wang. Laishuan Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Laishuan, Abdülhamit Subaşı, Chen Chen, et al.. (2025). Smart IoT-Based Solutions for Neonatal Sleep Stratification: Single-Dual Channel EEG, AdaptiSelect, Multiview Fusion, and Rotational Ensemble Stacking. IEEE Internet of Things Journal. 12(22). 46018–46037.
2.
Subaşı, Abdülhamit, et al.. (2025). A Novel NICU Sleep State Stratification: Multiperspective Features, Adaptive Feature Selection and Ensemble Model. IEEE Transactions on Biomedical Engineering. 72(9). 2684–2697. 1 indexed citations
3.
Wang, Laishuan, et al.. (2024). Single-Channel EEG Data Analysis Using a Multi-Branch CNN for Neonatal Sleep Staging. IEEE Access. 12. 29910–29925. 12 indexed citations
4.
Chen, Xuelian, et al.. (2023). Febrile seizure in children with COVID-19 during the Omicron wave. Frontiers in Pediatrics. 11. 1197156–1197156. 1 indexed citations
5.
Wang, Laishuan, et al.. (2023). MS-HNN: Multi-Scale Hierarchical Neural Network With Squeeze and Excitation Block for Neonatal Sleep Staging Using a Single-Channel EEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 31. 2195–2204. 19 indexed citations
6.
Chen, Chen, et al.. (2023). An Ensemble Voting Approach With Innovative Multi-Domain Feature Fusion for Neonatal Sleep Stratification. IEEE Access. 12. 206–218. 8 indexed citations
7.
Wu, Yonglin, et al.. (2023). A Sequential End-to-End Neonatal Sleep Staging Model with Squeeze and Excitation Blocks and Sequential Multi-Scale Convolution Neural Networks. International Journal of Neural Systems. 34(3). 2450013–2450013. 5 indexed citations
8.
Yin, Lijun, Lu Lu, Guoping Lü, et al.. (2023). Molecular characteristics of carbapenem-resistant gram-negative bacilli in pediatric patients in China. BMC Microbiology. 23(1). 136–136. 5 indexed citations
9.
10.
Ye, Ziqing, Yuhuan Wang, Zifei Tang, et al.. (2023). Understanding endoscopic and clinicopathological features of patients with very early onset inflammatory bowel disease: Results from a decade of study. Digestive and Liver Disease. 56(1). 50–54.
11.
Ren, Haoran, Xinyu Jiang, Long Meng, et al.. (2022). fNIRS-Based Dynamic Functional Connectivity Reveals the Innate Musical Sensing Brain Networks in Preterm Infants. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 30. 1806–1816. 7 indexed citations
12.
Awais, Muhammad, Xi Long, Bin Yin, et al.. (2021). A Hybrid DCNN-SVM Model for Classifying Neonatal Sleep and Wake States Based on Facial Expressions in Video. IEEE Journal of Biomedical and Health Informatics. 25(5). 1441–1449. 38 indexed citations
13.
Gan, Mingyu, Kai Yan, Feifan Xiao, et al.. (2021). Combining Metagenomic Sequencing With Whole Exome Sequencing to Optimize Clinical Strategies in Neonates With a Suspected Central Nervous System Infection. Frontiers in Cellular and Infection Microbiology. 11. 671109–671109. 29 indexed citations
14.
Dong, Xinran, Bingbing Wu, Huijun Wang, et al.. (2021). Clinical and Genetic Etiologies of Neonatal Unconjugated Hyperbilirubinemia in the China Neonatal Genomes Project. The Journal of Pediatrics. 243. 53–60.e9. 12 indexed citations
15.
Abbasi, Saadullah Farooq, Jawad Ahmad, Ahsen Tahir, et al.. (2020). EEG-Based Neonatal Sleep-Wake Classification Using Multilayer Perceptron Neural Network. IEEE Access. 8. 183025–183034. 52 indexed citations
16.
Chen, Hongyu, Wei Chen, Chunmei Lu, et al.. (2020). Design of an Integrated Wearable Multi-Sensor Platform Based on Flexible Materials for Neonatal Monitoring. IEEE Access. 8. 23732–23747. 39 indexed citations
17.
Awais, Muhammad, Chen Chen, Xi Long, et al.. (2020). Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition. IEEE Access. 8. 59100–59113. 15 indexed citations
18.
Awais, Muhammad, Xi Long, Bin Yin, et al.. (2020). Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?. BMC Research Notes. 13(1). 507–507. 17 indexed citations
19.
Song, Dongli & Laishuan Wang. (2018). Gut-microbiota-brain axis: implications and progress in neonatology. Zhonghua weichan yixue zazhi. 21(7). 435–441.
20.
Zhou, Qinhua, Laishuan Wang, Chao Chen, et al.. (2012). A Case Series of 130 Neonates with Congenital Syphilis: Preterm Neonates Had More Clinical Evidences of Infection than Term Neonates. Neonatology. 102(2). 152–156. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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