Dr Tianning Li

Dr Tianning Li
NameDr Tianning Li
Email Addresstianning.li@unisq.edu.au
Job TitleLecturer (Computing)
QualificationsBISM Nanjing, MAccFin Adelaide, PhD USQ
DepartmentSchool of Mathematics, Physics and Computing
AffiliationsSchool of Agriculture and Environmental Science
ORCIDhttps://orcid.org/0000-0001-5142-8654
  • 10215
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    total downloads of outputs
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  • 35
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Employment

PositionOrganisationFromTo
LecturerUniversity of Southern Queensland2017

Fields of Research

  • 400399. Biomedical engineering not elsewhere classified
  • 400607. Signal processing
  • 461199. Machine learning not elsewhere classified
BISM
Nanjing
2009
MAccFin
Adelaide
2012
PhD
USQ
2015

Current Supervisions

Research TitleSupervisor TypeLevel of StudyCommenced
Depth of Anaesthesia Assessment and Prediction Based on ElectroencephalographPrincipal SupervisorDoctoral2025
Depth of Anaesthesia assessment and Anaesthesia state change early warning system based on EEG data.Principal SupervisorDoctoral2024
Size-efficient Big Data reduction and storage: methods and applicationAssociate SupervisorDoctoral2023
Data Mining From Big Data SourcesAssociate SupervisorDoctoral2023
Advanced Machine Learning Models for EEG Signal Analysis in Assessing Depth of AnaesthesiaAssociate SupervisorDoctoral2023
Epileptic Seizure Prediction Based on ElectroencephalographyAssociate SupervisorDoctoral2020

A novel epileptic seizure prediction method based on synchroextracting transform and 1-dimensional convolutional neural network

Ra, Jee Sook, Li, Tianning and Li, Yan. 2023. "A novel epileptic seizure prediction method based on synchroextracting transform and 1-dimensional convolutional neural network." Computer Methods and Programs in Biomedicine. 240. https://doi.org/https://doi.org/10.1016/j.cmpb.2023.107678

A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG

Tawhid, Md. Nurul Ahad, Siuly, Siuly and Li, Tianning. 2022. "A Convolutional Long Short-Term Memory-Based Neural Network for Epilepsy Detection From EEG." IEEE Transactions on Instrumentation and Measurement. 71, pp. 1-11. https://doi.org/10.1109/TIM.2022.3217515

A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia

Schmierer, Thomas, Li, Tianning and Li, Yan. 2022. "A novel empirical wavelet SODP and spectral entropy based index for assessing the depth of anaesthesia." Health Information Science and Systems. 10 (1), pp. 1-14. https://doi.org/10.1007/s13755-022-00178-8

A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

Ra, Jee S., Li, Tianning and Li, Yan. 2021. "A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction." Sensors. 21 (23). https://doi.org/10.3390/s21237972

A novel spectral entropy-based index for assessing the depth of anaesthesia

Ra, Jee Sook, Li, Tianning and Li, Yan. 2021. "A novel spectral entropy-based index for assessing the depth of anaesthesia." Brain Informatics. 8 (1). https://doi.org/10.1186/s40708-021-00130-8

Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques

Li, Tianning and Wen, Peng. 2017. "Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques." IET Signal Processing. 11 (2), pp. 221-227. https://doi.org/10.1049/iet-spr.2015.0114

Anesthesia assessment based on ICA permutation entropy analysis of two-channel EEG signals

Li, Tianning, Sivakumar, Prashanth and Tao, Xiaohui. 2019. "Anesthesia assessment based on ICA permutation entropy analysis of two-channel EEG signals." Liang, Peipeng, Goel, Vinod and Shan, Chunlei (ed.) 12th International Conference on Brain Informatics (BI 2019). Haikou, China 13 - 15 Dec 2019 Switzerland. Springer. https://doi.org/10.1007/978-3-030-37078-7_24

Complex networks approach for depth of anesthesia assessment

Diykh, Mohammed, Li, Yan, Wen, Peng and Li, Tianning. 2018. "Complex networks approach for depth of anesthesia assessment." Measurement. 119, pp. 178-189. https://doi.org/10.1016/j.measurement.2018.01.024

Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band

Li, Tianning and Wen, Peng. 2016. "Depth of anaesthesia assessment based on adult electroencephalograph beta frequency band." Physical and Engineering Sciences in Medicine. 39 (3), pp. 773-781. https://doi.org/10.1007/s13246-016-0459-5

Depth of anaesthesia monitors and the latest algorithms

Li, Tianning and Li, Yan. 2014. "Depth of anaesthesia monitors and the latest algorithms." Asian Pacific Journal of Tropical Medicine. 7 (6), pp. 429-437. https://doi.org/10.1016/S1995-7645(14)60070-5

Depth of anaesthesia assessment based on time and frequency features of simplified electroencephalogram (EEG)

Li, Tianning. 2015. Depth of anaesthesia assessment based on time and frequency features of simplified electroencephalogram (EEG). PhD Thesis Doctor of Philosophy. University of Southern Queensland.

Anaesthetic EEG signal denoise using improved nonlocal mean methods

Li, Tianning, Wen, Peng and Jayamaha, Sophie. 2014. "Anaesthetic EEG signal denoise using improved nonlocal mean methods." Physical and Engineering Sciences in Medicine. 37 (2), pp. 431-437. https://doi.org/10.1007/s13246-014-0263-z

Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features

Li, Tianning, Huang, Yi, Wen, Paul and Li, Yan. 2024. "Accurate depth of anesthesia monitoring based on EEG signal complexity and frequency features." Brain Informatics. 11. https://doi.org/10.1186/s40708-024-00241-y

EventsVista: Enhancing Event Visualization and Interpretation

Farhat, Mohamad Khalil, Zhang, Ji, Tao, Xiaohui, Li, Tianning and Yu, T.. 2024. "EventsVista: Enhancing Event Visualization and Interpretation." 2023 10th International Conference on Behavioural and Social Computing (BESC). Larnaca, Cyprus 30 Oct - 01 Nov 2023 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/BESC59560.2023.10386648

Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment

Schmierer, Thomas, Li, Tianning and Li, Yan. 2024. "Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment." Artificial Intelligence in Medicine. 151. https://doi.org/10.1016/j.artmed.2024.102869