arXiv subjects

Weitao Tang

Publications and source records attributed to Weitao Tang.

4 recordsLinked to original sources

arXiv2025

FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classification

Introduction: This study presents FetalSleepNet, the first published deep learning approach to classifying sleep states from the ovine electroencephalogram (EEG). Fetal EEG is complex to acquire and difficult and laborious to interpret consistently. However, accurate sleep stage classification may aid in the early detection of abnormal brain maturation associated with pregnancy complications (e.g. hypoxia or intrauterine growth restriction). Methods: EEG electrodes were secured onto the ovine dura over the parietal cortices of 24 late gestation fetal sheep. A lightweight deep neural network originally developed for adult EEG sleep staging was trained on the ovine EEG using transfer learning from adult EEG. A spectral equalisation-based domain adaptation strategy was used to reduce cross-domain mismatch. Results: We demonstrated that while direct transfer performed poorly, full fine tuning combined with spectral equalisation achieved the best overall performance (accuracy: 86.6 percent, macro F1-score: 62.5), outperforming baseline models. Conclusions: To the best of our knowledge, FetalSleepNet is the first deep learning framework specifically developed for automated sleep staging from the fetal EEG. Beyond the laboratory, the EEG-based sleep stage classifier functions as a label engine, enabling large scale weak/semi supervised labeling and distillation to facilitate training on less invasive signals that can be acquired in the clinic, such as Doppler Ultrasound or electrocardiogram data. FetalSleepNet's lightweight design makes it well suited for deployment in low power, real time, and wearable fetal monitoring systems.

eess.SP↗

arXiv2025

Fetal Sleep: A Cross-Species Review of Physiology, Measurement, and Classification

Study Objectives: Fetal sleep is a vital yet underexplored aspect of prenatal neurodevelopment. Its cyclic organization reflects the maturation of central neural circuits, and disturbances in these patterns may offer some of the earliest detectable signs of neurological compromise. This is the first review to integrate more than seven decades of research into a unified, cross-species synthesis of fetal sleep. We examine: (i) Physiology and Ontogeny-comparing human fetuses with animal models; and (ii) Methodological Evolution-transitioning from invasive neurophysiology to non-invasive monitoring and deep learning frameworks. Methods: A structured narrative synthesis was guided by a systematic literature search across four databases (PubMed, Scopus, IEEE Xplore, and Google Scholar). From 2,925 identified records, 171 studies involving fetal sleep-related physiology, sleep-state classification, or signal-based monitoring were included in this review. Results: Across the 171 studies, fetal sleep states become clearly observable as the brain matures. In fetal sheep and baboons, organized cycling between active and quiet sleep emerges at approximately 80%-90% gestation. In humans, this differentiation occurs later, around 95% gestation, with full maturation reached near term. Despite extensive animal research, no unified, clinically validated framework exists for defining fetal sleep states, limiting translation into routine obstetric practice. Conclusions: By integrating evidence across species, methodologies, and clinical contexts, this review provides the scientific foundation for developing objective, multimodal, and non-invasive fetal sleep monitoring technologies-tools that may ultimately support earlier detection of neurological compromise and guide timely prenatal intervention.

q-bio.NC↗

arXiv2024

Fair Knowledge Tracing in Second Language Acquisition

In second-language acquisition, predictive modeling aids educators in implementing diverse teaching strategies, attracting significant research attention. However, while model accuracy is widely explored, model fairness remains under-examined. Model fairness ensures equitable treatment of groups, preventing unintentional biases based on attributes such as gender, ethnicity, or economic background. A fair model should produce impartial outcomes that do not systematically disadvantage any group. This study evaluates the fairness of two predictive models using the Duolingo dataset's en\_es (English learners speaking Spanish), es\_en (Spanish learners speaking English), and fr\_en (French learners speaking English) tracks. We analyze: 1. Algorithmic fairness across platforms (iOS, Android, Web). 2. Algorithmic fairness between developed and developing countries. Key findings include: 1. Deep learning outperforms machine learning in second-language knowledge tracing due to improved accuracy and fairness. 2. Both models favor mobile users over non-mobile users. 3. Machine learning exhibits stronger bias against developing countries compared to deep learning. 4. Deep learning strikes a better balance of fairness and accuracy in the en\_es and es\_en tracks, while machine learning is more suitable for fr\_en. This study highlights the importance of addressing fairness in predictive models to ensure equitable educational strategies across platforms and regions.

cs.HC↗

arXiv2019

Object Detection in Specific Traffic Scenes using YOLOv2

object detection framework plays crucial role in autonomous driving. In this paper, we introduce the real-time object detection framework called You Only Look Once (YOLOv1) and the related improvements of YOLOv2. We further explore the capability of YOLOv2 by implementing its pre-trained model to do the object detecting tasks in some specific traffic scenes. The four artificially designed traffic scenes include single-car, single-person, frontperson-rearcar and frontcar-rearperson.

cs.CV↗