Annotated electroencephalogram (EEG) data is the prerequisite for artificial intelligence-driven EEG autoanalysis. However, the scarcity of annotated data due to its high-cost and the resulted insufficient training limits the development of EEG autoanalysis. Generative self-supervised learning, represented by masked autoencoder, offers potential but struggles with non-Euclidean structures. To alleviate these challenges, this work proposes a self-supervised graph masked autoencoder for EEG representation learning, named GMAEEG. Concretely, a pretrained model is enriched with temporal and spatial representations through a masked signal reconstruction pretext task. A learnable dynamic adjacency matrix, initialized with prior knowledge, adapts to brain characteristics. Downstream tasks are achieved by finetuning pretrained parameters, with the adjacency matrix transferred based on task functional similarity. Experimental results demonstrate that with emotion recognition as the pretext task, GMAEEG reaches superior performance on various downstream tasks, including emotion, major depressive disorder, Parkinson's disease, and pain recognition. This study is the first to tailor the masked autoencoder specifically for EEG representation learning considering its non-Euclidean characteristics. Further, graph connection analysis based on GMAEEG may provide insights for future clinical studies.

Background: Nosocomial infection with multidrug resistant organisms (MDRO) can directly influence the curative effect and the prognosis of neonates, bringing great difficulties to clinical treatment. As direct contacts of neonates, the knowledge, attitudes, and practices (KAP) of doctors and nurses are critical for the prevention and control of MDRO infection in neonatal intensive care units (NICUs). Purpose: This study describes the KAP of doctors and nurses in NICUs toward the prevention and control of nosocomial infection with MDRO and analyzes its influencing factors. Methods: This cross-sectional study used convenience sampling to survey 397 doctors and nurses from the NICU of 28 hospitals in 11 cities in Zhejiang Province, China. A univariate analysis and a multiple linear regression were used to analyze the data. Results: The median scoring rate (interquartile range) of the knowledge, attitude, and practice questionnaire was 0.86 (0.82–0.91), 0.98 (0.91–1.00), and 0.995 (0.97–1.00), respectively. However, the median scoring rate regarding “basic knowledge of MDRO” and “special prevention and control measures” was 0.50 in knowledge. The multiple linear regression showed that the influencing factors of KAP were hospital grade, professional title, gender, regular supervision, and training. Conclusions: There was still room for improvement in the KAP of NICU doctors and nurses, especially regarding the knowledge. Men, doctors and nurses in Grade II hospitals, and doctors and nurses with primary professional titles had worse KAP. Training and supervision helped improve the KAP. Relevance to Clinical Practice: To improve the KAP of doctors and nurses to enhance the prevention and control effect for the MDRO infection in NICUs, hospitals and departments should carry out targeted training and strengthen supervision, while Grade II hospitals, men, and doctors and nurses with primary professional titles need more attention.

Background To evaluate the procedural pain experienced by neonates in a neonatal intensive care unit (NICU) setting and determine the corresponding pain grades. Methods Two experienced nurses independently used the Neonatal Infant Pain Scale (NIPS) to evaluate the neonatal pain during procedures taking place in the tertiary NICU and two level-two neonatal care units in the Children’s Hospital of Zhejiang University School of Medicine. The mean and distribution of NIPS pain scores and the corresponding pain grades of participants when experiencing clinical painful procedures were analysed. Results A total of 957 neonates exposed to 15 common clinical painful procedures were included in the study. The clinical painful procedures experienced by 957 participants could be divided into three groups: severe pain (NIPS score 5–7: peripheral intravenous cannulation, arterial catheterisation, arterial blood sampling, peripherally inserted central catheter placement and nasopharyngeal suctioning), mild to moderate pain (NIPS score 3–4: finger prick, intramuscular injection, adhesive removal, endotracheal intubation suctioning, heel prick, lumbar puncture and subcutaneous injection) and no pain to mild pain (NIPS score 0–2: gastric tube insertion, enema and intravenous injection). Conclusions The neonatal pain response to clinical procedures in NICU had certain pattern and preintervention drug analgesia could be taken for painful procedures with clustered high NIPS pain scores. Meanwhile, full coverage non-drug pain relief measures could be taken for procedures that are with scattered pain scores, and real-time pain evaluation should be provided to determine whether further drug analgesia is required.

Objective: Common pain assessment approaches such as self-evaluation and observation scales are inappropriate for children as they require patients to have reasonable communication ability. Subjective, inconsistent, and discontinuous pain assessment in children may reduce therapeutic effectiveness and thus affect their later life. Methods: To address the need for suitable assessment measures, this paper proposes a spatiotemporal deep learning framework for scalp electroencephalogram (EEG)-based automated pain assessment in children. The dataset comprises scalp EEG data recorded from 33 pediatric patients with an arterial puncture as a pain stimulus. Two electrode reduction plans in line with clinical findings are proposed. Combining three-dimensional hand-crafted features and preprocessed raw signals, the proposed transformer-based pain assessment network (STPA-Net) integrates both spatial and temporal information. Results: STPA-Net achieves superior performance with a subject-independent accuracy of 87.83% for pain recognition, and outperforms other state-of-the-art approaches. The effectiveness of electrode combinations is explored to analyze pain-related cortical activities and correspondingly reduce cost. The two proposed electrode reduction plans both demonstrate competitive pain assessment performance qualitatively and quantitatively. Conclusion and significance: This study is the first to develop a scalp EEG-based automated pain assessment for children adopting a method that is objective, standardized, and consistent. The findings provide a potential reference for future clinical research.

Background: Neonatal pain assessment (NPA) represents a huge global problem of essential importance, as a timely and accurate assessment of neonatal pain is indispensable for implementing pain management. Purpose: To investigate the consistency of pain scores derived through video-based NPA (VB-NPA) and on-site NPA (OS-NPA), providing the scientific foundation and feasibility of adopting VB-NPA results in a real-world scenario as the gold standard for neonatal pain in clinical studies and labels for artificial intelligence (AI)-based NPA (AI-NPA) applications. Setting: A total of 598 neonates were recruited from a pediatric hospital in China. Methods: This observational study recorded 598 neonates who underwent one of 10 painful procedures, including arterial blood sampling, heel blood sampling, fingertip blood sampling, intravenous injection, subcutaneous injection, peripheral intravenous cannulation, nasopharyngeal suctioning, retention enema, adhesive removal, and wound dressing. Two experienced nurses performed OS-NPA and VB-NPA at a 10-day interval through double-blind scoring using the Neonatal Infant Pain Scale to evaluate the pain level of the neonates. Intra-rater and inter-rater reliability were calculated and analyzed, and a paired samples t-test was used to explore the bias and consistency of the assessors’ pain scores derived through OS-NPA and VB-NPA. The impact of different label sources was evaluated using three state-of-the-art AI methods trained with labels given by OS-NPA and VB-NPA, respectively. Results: The intra-rater reliability of the same assessor was 0.976–0.983 across different times, as measured by the intraclass correlation coefficient. The inter-rater reliability was 0.983 for single measures and 0.992 for average measures. No significant differences were observed between the OS-NPA scores and the assessment of an independent VB-NPA assessor. The different label sources only caused a limited accuracy loss of 0.022–0.044 for the three AI methods. Conclusion: VB-NPA in a real-world scenario is an effective way to assess neonatal pain due to its high intra-rater and inter-rater reliability compared to OS-NPA and could be used for the labeling of large-scale NPA video databases for clinical studies and AI training.

Objective : Neonatal pain can have long-term adverse effects on newborns' cognitive and neurological development. Video-based N eonatal P ain A ssessment (NPA) method has gained increasing attention due to its performance and practicality. However, existing methods focus on assessment under controlled environments while ignoring real-life disturbances present in uncontrolled conditions. Methods : We propose a video-based NPA method, which is robust to four real-life disturbances and adaptively highlights keyframes. Our method involves a region-channel-attention module for extracting facial features under the disturbances of facial occlusion and pose variation; a body language analysis module robust to disturbances from body occlusion and movement interference, which utilizes skeleton sequences to represent the neonate's body; and a keyframes-aware convolution to get rid of information located at non-contributing moments. For evaluation, we built an NPA video dataset of 1091 neonates with disturbance annotations. Results : The results show that our method consistently outperforms state-of-the-art methods on the full dataset and nine subsets, where it achieves an accuracy of 91.04% on the full dataset with an accuracy increment of 6.27%. Contributions : We present the problem of video-based NPA under uncontrolled conditions, propose a method robust to four disturbances, and construct a video NPA dataset, thus facilitating the practical applications of NPA.