Multilevel Laser-Induced Pain Measurement with Wasserstein Generative Adversarial Network - Gradient Penalty Model.

Pain is an experience of unpleasant sensations and emotions associated with actual or potential tissue damage. In the global context, billions of people are affected by pain disorders. There are particular challenges in the measurement and assessment of pain, and the commonly used pain measuring tools include traditional subjective scoring methods and biomarker-based measures. The main tools for biomarker-based analysis are electroencephalography (EEG), electrocardiography and functional magnetic resonance. The EEG-based quantitative pain measurements are of immense value in clinical pain management and can provide objective assessments of pain intensity. The assessment of pain is now primarily limited to the identification of the presence or absence of pain, with less research on multilevel pain. High power laser stimulation pain experimental paradigm and five pain level classification methods based on EEG data augmentation are presented. First, the EEG features are extracted using modified S-transform, and the time-frequency information of the features is retained. Based on the pain recognition effect, the 20-40[Formula: see text]Hz frequency band features are optimized. Afterwards the Wasserstein generative adversarial network with gradient penalty is used for feature data augmentation. It can be inferred from the good classification performance of features in the parietal region of the brain that the sensory function of the parietal lobe region is effectively activated during the occurrence of pain. By comparing the latest data augmentation methods and classification algorithms, the proposed method has significant advantages for the five-level pain dataset. This research provides new ways of thinking and research methods related to pain recognition, which is essential for the study of neural mechanisms and regulatory mechanisms of pain.

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

One-Dimensional Local Binary Pattern and Common Spatial Pattern Feature Fusion Brain Network for Central Neuropathic Pain.

Central neuropathic pain (CNP) after spinal cord injury (SCI) is related to the plasticity of cerebral cortex. The plasticity of cortex recorded by electroencephalogram (EEG) signal can be used as a biomarker of CNP. To analyze changes in the brain network mechanism under the combined effect of injury and pain or under the effect of pain, this paper mainly studies the changes of brain network functional connectivity in patients with neuropathic pain and without neuropathic pain after SCI. This paper has recorded the EEG with the CNP group after SCI, without the CNP group after SCI, and a healthy control group. Phase-locking value has been used to construct brain network topological connectivity maps. By comparing the brain networks of the two groups of SCI with the healthy group, it has been found that in the [Formula: see text] and [Formula: see text] frequency bands, the injury increases the functional connectivity between the frontal lobe and occipital lobes, temporal, and parietal of the patients. Furthermore, the comparison of brain networks between the group with CNP and the group without CNP after SCI has found that pain has a greater effect on the increased connectivity within the patients' frontal lobes. Motor imagery (MI) data of CNP patients have been used to extract one-dimensional local binary pattern (1D-LBP) and common spatial pattern (CSP) features, the left and right hand movements of the patients' MI have been classified. The proposed LBP-CSP feature method has achieved the highest accuracy of 98.6% and the average accuracy of 91.5%. The results of this study have great clinical significance for the neural rehabilitation and brain-computer interface of CNP patients.

Exploration of sleep function connection and classification strategies based on sub-period sleep stages.

Background: As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. Methods: Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages. Results: The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%. Conclusion: The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.

Total Laparoscopic Hysterectomy in Patients with Large Uteri: Comparison of Uterine Removal by Transvaginal and Uterine Morcellation Approaches.

The aim of this study was to compare the clinical results of total laparoscopic hysterectomy (TLH) for large uterus with uterus size of 12 gestational weeks (g.w.) or greater through transvaginal or uterine morcellation approaches. We retrospectively collected the clinical data of those undergoing total laparoscopic hysterectomies between January 2004 and June 2012. Intraoperative and postoperative outcomes were compared between patients whose large uterus was removed through transvaginal or morcellation approaches. The morcellation group has significantly shorter mean operation time and uterus removal time and smaller incidence of intraoperative complications than the transvaginal group (all P < 0.05). No statistical significant difference regarding the mean blood loss, uterine weight, and length of hospital stay was noted in the morcellation and transvaginal groups (all P > 0.05). In two groups, there was one patient in each group who underwent conversion to laparotomy due to huge uterus size. With regard to postoperative complications, there was no statistical significant difference regarding the frequencies of pelvic hematoma, vaginal stump infection, and lower limb venous thrombosis in two groups (all P > 0.05). TLH through uterine morcellation can reduce the operation time, uterus removal time, and the intraoperative complications and provide comparable postoperative outcomes compared to that through the transvaginal approaches.

Application of laparoscopy in the combined surgical procedures of gynecological and digestive disorders in obese women: a retrospective cohort study.

BACKGROUND: Laparoscopy surgery has been widely used for many decades and combined laparoscopic procedures have become favorable choices for concomitant pathologies in the abdomen. However, the type of combination procedures and their safety in obese women have not been well elucidated in obese women. METHODS: Here we retrospectively reported 147 obese women underwent combined laparoscopic gynecological surgery and cholecystectomy/appendicectomy in our hospital from January 2003 to December 2011. Of the total number of patients (n = 147), various laparoscopic gynecological surgeries were combined with laparoscopic cholecystectomy in 93 patients, and were combined with laparoscopic appendectomy in the rest 54 patients. Patients' ages ranged from 24 to 55 years with an average of 33 years. RESULTS: Our results showed that combined procedures caused various operative time and blood loss, with no difference considering the time to resume oral intake and length of hospital stay. Intraoperative complications occurred in a total of 7 patients (4.8%). None of the patients suffered from major complications after laparoscopic surgery, and minor postoperative complications occurred in 30 patients (20.4%). The follow-up period ranged from 6 to 24 months (average, 18.5 months). None of the patients developed complications during follow-up, except that one patient suffered from colporrhagia. CONCLUSIONS: Our results further suggest that the combined abdominal laparoscopic procedures of gynecologic and general surgery are safe and economic choices for obese women, and benefit patients in many ways including lesser pain, shorter hospital stays and earlier recovery.