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Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant's movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children's Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures. [ABSTRACT FROM AUTHOR]

Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying such EEG patterns is critical to the diagnosis of ESES. While most of the existing automatic ESES quantification systems ignore the morphological variations of the signal as well as the individual variability among subjects. To address these issues, this paper presents a hybrid expert system that dedicates to mimicking the decision-making process of clinicians in ESES quantification by taking the morphological variations, individual variability, and medical knowledge into consideration. The proposed hybrid system not only offers a general scheme that could propel a semi-auto morphology analysis-based expert decision model to a fully automated ESES quantification with biogeography-based optimization (BBO), but also proposes a more precise individualized quantification system to involve the personalized characteristics by adopting an individualized parameters-selection framework. The feasibility and reliability of the proposed method are evaluated on a clinical dataset collected from twenty subjects at Children’s Hospital of Fudan University, Shanghai, China. The estimation error for the individualized quantitative descriptor ESES is 0-4.32% and the average estimation error is 0.95% for all subjects. Experimental results show the presented system outperforms existing works and the individualized system significantly improves the performance of ESES quantification. The favorable results indicate that the proposed hybrid expert system for automatic ESES quantification is promising to support the diagnosis of ESES. [ABSTRACT FROM AUTHOR]

Background: The surgery for esophageal cancer arising after prior gastrectomy is technically difficult with high morbidity and mortality. Endoscopic submucosal dissection (ESD) is a minimally invasive endoscopic treatment for superficial SCC with high curative resection rate. But few studies are concerned about ESD under these circumstances. The aim of this study was to elucidate the short- and long-term outcomes of ESD for superficial esophageal squamous cell cancer (SCC) in patients with prior gastrectomy.Methods: From January 2009 to January 2019, 37 patients with prior gastrectomy who underwent ESD for superficial esophageal SCC were retrospectively enrolled at the Zhongshan Hospital, Fudan University in Shanghai, China. Rates of en bloc resection, complete resection, curative resection, incidence of postoperative bleeding, perforation and postoperative stricture were evaluated as short-term outcomes. Overall survival, and local recurrence-free survival were evaluated as long-term outcomes.Results: The rate of en bloc resection, complete resection and curative resection were 94.6%, 86.5% and 78.4%, respectively. No perforation was observed. 1 (2.7%) patient experienced postoperative bleeding. During the median observation of 43 months, 3 (8.6%) patients experienced esophageal stricture, successfully managed by balloon dilation. 3(8.6%) patients had local recurrence after ESD with 5-year local recurrence-free survival rate of 91.4%. During the observation period, 4 patients died of other reasons. The 1, 3, 5-year overall survival rates were 97.1%, 97.1% and 91.4%.Conclusions: The short-term outcomes indicate ESD is technically difficult with lower resection completeness in patients after gastrectomy, while the long-term outcomes are rather favorable. [ABSTRACT FROM AUTHOR]

Sleep is a natural phenomenon controlled by the central nervous system. The sleep-wake pattern, which functions as an essential indicator of neurophysiological organization in the neonatal period, has profound meaning in the prediction of cognitive diseases and brain maturity. In recent years, unobtrusive sleep monitoring and automatic sleep staging have been intensively studied for adults, but much less for neonates. This work aims to investigate a novel video-based unobtrusive method for neonatal sleep-wake classification by analyzing the behavioral changes in the neonatal facial region. A hybrid model is proposed to monitor the sleep-wake patterns of human neonates. The model combines two algorithms: deep convolutional neural network (DCNN) and support vector machine (SVM), where DCNN works as a trainable feature extractor and SVM as a classifier. Data was collected from nineteen Chinese neonates at the Children's Hospital of Fudan University, Shanghai, China. The classification results are compared with the gold standard of video-electroencephalography scored by pediatric neurologists. Validations indicate that the proposed hybrid DCNN-SVM model achieved reliable performances in classifying neonatal sleep and wake states in RGB video frames (with the face region detected), with an accuracy of 93.8 ± 2.2% and an F1-score 0.93 ± 0.3. [ABSTRACT FROM AUTHOR]