By covering seven pieces of academic research,the author concludes seven factors which influence infants’language learning:touch influences how infants learn language;enhancing transitions from declarative to procedu...By covering seven pieces of academic research,the author concludes seven factors which influence infants’language learning:touch influences how infants learn language;enhancing transitions from declarative to procedural performance accelerates learning;musical rhythm discrimination influences children’s grammar skills;verbal positional memory influences infants’language learning;expressive vocabulary influences infants’language learning;kid’s oral language skills can predict future writing difficulties;play wires kids’brains for social and academic success.As shown in the above research,infants’language learning can be easily influenced by social environment,which indicate the behaviorist perspective,a theory of learning that explains language acquisition.展开更多
Many preterm infants suffer from neural disorders caused by early birth complications. The detection of children with neurological risk is an important challenge. The electroencephalogram is an important technique for...Many preterm infants suffer from neural disorders caused by early birth complications. The detection of children with neurological risk is an important challenge. The electroencephalogram is an important technique for establishing long-term neurological prognosis. Within this scope, the goal of this study is to propose an automatic detection of abnormal preterm babies’ electroencephalograms (EEG). A corpus of 316 neonatal EEG recordings of 100 infants born after less than 35 weeks of gestation were preprocessed and a time series of standard deviation was computed. This time series was thresholded to detect Inter Burst Intervals (IBI). Temporal features were extracted from bursts and IBI. Feature selection was carried out with classification in one step so as to select the best combination of features in terms of classification performance. Two classifiers were tested: Multiple Linear Regressions and Support Vector Machines (SVM). Performance was computed using cross validations. Methods were validated on a corpus of 100 infants with no serious brain damage. The Multiple Linear Regression method shows the best results with a sensitivity of 86.11% ± 10.01%, a specificity of 77.44% ± 7.62% and an AUC (Area under the ROC curves) of 0.82 ± 0.04. An accurate detection of abnormal EEG for preterm infants is feasible. This study is a first step towards an automatic analysis of the premature brain, making it possible to lighten the physician’s workload in the future.展开更多
Infant drowning has occurred frequently in swimming pools recent years,which motivates the research on automatic real-time detection of the accident.Unlike youths or adults,swimming infants are small in terms of size ...Infant drowning has occurred frequently in swimming pools recent years,which motivates the research on automatic real-time detection of the accident.Unlike youths or adults,swimming infants are small in terms of size and motion range,and unable to send out distress signals in emergencies,which exerts negative effects on the detection of drowning.Aiming at this problem,a new step is initialized towards detecting infant drowning automatically and efficiently based on video surveillance.Diverse live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets.A part of the datasets is downscaled or enlarged to enhance generalization ability of the model.On this basis,advantages of Faster R-CNN and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in real-world.Supervised learning experiments are carried out,model test results show that mean Average Precision(mAP)of either Faster R-CNN or YOLOv5s of the series of YOLOv5 can be over 89%;the former can process merely 6 frames of videos per second with the precision of only 62.04%,while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%.The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance,which is of great application value to reduce the accidents in swimming pools.展开更多
目的探讨极低出生体重儿(VLBWI)输血及输血量的影响因素及其预测模型构建。方法选取2017年2月—2021年1月在我院收治的102例VLBWI,采用计算机产生随机数法以3∶1的比例分为训练集(76例)和测试集(26例)。比较训练集中输血组和未输血组患...目的探讨极低出生体重儿(VLBWI)输血及输血量的影响因素及其预测模型构建。方法选取2017年2月—2021年1月在我院收治的102例VLBWI,采用计算机产生随机数法以3∶1的比例分为训练集(76例)和测试集(26例)。比较训练集中输血组和未输血组患儿的一般资料和住院期间疾病及治疗措施,Logistic回归法分析影响VLBWI输血的危险因素,分别采用Logistic回归、CatBoost、XGBoost和Light GBM四种机器学习法构建输血预测模型,比较四个模型的预测效能。使用多元线性逐步回归分析影响VLBWI输血量的独立影响因素,并拟合预测模型。结果出生体重小、胎龄小、生后两周内采血量多、肠外营养时间长及剖宫产是患儿输血的独立危险因素(P<0.05)。Logistic回归、XGBoost、CatBoost、Li g h tGBM模型的AUC分别为0.836(95%CI:0.745~0.889)、0.801(95%CI:0.734~0.862)、0.738(95%CI:0.658~0.800)和0.700(95%CI:0.609~0.785),与测试集结果相比,差异均无统计学意义(P>0.05)。使用步进法进行多元线性回归分析,确定出生体重、胎龄、出生时血红蛋白(Hb)值、出生时红细胞比容(Hct)为VLBWI输血量的独立影响因素,并构建预测模型:VLBWI输血量Y=24.175-0.731×出生体重-0.538×胎龄-0.431×出生时Hb值-0.569×出生时Hct,F=33.321,P<0.001,D-W(德宾-沃森)=1.725,R2=0.671。结论VLBWI输血指征中出生体重、胎龄、生后两周内采血量、肠外营养时间及剖宫产是影响患儿输血的独立危险因素。出生体重、胎龄、出生时Hb值、出生时Hct为VLBWI输血量的独立影响因素。通过Logistic回归、CatBoost、XGBoost和LightGBM四种机器学习法进行预测,发现Logistic曲线的预测效果更加准确。展开更多
文摘By covering seven pieces of academic research,the author concludes seven factors which influence infants’language learning:touch influences how infants learn language;enhancing transitions from declarative to procedural performance accelerates learning;musical rhythm discrimination influences children’s grammar skills;verbal positional memory influences infants’language learning;expressive vocabulary influences infants’language learning;kid’s oral language skills can predict future writing difficulties;play wires kids’brains for social and academic success.As shown in the above research,infants’language learning can be easily influenced by social environment,which indicate the behaviorist perspective,a theory of learning that explains language acquisition.
文摘Many preterm infants suffer from neural disorders caused by early birth complications. The detection of children with neurological risk is an important challenge. The electroencephalogram is an important technique for establishing long-term neurological prognosis. Within this scope, the goal of this study is to propose an automatic detection of abnormal preterm babies’ electroencephalograms (EEG). A corpus of 316 neonatal EEG recordings of 100 infants born after less than 35 weeks of gestation were preprocessed and a time series of standard deviation was computed. This time series was thresholded to detect Inter Burst Intervals (IBI). Temporal features were extracted from bursts and IBI. Feature selection was carried out with classification in one step so as to select the best combination of features in terms of classification performance. Two classifiers were tested: Multiple Linear Regressions and Support Vector Machines (SVM). Performance was computed using cross validations. Methods were validated on a corpus of 100 infants with no serious brain damage. The Multiple Linear Regression method shows the best results with a sensitivity of 86.11% ± 10.01%, a specificity of 77.44% ± 7.62% and an AUC (Area under the ROC curves) of 0.82 ± 0.04. An accurate detection of abnormal EEG for preterm infants is feasible. This study is a first step towards an automatic analysis of the premature brain, making it possible to lighten the physician’s workload in the future.
基金This work was supported by the CAAI-Huawei MindSpore Open Fund and the General Program of Natural Science Foundation of Fujian Province,China(No.2020J01473).
文摘Infant drowning has occurred frequently in swimming pools recent years,which motivates the research on automatic real-time detection of the accident.Unlike youths or adults,swimming infants are small in terms of size and motion range,and unable to send out distress signals in emergencies,which exerts negative effects on the detection of drowning.Aiming at this problem,a new step is initialized towards detecting infant drowning automatically and efficiently based on video surveillance.Diverse live-scene videos of infant swimming and drowning are collected from a variety of natatoriums and labeled as datasets.A part of the datasets is downscaled or enlarged to enhance generalization ability of the model.On this basis,advantages of Faster R-CNN and a series of YOLOv5 models are specifically explored to enable fast and accurate detection of infant drowning in real-world.Supervised learning experiments are carried out,model test results show that mean Average Precision(mAP)of either Faster R-CNN or YOLOv5s of the series of YOLOv5 can be over 89%;the former can process merely 6 frames of videos per second with the precision of only 62.04%,while the latter can reach an average speed of 75 frames/s with the precision of about 86.6%.The YOLOv5s eventually stands out as an optimal model for detecting infant drowning in view of comprehensive performance,which is of great application value to reduce the accidents in swimming pools.
文摘目的探讨极低出生体重儿(VLBWI)输血及输血量的影响因素及其预测模型构建。方法选取2017年2月—2021年1月在我院收治的102例VLBWI,采用计算机产生随机数法以3∶1的比例分为训练集(76例)和测试集(26例)。比较训练集中输血组和未输血组患儿的一般资料和住院期间疾病及治疗措施,Logistic回归法分析影响VLBWI输血的危险因素,分别采用Logistic回归、CatBoost、XGBoost和Light GBM四种机器学习法构建输血预测模型,比较四个模型的预测效能。使用多元线性逐步回归分析影响VLBWI输血量的独立影响因素,并拟合预测模型。结果出生体重小、胎龄小、生后两周内采血量多、肠外营养时间长及剖宫产是患儿输血的独立危险因素(P<0.05)。Logistic回归、XGBoost、CatBoost、Li g h tGBM模型的AUC分别为0.836(95%CI:0.745~0.889)、0.801(95%CI:0.734~0.862)、0.738(95%CI:0.658~0.800)和0.700(95%CI:0.609~0.785),与测试集结果相比,差异均无统计学意义(P>0.05)。使用步进法进行多元线性回归分析,确定出生体重、胎龄、出生时血红蛋白(Hb)值、出生时红细胞比容(Hct)为VLBWI输血量的独立影响因素,并构建预测模型:VLBWI输血量Y=24.175-0.731×出生体重-0.538×胎龄-0.431×出生时Hb值-0.569×出生时Hct,F=33.321,P<0.001,D-W(德宾-沃森)=1.725,R2=0.671。结论VLBWI输血指征中出生体重、胎龄、生后两周内采血量、肠外营养时间及剖宫产是影响患儿输血的独立危险因素。出生体重、胎龄、出生时Hb值、出生时Hct为VLBWI输血量的独立影响因素。通过Logistic回归、CatBoost、XGBoost和LightGBM四种机器学习法进行预测,发现Logistic曲线的预测效果更加准确。
