Research into the formation of a soccer curriculum in early childhood education Through the action research with students belonging to IPU’s soccer club and nursery school children

This six-month research examined the influence of playing soccer on the mental and physical development of 5 year old nursery school children under the corporation of students from IPU’s soccer club. Our objectives were to observe: 1） how soccer is a stimulative for young children as a content of early childhood education, 2） what kinds of effects children gain from a developmental point of view. 3） the possibility developing a new area of the early childhood education and care. Our conclusions suggest the following findings: 1）soccer is effective for improving children\u27s health, motor skills and the formation of human relations ability, 2）the most important coaching method for young children is child-oriented［without strong teacher intervention eacherintervention］as children develop their ability in small group, 3）it provides numerous chances to understand each child’s personality more deeply through such activities

Automated prediction of sudden cardiac death using statistically extracted features from electrocardiogram signals

Sudden cardiac death (SCD) is becoming a severe problem despite significant advancements in the usage of the information and communication technology (ICT) in the health industry. Predicting an unexpected SCD of a person is of high importance. It might increase the survival rate. In this work, we have developed an automated method for predicting SCD utilizing statistical measures. We extracted the intrinsic attributes of the electrocardiogram (ECG) signals using Hilbert-Huang and wavelet transforms. Then utilizing machine learning (ML) classifier, we are using these traits to automatically classify regular and SCD existing risks. Support vector machine (SVM), decision tree (DT), naive Bayes (NB), discriminate k-nearest neighbors (KNN), analysis (Disc.), as well as an ensemble of classifiers also utilized (Ens.). The efficiency and practicality of the proposed methods are evaluated using a standard database and measured ECG data obtained from 18 ECG records of SCD cases and 18 ECG records of normal cases. For the automated scheme, the set of features can predict SCD very fast that is, half an hour before the occurrence of SCD with an average accuracy of 100.0% (KNN), 99.9% (SVM), 98.5% (NB), 99.4% (DT), 99.5% (Disc.), and 100.0% (Ens.

Classification of health deterioration by geometric invariants

The authors are grateful to the Operational Programme "Development of the Internal Grant Agency of the University of Hradec Kralove", reg. no. CZ.02.2.69/0.0/0.0/19_073/0016949, project no. IGRA-TYM-2021008 (investigators: Damian Busovsky and Katerina Voglova) .This study was also possible thanks to the project TP01010032 "The Centre of Creative Activities and Knowledge Transfer at University Hradec Kralove." This project was co -financed by the state budget of the Technology Agency of the Czech Republic under the GAMA 2 Progamme.Furthermore, the authors are grateful to the Excellence project PrF UHK 2215/2023-2024 for its financial support.Background and Objectives: Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress.Methods: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL).Results: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51Conclusions: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.Operational Programme "Development of the Internal Grant Agency of the University of Hradec Kralove" CZ.02.2.69/0.0/0.0/19_073/0016949, IGRA-TYM-2021008Centre of Creative Activities and Knowledge Transfer at Uni- versity Hradec KraloveState budget of the Technology Agency of the Czech RepublicCentre of Creative Activities and Knowledge Transfer at University Hradec KraloveExcellence project PrF UHKTP01010032, 2215/2023-202

Three-dimensional Phase Space Characteristics of Electrocardiogram Segments in Online and Early Prediction of Sudden Cardiac Death

Introduction: Predicting sudden cardiac death (SCD) using electrocardiogram (ECG) signals has come to the attention of researchers in recent years. One of the most common SCD identifiers is ventricular fibrillation (VF). The main objective of the present study was to provide an online prediction system of SCD using innovative ECG measures 10 minutes before VF onset. Additionally, it aimed to evaluate the different segments of the ECG signal (which depend on ventricular function) comparatively to determine the efficient component in predicting SCD. The ECG segments were QS, RT, QR, QT, and ST.Material and Methods: After defining the ECG characteristic points and segments, innovative measures were appraised using the three-dimensional phase space of the ECG component. Tracking signal dynamics and lowering the computational cost make the feature suitable for online and offline applications. Finally, the prediction was performed using the support vector machine (SVM).Results: Using the QR measures, SCD detection was realized ten minutes before its occurrence with an accuracy, specificity, and sensitivity of 100%.Conclusion: The superiority of the proposed system compared to the state-of-the-art SCD prediction schemes was revealed in terms of both classification performances and computational speed

Prediction of Sudden Cardiac Death Using Ensemble Classifiers

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised machine learning methods have been successfully used for medical diagnosis. Ensemble classifiers are known to achieve better prediction accuracy than its constituent base classifiers. In an effort to understand the factors contributing to SCD, data on 2,521 patients were collected for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). The data included 96 features that were gathered over a period of 5 years. The goal of this dissertation was to develop a model that could accurately predict SCD based on available features. The prediction model used the Cox proportional hazards model as a score and then used the ExtraTreesClassifier algorithm as a boosting mechanism to create the ensemble. We tested the system at prediction points of 180 days and 365 days. Our best results were at 180-days with accuracy of 0.9624, specificity of 0.9915, and F1 score of 0.9607