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.

Heart Rate Variability: A possible machine learning biomarker for mechanical circulatory device complications and heart recovery

Cardiovascular disease continues to be the number one cause of death in the United States, with heart failure patients expected to increase to \u3e8 million by 2030. Mechanical circulatory support (MCS) devices are now better able to manage acute and chronic heart failure refractory to medical therapy, both as bridge to transplant or as bridge to destination. Despite significant advances in MCS device design and surgical implantation technique, it remains difficult to predict response to device therapy. Heart rate variability (HRV), measuring the variation in time interval between adjacent heartbeats, is an objective device diagnostic regularly recorded by various MCS devices that has been shown to have significant prognostic value for both sudden cardiac death as well as all-cause mortality in congestive heart failure (CHF) patients. Limited studies have examined HRV indices as promising risk factors and predictors of complication and recovery from left ventricular assist device therapy in end-stage CHF patients. If paired with new advances in machine learning utilization in medicine, HRV represents a potential dynamic biomarker for monitoring and predicting patient status as more patients enter the mechanotrope era of MCS devices for destination therapy

T-Wave Morphology Restitution Predicts Sudden Cardiac Death in Patients With Chronic Heart Failure

BACKGROUND: Patients with chronic heart failure are at high risk of sudden cardiac death (SCD). Increased dispersion of repolarization restitution has been associated with SCD, and we hypothesize that this should be reflected in the morphology of the T-wave and its variations with heart rate. The aim of this study is to propose an electrocardiogram (ECG)-based index characterizing T-wave morphology restitution (TMR), and to assess its association with SCD risk in a population of chronic heart failure patients. METHODS AND RESULTS: Holter ECGs from 651 ambulatory patients with chronic heart failure from the MUSIC (MUerte Súbita en Insuficiencia Cardiaca) study were available for the analysis. TMR was quantified by measuring the morphological variation of the T-wave per RR increment using time-warping metrics, and its predictive power was compared to that of clinical variables such as the left ventricular ejection fraction and other ECG-derived indices, such as T-wave alternans and heart rate variability. TMR was significantly higher in SCD victims than in the rest of patients (median 0.046 versus 0.039, P<0.001). When TMR was dichotomized at TMR=0.040, the SCD rate was significantly higher in the TMR≥0.040 group (P<0.001). Cox analysis revealed that TMR≥0.040 was strongly associated with SCD, with a hazard ratio of 3.27 (P<0.001), independently of clinical and ECG-derived variables. No association was found between TMR and pump failure death. CONCLUSIONS: This study shows that TMR is specifically associated with SCD in a population of chronic heart failure patients, and it is a better predictor than clinical and ECG-derived variables

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

Prediction of ventricular fibrillation using support vector machine

Sudden cardiac death (SCD) remains one of the top causes of high mortality rate. Early prediction of ventricular fibrillation (VF), and hence SCD, can improve the survival chance of a patient by enabling earlier treatment. Heart rate variability analysis (HRV) has been widely adopted by the researchers in VF prediction. Different combinations of features from multiple domains were explored but the spectral analysis was performed without the required preprocessing or on a shorter segment as opposed to the standards of The European and North American Task force on HRV. Thus, our study aimed to develop a robust prediction algorithm by including only time domain and nonlinear features while maintaining the prediction resolution of one minute. Nine time domain features and seven nonlinear features were extracted and classified using support vector machine (SVM) of different kernels. High accuracy of 94.7% and sensitivity of 100% were achieved using extraction of only two HRV features and Gaussian kernel SVM without complicated preprocessing of HRV signals. This algorithm with high accuracy and low computational burden is beneficial for embedded system and real-time application which could help alert the individuals sooner and hence improving patient survival chance

Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Aim To develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF). Methods Thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay “τ” in the original time-series X(t), which produces the Y(t) = X(t − τ). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (μ), standard deviation (σ) and coefficient of variation (CV = σ/μ), kurtosis (β) for the box counting of PSR diagrams were reported. Results During SR, CV was always 0.05. A similar pattern was observed with β, where < 6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CVth = 0.05 and βth < 6. For optimisation of the accuracy, a new index (J) was proposed: J=wCVCVth+1−wββth. During SR the upper limit of J was the value of 1. Furthermore CV, β and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4 min 31 s; SD: 2 min 30 s); allowing sufficient time for preventive therapy. Conclusion The J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias

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

Prompt and accurate diagnosis of ventricular arrhythmias with a novel index based on phase space reconstruction of ECG

Aim: to develop a statistical index based on the phase space reconstruction (PSR) of the electrocardiogram (ECG) for the accurate and timely diagnosis of ventricular tachycardia (VT) and ventricular fibrillation (VF).Methods: thirty-two ECGs with sinus rhythm (SR) and 32 ECGs with VT/VF were analyzed using the PSR technique. Firstly, the method of time delay embedding were employed with the insertion of delay “?” in the original time-series X(t), which produces the Y(t) = X(t ? ?). Afterwards, a PSR diagram was reconstructed by plotting Y(t) against X(t). The method of box counting was applied to analyze the behavior of the PSR trajectories. Measures as mean (?), standard deviation (?) and coefficient of variation (CV = ?/?), kurtosis (?) for the box counting of PSR diagrams were reported.Results: during SR, CV was always &lt; 0.05, while with the onset of arrhythmia CV increased &gt; 0.05. A similar pattern was observed with ? , where &lt; 6 was considered as the cut-off point between SR and VT/VF. Therefore, the upper threshold for SR was considered CV th = 0.05 and ?th &lt; 6. For optimisation of the accuracy, a new index (J ) was proposed: View the MathML sourceJ=wCVCVth+1?w??th.During SR the upper limit of J was the value of 1. Furthermore CV, ? and J crossed the cut-off point timely before the onset of arrhythmia (average time: 4 min 31 s; SD: 2 min 30 s); allowing sufficient time for preventive therapy.Conclusion: the J index improved ECG utility for arrhythmia monitoring and detection utility, allowing the prompt and accurate diagnosis of ventricular arrhythmias