Respiratory diseases morbidity and mortality rate of children between 0-5 age is considerably high in the US, China and East Asian countries. Studies conducted in Mongolia reveals that respiratory diseases morbidity rate of children between 1-4 age is very high in UB. Respiratory diseases case of children aged between 0-5 is predominantly registered in UB. Recent trend suggests that respiratory disease case of children aged between 0-5 shows no sign of decreasing, instead it is increasing. In addition to virus and bacteria, social factors have been one of the main influencers to respiratory diseases morbidity among small children, it was concluded.

Rationale: The most common reasons of childhood deaths is Respiratory Tract Infections and every year, 3 million out of 5 million children under five years of age suffering from respiratory diseases die due to lung inflammation (Stansfield et al, 1993).
In Ulaanbaatar live 182016 children aged 0-4 years and it around 45 percent of the total population in 2018. Ulaanbaatar is ranked first place in the world by air pollution, especially, atmospheric particulate matter (PM2.5) are much higher than the average annual concentrations indication in the recommendations of World Health Organization. This brings high risk of respiratory diseases morbidity and mortality to the children.
It is need to study the last ten-year trend of respiratory diseases which to happen to the children under five years of age, and it should be reduced the respiratory illness of young children. Objective: To study the last ten-year morbidity and mortality trend of respiratory diseases among children aged 0-4 in Ulaanbaatar. Methodology: We have been investigated the trend-survey of the respiratory disease morbidity and mortality of children aged 0-4 in Ulaanbaatar based on the last ten-year statistical data of the Ulaanbaatar Health Department. The population was estimated at 10000 people. Result: Hospital admissions because of respiratory diseases of children under 5-year-old were 3629,9-2009; 3827,3-2010; 3890-2011; 3958,3-2012; 4825,7-2013; 5723,5-2014 4669,4-2015; 5839,3-2016; 5624,4-2017; 4754.9-2018; 3811-2019 respectively, per 10000 people in Ulaanbaatar. The number of hospitalized patients due to respiratory diseases of Ulaanbaatar is increasing gradually from 2009 to 2019.
Respiratory disease mortality was 20,8 in 2008; 21-2009; 20,6-2010; 16,2-2011; 16-2012; 16,4-2013; 17,8-2014; 17,3-2015; 18,2-2016; 15,4-2017; 16.1-2018; 14.1-2019 respectively and mortality rate has been slightly decreased in recent years. Conclusion Although respiratory illness of children aged 0-4 in Ulaanbaatar has been steadily increasing over the past decade, the children mortality has been decreasing in recent years, gradually. Furthermore, it shall be studied of air quality and outdoor indoor air pollution-related diseases among children, detailed.

In this study, we propose a novel method for obstructive sleep apnea (OSA) detection using a piezo-electric sensor. OSA is a relatively common sleep disorder. However, more than 80% of OSA patients remain undiagnosed. We investigated the feasibility of OSA assessment using a single-channel physiological signal to simplify the OSA screening. We detected both snoring and heartbeat information by using a piezo-electric sensor, and snoring index (SI) and features based on pulse rate variability (PRV) analysis were extracted from the filtered piezo-electric sensor signal. A support vector machine (SVM) was used as a classifier to detect OSA events. The performance of the proposed method was evaluated on 45 patients from mild, moderate, and severe OSA groups. The method achieved a mean sensitivity, specificity, and accuracy of 72.5%, 74.2%, and 71.5%; 85.8%, 80.5%, and 80.0%; and 70.3%, 77.1%, and 71.9% for the mild, moderate, and severe groups, respectively. Finally, these results not only show the feasibility of OSA detection using a piezo-electric sensor, but also illustrate its usefulness for monitoring sleep and diagnosing OSA.
Heart Rate ; Humans ; Mass Screening* ; Methods ; Sensitivity and Specificity ; Sleep Apnea, Obstructive* ; Sleep Wake Disorders ; Snoring ; Support Vector Machine

BACKGROUND: In this study, we propose a method for automatically predicting atrial fibrillation (AF) based on convolutional neural network (CNN) using a short-term normal electrocardiogram (ECG) signal. METHODS: We designed a CNN model and optimized it by dropout and normalization. One-dimensional convolution, max-pooling, and fully-connected multiple perceptron were used to analyze the short-term normal ECG. The ECG signal was preprocessed and segmented to train and evaluate the proposed CNN model. The training and test sets consisted of the two AF and one normal dataset from the MIT-BIH database. RESULTS: The proposed CNN model for the automatic prediction of AF achieved a high performance with a sensitivity of 98.6%, a specificity of 98.7%, and an accuracy of 98.7%. CONCLUSION: The results show the possibility of automatically predicting AF based on the CNN model using a short-term normal ECG signal. The proposed CNN model for the automatic prediction of AF can be a helpful tool for the early diagnosis of AF in healthcare fields.
Atrial Fibrillation ; Dataset ; Delivery of Health Care ; Early Diagnosis ; Electrocardiography ; Methods ; Neural Networks (Computer) ; Sensitivity and Specificity

Background: This paper proposes a novel method for automatically identifying sleep apnea (SA) severity based on deep learning from a short-term normal electrocardiography (ECG) signal. Methods: A convolutional neural network (CNN) was used as an identification model and implemented using a one-dimensional convolutional, pooling, and fully connected layer.An optimal architecture is incorporated into the CNN model for the precise identification of SA severity. A total of 144 subjects were studied. The nocturnal single-lead ECG signal was collected, and the short-term normal ECG was extracted from them. The short-term normal ECG was segmented for a duration of 30 seconds and divided into two datasets for training and evaluation. The training set consists of 82,952 segments (66,360 training set, 16,592 validation set) from 117 subjects, while the test set has 20,738 segments from 27 subjects. Results: F1-score of 98.0% was obtained from the test set. Mild and moderate SA can be identified with an accuracy of 99.0%. Conclusion The results showed the possibility of automatically identifying SA severity based on a short-term normal ECG signal.

Purpose: We propose the Lifelog Bigdata Platform as a sustainable digital healthcare system based on individual-centric lifelog datasets and describe the standardization of lifelog and clinical data in its full-cycle management system. Materials and Methods: The Lifelog Bigdata Platform was developed by Yonsei Wonju Health System on the cloud to support digital healthcare and precision medicine. It consists of five core components: data acquisition system, de-identification of individual information, lifelog integration, analyzer, and service. We designed a gathering system into a dedicated virtual machine to save lifelog or clinical outcomes and established standard guidelines for maintaining the quality of gathering procedures. We used standard integration keys to integrate the lifelog and clinical data. Metadata were generated from the data warehouse after loading combined or fragmented data on it. We analyzed the de-identified lifelog and clinical data using the lifelog analyzer to prevent and manage acute and chronic diseases through providing results of statistics on analysis. Results: The big data centers were built in four hospitals and seven companies for integrating lifelog and clinical data to develop the Lifelog Bigdata Platform. We integrated and loaded lifelog big data and clinical data for 3 years. In the first year, we uploaded 94 types of data on the platform with a total capacity of 221 GB. Conclusion The Lifelog Bigdata Platform is the first to combine lifelog and clinical data. The proposed standardization guidelines can be used for future platforms to achieve a virtuous cycle structure of lifelogging big data and an industrial ecosystem.

Background: Class III malocclusion is considered to be one of the most difficult and complex orthodontic problems to treat. For patients whose orthodontic problems are so severe that neither growth modification nor camouflage offers solution, surgery to realign the jaws or reposition dentoalveolar segments is the only possible treatment option left. Aim: The treatment objectives were to: (1) level and align the dental arches; (2) obtain an ideal overbite and overjet, to establish correct anterior guidance; and (3) improve the facial profile. Case: Patient is present with skeletal Class III malocclusion, concave facial profile, facial proportion 0.96:1:1.04, loss of occlusion due to early molar teeth loss, molar and canine relationship were Class III, overbite 0 mm, overjet -1 mm, midline shifted into the 6 mm left side. Results: Excellent facial and occlusal results were achieved with this surgical-orthodontic management, post-treatment results showed a Class I relationship and ideal overjet and overbite. The facial profile became straight type. Conclusion This case report describes the treatment of a female with dental and skeletal class III relationships. Surgical- orthodontic treatment was the best option for achieving an acceptable occlusion and a good esthetic result in this case. An experienced multidisciplinary team approach ensures a satisfactory outcome.