Turbulent Kinetic Energy of Flow During Inhale and Exhale to Characterize the Severity of Obstructive Sleep Apnea Patient

This paper aims to investigate and present the numerical investigation of airflow characteristics using Turbulent Kinetic Energy (TKE) to characterize the upper airway with obstructive sleep apnea (OSA) under inhale and exhale breathing conditions. The importance of TKE under both breathing conditions can show an accurate method in expressing the severity of flow in sleep disorder. Computational fluid dynamics are used to simulate the upper airway's airflow via steady-state Reynolds-averaged Navier-Stokes (RANS) with k–ω shear stress transport (SST) turbulence model. The three-dimensional (3D) airway model is created based on the CT scan images of an actual patient, meshed with 1.29 million elements using Materialise Interactive Medical Image Control System (MIMICS) and ANSYS software, respectively. Both high TKE and Rev were noticed around the region after the necking (smaller cross-sectional area) during the inhale and exhale breathing. The turbulent kinetic energy could be used as a valuable measure to identify the severity of OSA. This study is expected to provide a better understanding and clear visualization of the airflow characteristics during the inhale and exhale breathing in the upper airway of patients for the medical practitioners in the OSA research field.

Patient satisfaction to evaluate the efficacy of mandibular advancement device in a treatment modality for mild to moderate sleep apnea patient in Indore Region

Obstructive sleep apnea (OSA) represents the most severe syndrome associated with obstruction of the upper airway. People with obstructive sleep apnea (OSA) repeatedly stop breathing during their sleep for a moment or longer and as several as many times throughout one night. The Aim of study was to evaluate the efficacy of mandibular advancement devices in different age group, between male and female and according to body mass index in mild to moderate obstructive sleep apnea patient.cross sectional study. 30 patients of different gender and age group were selected with mild to moderate OSA and asked to fill the Berlin questionnaire for diagnosis of obstructive sleep apnea after obtaining the necessary consent.The analysis was done by using SPSS, IBM version 20.0. The level of significance was fixed at 5% and p ≤ 0.05. On evaluation of berlin questionnaire revealed that 83% of patients (including male and female) shows the significant improvement after the MAD treatment 16.7 % patient are not satisfied with the MAD. Study shows that the patient with OSA showed Positive Berlin Questionnaire before the MAD treatment and after 2 month it was revealed that MAD treatment showed statically significant improvement in OSA.

Obstructive Sleep Apnea Patient’s Heart Beat Monitoring System from Android Smartphone using MQTT Protocol

Sleep apnea disease is a disease at the respiration system in a human that dangerous and has a high mortality rate. There is two sleep apnea, the first is central sleep apnea and then obstructive sleep apnea, basically sleep apnea is a condition that somebody stop breathing when they were sleeping for a few second, sleep apnea caused by the relaxation of respiration muscle. When sleep apnea comes back, sleep apnea patients need to wake up from and breathe normally again. This system is made to provide some mechanism outside of the human body to help obstructive sleep apnea patient woke up from their sleep and breathe well. Furthermore, with this system, might patients could be monitored although they were not at a hospital. In its work, the system is using a microcontroller and smartphone that are connected with the MQTT protocol to help patients. The microcontroller is used for sensing patient heart rate by connecting it with the AD8232 module sensor wich then the signal would be classified to determine the condition of sleep apnea using the KNN classification method. The result of classification by the microcontroller be delivered to the user’s smartphone to be the trigger for alarm, patient’s monitoring system, etc. Research result shows that the MQTT protocol 100% successful to transmit the data with 39.74 ms delay. The patient, patient’s family, and medic smartphone’s apps can monitor and successfully show a notification when sleep apnea’s patient recurring. Accurate of the sensor at sensing heart rate is 91.32% and the accuracy of the classification method is 86.6%. Other than that, the average processing speed from the sensing proses to classification is 1273.85 ms, and the time needed for data arrived at the user’s smartphone is 1312.74 ms.

Floppy Closing Door Epiglottis Treated Successfully with an Mhealth Application Based on Myofunctional Therapy: A Case Report

We introduce the first case reported to date of a floppy closing door epiglottis in an OSA (obstructive sleep apnea) patient treated successfully with an Mhealth smartphone application based on myofunctional therapy.