A Comprehensive Analysis on the Efficient Mechanisms to Detect Obstructive Sleep Apnea Using AI and Heuristic Algorithms
01 Jan 2021-International journal of current research and review (Radiance Research Academy)-Vol. 13, Iss: 04, pp 91-96
TL;DR: A comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.
Abstract: Obstructive sleep apnea is a common problem arising in adults and children nowadays, determined by abnormalities in breathing gaps or incapability of air intake capacity during sleeping results in a decrease in oxygen level in blood. The brain detects this sudden decrease in the level of oxygen and sends a signal to wake the person up. Studies revealed the breathing stops for almost 10 seconds during a sleep apnea episode. There is no restriction on who can develop Obstructive Sleep Apnea(OSA), it can affect adults as well as infants. Our research primarily aims at assessing the various recent developments and studies made as a solution to this alarming problem. Their methodology and techniques have been studied and accuracy and sensitivity rates compared. A comprehensive and detailed study has been conducted on several research papers and studies done in the field of predicting sleep apnea. Sleep Apnea and classification of apneic signals have been mentioned in our study. The related researches have been studied extensively and compiled in our research work. The various techniques used by the researchers have been studied and tabulated along with the algorithm accuracies. It is observed that signal measurement along with AI algorithms has made significant advancements in OSA prediction. It is observed that Self Developed Algorithm on VAD showed the highest accuracy of 97%. PPG signal analysis and binary classification algorithm showed good accuracies of 86.67% and 86% respectively. AdaBoost, Decision Table and Bagging REPTree and SVM classifier also showed good accuracy of around 83% in the detection of Sleep Apnea episodes. The study highlighted the research works done to combat the rising problem of Obstructive Sleep Apnea. This comprehensive study of existing methods will help researchers to identify their drawbacks and find out more efficient solutions to them, which will help the humanity less prone to risks due to this alarming issue of sleep apnea.
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TL;DR: This paper proposes an easy-to-use, cheap mobile-based approach relying on three steps, which has been tested on a literature database with 35 OSA patients and a comparison against five well-known classifiers has been carried out.
Abstract: Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF...THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out.
44 citations
TL;DR: This work compares two different feature selection methods, one of which is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search, which present a good option for apnea screening with low resources.
Abstract: Obstructive sleep apnea is a disorder characterized by pauses in respiration during sleep. Due to this disturbance in breathing, there is a decrease in the oxygen saturation (SpO2) level. Thus, SpO2 can be used as a source of information for the automatic detection of apnea. Several solutions exist in the literature where different features are used. To find a better discriminant capacity, a subset of few features that obtains higher accuracy with the proper classifier is needed. To face this challenge, this work compares two different feature selection methods. The first one is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search. These methods are tested with different classifiers. Two public datasets with 8 and 25 subjects are used to test and compare the performances of the different feature selection methods. A set of features for each classifier is obtained, and the results are compared with the previous work. The results found in this work show a good performance with respect to the state of the art and present a good option for apnea screening with low resources.
20 citations
TL;DR: In this article, the vibrational wavenumber of Modafinil was computed using density functional theory and the data obtained from the calculations were used to assign vibrational bands obtained in the IR and Raman spectrum.
Abstract: Modafinil is a wakefulness-promoting agent used for treatment of disorders such as narcolepsy, Sleep disorders and excessive daytime sleepiness associated with obstructive sleep apnea. It has also been seen widespread psychostimulant used as a purported cognition-enhancing agent. IR and FT-Raman spectrum of Modafinil was analyzed. The vibrational wavenumber were computed using Density Functional Theory. The data obtained from wavenumber calculations are used to assign the vibrational bands obtained in the IR and Raman spectrum. The bond length and bond angles of the title compound are computed. NBO analysis, HOMO–LUMO, first and second order hyperpolarizability and molecular electrostatic potential results are also reported. PASS analysis of the Modafinil predicts sleep disorders treatment, activity with Pa (probability to be active) value of 0.822. Molecular docking studies exhibit the posing of ligand with the protein.
6 citations