Obstructive sleep apnea (OSA) is a typical rest problem brought by strange relaxation on breathing. The seriousness of OSA can prompt numerous manifestations, for example, unexpected heart failure. Polysomnography (PSG) is the highest quality level for OSA determination. It archives numerous signs as of the patient’s body intended in any event one entire evening then ascertains the apnea–hypopnea index (AHI) which is quantity of apnea or hypopnea rates every time. This worth is then used to group patients into OSA seriousness levels. OSA is an undeniably normal, constant, rest-related breathing issue. OSA is described by occasional narrowing and impediment of the pharyngeal aviation route during rest. In this paper, the OSA has been detected by lessening clamor in ECG chronicles, and the first ECG signal was separated through a Pan–Tompkins algorithm and Fisher feature selection. OSA severity classification using KNN using Transfer Learning. The primary preferences of our proposed strategy incorporate simpler information securing, immediate OSA seriousness discovery, and successful element extraction without area information from the ability. The data has been collected from PhysioNet, and the simulation has been carried out using MATLAB. In this, there are 92 instances available, the 72 data used for training, and remaining 20 have taken into testing purpose. The outcome of the proposed method shows accuracy 98.6%, sensitivity at 97.3%, and specificity which has been achieved by 100%.


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    Title :

    Obstructive Sleep Apnea Severity Detection Using Pan–Tompkins Algorithm and Fisher Feature Selection


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:


    Publication date :

    2021-11-23


    Size :

    18 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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