Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome

doi:10.1016/j.sleep.2021.07.012

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24042

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24042

Title:	Explainable fuzzy neural network with easy-to-obtain physiological features for screening obstructive sleep apnea-hypopnea syndrome
Authors:	Juang, CF;Wen, CY;Chang, KM;Chen, YH;Wu, MF;Huang, WC
Keywords:	Apnea-hypopnea index;Fuzzy neural networks;Neural networks;Obstructive sleep apnea-hypopnea;syndrome
Date:	2021
Issue Date:	2022-08-05T09:46:54Z (UTC)
Publisher:	ELSEVIER
ISSN:	1389-9457
Abstract:	Objective/background: Recently, several tools for screening obstructive sleep apnea-hypopnea syndrome (OSAHS) have been devised with varied shortcomings. To overcome these drawbacks, we aimed to propose a self-estimation method using an explainable prediction model with easy-to-obtain variables and evaluate its performance for predicting OSAHS. Patients/methods: This retrospective, cross-sectional study selected significant easy-to-obtain variables from patients, suspected of having OSAHS by regression analysis, and fed these variables into the proposed explainable fuzzy neural network (EFNN), a back propagation neural network (BPNN) and a stepwise regression model to compare the screening performance for OSAHS. Results: Of the 300 participants, three easily available features, such as waist circumference, mean blood pressure (BP) at the end of polysomnography and the difference in systolic BP between the end and start of polysomnography, were obtained from regression analysis with a five-fold cross-validation scheme. Feeding these three variables into the prediction models showed that the average prediction differences for apnea-hypopnea index (AHI) when using the EFNN, BPNN, and regression model were respectively 1.5 +/- 18.2, 3.5 +/- 19.1 and 0.1 +/- 19.3, indicating none of the tested methods had good efficacy to predict the AHI values. The performance as determined by the sensitivity thorn specificity-1 value for screening moderate-to-severe OSAHS of the EFNN, BPNN and regression model were respectively 0.440, 0.414 and 0.380. Conclusions: When fed with easy-to-obtain physiological features, the understandable EFNN should be the preferred method to predict moderate-to-severe OSAHS. (c) 2021 Elsevier B.V. All rights reserved.
URI:	http://dx.doi.org/10.1016/j.sleep.2021.07.012 https://www.webofscience.com/wos/woscc/full-record/WOS:000693402400036 https://ir.csmu.edu.tw:8080/handle/310902500/24042
Relation:	SLEEP MEDICINE ,2021,v85 , P280-290
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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