A New Method for Self-Estimation of the Severity of Obstructive Sleep Apnea Using Easily Available Measurements and Neural Fuzzy Evaluation System

Wu, Ming‐Feng; Huang, Wei-Hsiang; Juang, Chia‐Feng; Chang, Kai-Ming; Wen, Chih-Yu; Chen, Yu-Hsuan; Lin, Ching-Yi; Chen, Yi-Chan; Lin, Ching-Cheng

doi:10.1109/jbhi.2016.2633986

Cited by 18 publications

(9 citation statements)

References 31 publications

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“…The gold standard of sleep-disordered diagnosis including conditions such as OSA is polysomnography (PSG). It is used to determine the frequency and severity of normal respiratory disorder events per hour and reports as the Apnea-Hypopnea Index (AHI) which can be used to classify the OSA as normal (AHI<5), mild (AHI is in [5][6][7][8][9][10][11][12][13][14], moderate (AHI is in [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30], and severe (AHI>30), respectively [10]. However, this method is a form of clinical practice which has to be done overnight in a laboratory or hospital [13] using numerous sensors to acquire the necessary data, such as electroencephalogram (EEG), electrooculogram (EOG), chin electromyography (EMG), leg movement, airflow, cannula flow, respiratory effort, oximetry, body position, electrocardiogram (ECG), and so forth [6].…”

Section: Introductionmentioning

confidence: 99%

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

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Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal breathing. The severity of OSA can lead to many symptoms such as sudden cardiac death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It records many signals from the patient's body for at least one whole night and calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or hypopnea incidences per hour. This value is then used to classify patients into OSA severity levels. However, it has many disadvantages and limitations. Consequently, we proposed a novel methodology of OSA severity classification using a Deep Learning approach. We focused on the classification between normal subjects (AHI < 5) and severe OSA patients (AHI > 30). The 15-second raw ECG records with apnea or hypopnea events were used with a series of one-dimensional Convolutional Neural Networks (1-D CNN) for automatic feature extraction, deep recurrent neural networks with Long Short-Term Memory (LSTM) for temporal information extraction, and fully-connected neural networks (DNN) for feature encoding from a large number of features until it closed to two classes. The main advantages of our proposed method include easier data acquisition, instantaneous OSA severity detection, and effective feature extraction without domain knowledge from expertise. To evaluate our proposed method, 545 subjects of which 364 were normal and 181 were severe OSA patients obtained from the MrOS sleep study (Visit 1) database were used with the kfold cross-validation technique. The accuracy of 79.45% for OSA severity classification with sensitivity, specificity, and F-score was achieved. This is significantly higher than the results from the SVM classifier with RR Intervals and ECG derived respiration (EDR) signal feature extraction. The promising result shows that this proposed method is a good start for the detection of OSA severity from a single channel ECG which can be obtained from wearable devices at home and can also be applied to near realtime alerting systems such as before SCD occurs.

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Section: Introductionmentioning

confidence: 99%

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Banluesombatkul

Rakthanmanon

Wilaiprasitporn

2018

TENCON 2018 - 2018 IEEE Region 10 Conference

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“…For instance, gender appears as important in the works of Mencar et al [15] and Ustun et al [21], but not in our setting. Possibly, this is because females are under-represented in our work, and in the works of Wu et al [25] and Huang et al [12]; in our dataset, only 26% of the patients are women.…”

Section: Discussion On the Selected Attributesmentioning

confidence: 69%

“…In the work of Wu et al [25], the authors use a neural fuzzy evaluation system [26] over 17 patient variables to predict the occurrence of OSA, as well as the value of indicator AHI. Their findings, by means of a stepwise regression [3], indicate that variables Body Mass Index (BMI), difference of systolic blood pressure before going to sleep and early in the morning, and the Epworth Sleepiness Scale (ESS) [1], were the most important factors in predicting OSA.…”

Section: Related Workmentioning

confidence: 99%

An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening

Rodrigues

Pépin

Goeuriot

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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The identification of Obstructive Sleep Apnea (OSA) relies on laborious and expensive polysomnography (PSG) exams. However, it is known that other factors, easier to measure, can be good indicators of OSA and its severity. In this work, we extensively investigate the use of Machine Learning techniques in the task of determining which factors are more revealing with respect to OSA. We ran extensive experiments over 1,042 patients from the Centre Hospitalier Universitaire of the city of Grenoble, France. The data included ordinary clinical information, and PSG results as a baseline. We employed data preparation techniques including cleaning of outliers, imputation of missing values, and synthetic data generation. Following, we performed an exhaustive attribute selection scheme to find the most representative features. We found that the prediction of OSA depends largely on variables related to age, body mass, and sleep habits more than the ones related to alcoholism, smoking, and depression. Next, we tested 60 regression/classification algorithms to predict the Apnea-Hypopnea Index (AHI), and the AHI-based severity of OSA. We achieved performances significantly superior to the state of the art both for AHI regression and classification. Our results can benefit the development of tools for the automatic screening of patients who should go through polysomnography and further treatments of OSA. Our methodology enables experimental reproducibility on similar OSA-detection problems, and more generally, on other problems with similar data models.

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“…Neural fuzzy networks (NFNs) have been widely applied in various fields [1][2][3]. Traditional NFNs combine neural networks to learn from processes with fuzzy reasoning to handle uncertain information.…”

Section: Introductionmentioning

confidence: 99%

“…In layer 2, we adopted a Gaussian membership function for FNFN, which had the following advantages: (1) a small number of parameters are needed to define; (2) better robustness; and (3) the performance is superior than polygonal membership functions. The degree of the membership function is calculated (3) where mij and σij represent the expected value and variance, respectively.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

et al. 2018

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This study used Xilinx Field Programmable Gate Arrays (FPGAs) to implement a functional neuro-fuzzy network (FNFN) for solving nonlinear control problems. A functional link neural network (FLNN) was used as the consequent part of the proposed FNFN model. This study adopted the linear independent functions and the orthogonal polynomials in a functional expansion of the FLNN. Thus, the design of the FNFN model could improve the control accuracy. The learning algorithm of the FNFN model was divided into structure learning and parameter learning. The entropy measurement was adopted in the structure learning to determine the generated new fuzzy rule, whereas the gradient descent method in the parameter learning was used to adjust the parameters of the membership functions and the weights of the FLNN. In order to obtain high speed operation and real-time application, a very high speed integrated circuit hardware description language (VHDL) was used to design the FNFN controller and was implemented on FPGA. Finally, the experimental results demonstrated that the proposed hardware implementation of the FNFN model confirmed the viability in the temperature control of a water bath and the backing control of a car.Electronics 2018, 7, 145 2 of 22 the accuracy of functional approximation. Corresponding to a FLNN, each fuzzy rule comprises a functional expansion of inputs. The linearly independent functions and orthogonal polynomials are used in FLNN. The learning algorithm was divided into structure learning and parameter learning and used for constructing the FNFN automatically. Initially, no rules existed in the FNFN model. In the structure learning algorithm, the entropy measure was used to determine a whether a new node needed to be added. In the parameter learning, the backpropagation leaning method was used to adjust the parameters of the FNFN model.Real-time control is very important in industrial process control system applications. For rapid computing hardware engineering, real-time control becomes more feasible [10]. Recently, there has been a focus on the hardware implementation [11] of artificial neural networks (ANNs). Furthermore, the realization that a hybrid of neural networks and fuzzy systems presents an even more powerful form of computational intelligence [12] provides additional motivation to complete hardware implementation. The main reason for hardware implementation is that it has high speed processing and real-time operating capability. In many applications, hardware implementation requires larger arrays and has resorted to digital simulation, which are usually built using digital integrated circuits. Development of digital integrated circuits such as FPGA [13] makes the hardware implementation process programmable and flexible. Recently, the hardware implementation of neural networks has been successfully implemented. Li et al.[10] discussed various aspects of the hardware implementation of an artificial neural network (ANN), e.g., generic architecture, back propagation, precision, etc. One of the b...

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A New Method for Self-Estimation of the Severity of Obstructive Sleep Apnea Using Easily Available Measurements and Neural Fuzzy Evaluation System

Cited by 18 publications

References 31 publications

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

Single Channel ECG for Obstructive Sleep Apnea Severity Detection Using a Deep Learning Approach

An Extensive Investigation of Machine Learning Techniques for Sleep Apnea Screening

FPGA Implementation of a Functional Neuro-Fuzzy Network for Nonlinear System Control

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