Suitable Feature Selection for OSA Classification Based on Snoring Sounds

Temrat, Phatchara; Jiraraksopakun, Yuttapong; Bhatranand, Apichai; Wea-asae, Katinee

doi:10.1109/ecticon.2018.8620046

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…Table I gives the extinction coefficient of deoxyhemoglobin and oxyhemoglobin at these two wavelengths. 3) DPF: DP F is the differential path length factor matrix, given by (5):…”

Section: A Principles Of the Fnirs Systemmentioning

confidence: 99%

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A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

Huang

Tang

Luo

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitive measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.

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“…Table I gives the extinction coefficient of deoxyhemoglobin and oxyhemoglobin at these two wavelengths. 3) DPF: DP F is the differential path length factor matrix, given by (5):…”

Section: A Principles Of the Fnirs Systemmentioning

confidence: 99%

“…Noncontact approach is generally based on audio and video recording, etc. For example, OSA can be identified through snoring [5], speech [6], and vision-based cardiopulmonary signals [7]. Noncontact approach dramatically reduces the cost, but it is vulnerable to external environmental noise.…”

Section: Introductionmentioning

confidence: 99%

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

Huang

Tang

Luo

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Hashemi et al [14] used L2-distance to rank features in multi-label datasets. Temrat et al [30] performed feature selection based on total variation distance for OSA classification. Yoon et al [33] applied an estimation of Jensen-Shannon divergence to capture locally important features.…”

Section: Related Workmentioning

confidence: 99%

Feature Selection Based on Divergence Functions: A Comparative Classiffication Study

Pourmand

Shabbak

Ganjali

2021

Stat., optim. inf. comput.

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Due to the extensive use of high-dimensional data and its application in a wide range of scientifc felds of research, dimensionality reduction has become a major part of the preprocessing step in machine learning. Feature selection is one procedure for reducing dimensionality. In this process, instead of using the whole set of features, a subset is selected to be used in the learning model. Feature selection (FS) methods are divided into three main categories: flters, wrappers, and embedded approaches. Filter methods only depend on the characteristics of the data, and do not rely on the learning model at hand. Divergence functions as measures of evaluating the differences between probability distribution functions can be used as flter methods of feature selection. In this paper, the performances of a few divergence functions such as Jensen-Shannon (JS) divergence and Exponential divergence (EXP) are compared with those of some of the most-known flter feature selection methods such as Information Gain (IG) and Chi-Squared (CHI). This comparison was made through accuracy rate and F1-score of classifcation models after implementing these feature selection methods.

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“…For example, the nonlinear classification algorithm to identify snoring sounds was studied by Ankishan [ 13 ]; Lim proposed a snoring recognition method based on RNN [ 14 , 15 ]. The study of OSAHS recognition based on snoring has also been proposed after the effective extraction of snoring signals: After extracting the time-domain features of snoring after apnea events, Temrat et al judged the severity degree of OSAHS through distinguishing different types of snoring by the leave-one-out cross-validation technique [ 16 ]. However, the time domain features such as zero-crossing rate (ZCR), energy entropy (EE), and integrated electromyography (IEMG) extracting snoring from background noise in this paper have two problems: (1) The similar features of some audio data are not easy to be distinguished; (2) The feature dimension is too less.…”

Section: Introductionmentioning

confidence: 99%

Detection of Snore from OSAHS Patients Based on Deep Learning

Shen

Cheng

Zhu

et al. 2020

Journal of Healthcare Engineering

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Obstructive sleep apnea-hypopnea syndrome (OSAHS) is extremely harmful to the human body and may cause neurological dysfunction and endocrine dysfunction, resulting in damage to multiple organs and multiple systems throughout the body and negatively affecting the cardiovascular, kidney, and mental systems. Clinically, doctors usually use standard PSG (Polysomnography) to assist diagnosis. PSG determines whether a person has apnea syndrome with multidimensional data such as brain waves, heart rate, and blood oxygen saturation. In this paper, we have presented a method of recognizing OSAHS, which is convenient for patients to monitor themselves in daily life to avoid delayed treatment. Firstly, we theoretically analyzed the difference between the snoring sounds of normal people and OSAHS patients in the time and frequency domains. Secondly, the snoring sounds related to apnea events and the nonapnea related snoring sounds were classified by deep learning, and then, the severity of OSAHS symptoms had been recognized. In the algorithm proposed in this paper, the snoring data features are extracted through the three feature extraction methods, which are MFCC, LPCC, and LPMFCC. Moreover, we adopted CNN and LSTM for classification. The experimental results show that the MFCC feature extraction method and the LSTM model have the highest accuracy rate which was 87% when it is adopted for binary-classification of snoring data. Moreover, the AHI value of the patient can be obtained by the algorithm system which can determine the severity degree of OSAHS.

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Suitable Feature Selection for OSA Classification Based on Snoring Sounds

Cited by 9 publications

References 11 publications

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

A Wearable Functional Near-Infrared Spectroscopy (fNIRS) System for Obstructive Sleep Apnea Assessment

Feature Selection Based on Divergence Functions: A Comparative Classiffication Study

Detection of Snore from OSAHS Patients Based on Deep Learning

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