Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Hassan, Ahnaf Rashik

doi:10.1016/j.bspc.2016.05.009

Cited by 131 publications

(44 citation statements)

References 31 publications

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“…On the other hand, the wavelet transform's Q-factor should be high for analyzing oscillatory signals, such as-EEG [30]. Nevertheless, except for the continuous wavelet transform, most wavelet transforms cannot tune the Q-factor [29].…”

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confidence: 98%

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

2017

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Section: Advantages Of Tqwtmentioning

confidence: 98%

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

2017

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“…Then the respiratory signals are labeled based on the features using a suitable classification algorithm. Obstructive sleep apnea (OSA) is a syndrome caused by the impulsive fall in air flow when a person sleeps and it is observed due to decrease in the oxygen saturation [Gutta, Cheng, Nguyen et al (2018); Hassan (2016)]. OSA is a condition for the serious disorders like cardiovascular disease, stroke, fatigue which decreases a cognitive function and extend of day time sleepiness.…”

Section: Introductionmentioning

confidence: 99%

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Sunil¹,

Sankaragomathi²

2019

Computer Modeling in Engineering &Amp; Sciences

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Obstructive Sleep Apnea (OSA) is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation. The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea (SA) activity. In the proposed method, the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted. These features are applied to the Classification and Regression Tree (CART)-Particle Swarm Optimization (PSO) classifier which classifies the signal into normal breathing signal and sleep apnea signal. The proposed method is validated to measure the performance metrics like sensitivity, specificity, accuracy and F1 score by applying time domain and frequency domain features separately. Additionally, the performance of the CART-PSO (CPSO) classification algorithm is evaluated through comparing its measures with existing classification algorithms. Concurrently, the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.

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“…Many examples can be mentioned here regarding the application of intelligent techniques in medical diagnostic automation (Hassan & Haque, 2015a, 2016a, 2016b; Bashar, Hassan & Bhuiyan, 2015a; Hassan, 2015a, 2015b, 2016) and EEG analysis (Hassan & Haque, 2015b, 2015c, 2016a, 2017; Bashar, Hassan & Bhuiyan, 2015b; Hassan, Siuly & Zhang, 2016; Hassan & Subasi, 2016, 2017; Hassan & Bhuiyan, 2015, 2016a, 2016b, 2016c, 2017; Hassan, Bashar & Bhuiyan, 2015a, 2015b). In 2011, Kravoska et al achieved 81% accuracy in sleep scoring using various features derived from PSG signals.…”

Section: Introductionmentioning

confidence: 99%

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Savareh

Bashiri

Behmanesh

et al. 2018

PeerJ

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IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting

Cited by 131 publications

References 31 publications

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

An automated method for sleep staging from EEG signals using normal inverse Gaussian parameters and adaptive boosting

Analysis of OSA Syndrome from PPG Signal Using CART-PSO Classifier with Time Domain and Frequency Domain Features

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

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