Screening of obstructive sleep apnea in an urban population in south India

Rajeswari, J.; Mani, Mohan Prasath

doi:10.1016/j.obmed.2020.100220

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…A self-administered questionnaire was developed to analyze age, gender, smoking habits, and working hours to diagnose sleep apnea. In conclusion, the young, male adults, smokers, and those long working hours seemed to be highly affected by sleep apnea [ 34 ]. The diagnosis of sleep stages and sleep-related disorders were analyzed using the standard questionnaires from the analysis of age and gender.…”

Section: Discussionmentioning

confidence: 99%

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Jayaraj

Mani

2021

Diagnostics

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To classify between normal and sleep apnea subjects based on sub-band decomposition of electroencephalogram (EEG) signals. This study comprised 159 subjects obtained from the ISRUC (Institute of System and Robotics—University of Coimbra), Sleep-EDF (European Data Format), and CAP (Cyclic Alternating Pattern) Sleep database, which consists of normal and sleep apnea subjects. The wavelet packet decomposition method was incorporated to categorize the EEG signals into five frequency bands, namely, alpha, beta, delta, gamma, and theta. Entropy and energy (non-linear) for all bands was calculated and as a result, 10 features were obtained for each EEG signal. The ratio of EEG bands included four parameters, including heart rate, brain perfusion, neural activity, and synchronization. In this study, a support vector machine with kernels and random forest classifiers was used for classification. The performance measures demonstrated that the improved results were obtained from the support vector machine classifier with a kernel polynomial order 2. The accuracy (90%), sensitivity (100%), and specificity (83%) with 14 features were estimated using the data obtained from ISRUC database. The proposed study is feasible and seems to be accurate in classifying the subjects with sleep apnea based on the extracted features from EEG signals using a support vector machine classifier.

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Section: Discussionmentioning

confidence: 99%

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Jayaraj

Mani

2021

Diagnostics

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“…Before the experiment began, each participant was handed a questionnaire. According to the questionnaire's standard, a score of 5 or higher indicates the presence of OSA, whereas a score of 5 or lower indicates normal [3].…”

Section: Self-administered Questionnairementioning

confidence: 99%

“…Based on the ndings above, a method was suggested to enhance the effects of music 1 (Neelambari) and music 2 on sleep apnea patients (Kapi). With a self-administered questionnaire that evaluated the Berlin, STOP-BANG, and OSA50 questionnaires, the participants were initially divided into a sleep apnea group and a normal group [3]. The signal acquisition was carried out using an EmotivEpoc + headset, and the protocol included a two-stage analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The polysomnography (PSG) approach was used to diagnose sleep apnea, which includes physiological signals such as the electromyogram, electrocardiogram, electroencephalogram (EEG), electrooculogram respiratory movement, air ow, oxygen saturation, and body posture [2]. To overcome the time consumption and expense of this PSG method, an enormous quantity of literature was considered validated questionnaires for diagnosing obstructive sleep apnea (OSA) [3]. The most effective treatment for all OSA patients is positive airway pressure (PAP).…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalogram Based Music Interventions on Obstructive Sleep Apnea using Brain Connectivity Analysis

Sreedha

et al. 2024

Preprint

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Sleep apnea is a disorder that represents the blockage of air in the upper airway while sleeping. It affects regular breathing repeatedly and poses potential risks. In this proposed study, brain connectivity analysis has been monitored using Indian Carnatic music in obstructive sleep apnea (OSA) patients. This study utilized 12 participants who underwent electroencephalogram (EEG) recording in two sessions, with no music and with music 1 (Neelambari) and music 2 (Kapi). After the pre-processing, the EEG signal bands were separated using wavelet packet decomposition (WPD). The absolute values of wavelet coefficients were used to analyze the brain connectivity patterns on both music and no music conditions. Compared to no music, Neelambari's music condition resulted in improved right-hemispheric effective connectivity between the frontal and temporal (F8 and T8) "executive" nodes. The correlation value of the beta band (0.943 & 0.998) and delta band (0.945 & 0.999), respectively, where the moderate right-hemispheric connectivity has been noted among regions in the theta (0.746 & 0.996) and alpha band (0.746 & 0.996). In no music condition, there is moderate right and left hemispheric connection between the frontal and temporal nodes in the delta (0.853 & 0.999) and beta band (0.885 to 0.993). In contrast, there is reduced connectivity in the theta (0.603 & 0.688) and alpha band (0.6883 to 0.9913). This proposed study conveyed that effective connectivity was noted in the Neelambari music condition compared to no music.

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The association between long working hours and obstructive sleep apnea assessed by STOP-BANG score: a cross-sectional study

Lee

2022

Int Arch Occup Environ Health

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Screening of obstructive sleep apnea in an urban population in south India

Cited by 4 publications

References 20 publications

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Classification of Sleep Apnea Based on Sub-Band Decomposition of EEG Signals

Electroencephalogram Based Music Interventions on Obstructive Sleep Apnea using Brain Connectivity Analysis

The association between long working hours and obstructive sleep apnea assessed by STOP-BANG score: a cross-sectional study

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