Analysis of Sleeping Respiratory Signal Utilizing Frequency Energy Features

Zhao, Sixian; Fang, Yu; Wang, Weibo; Liu, Dongbo

doi:10.1109/icicsp55539.2022.10050697

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…In patients with severe OSA, snoring after an episode of sleep apnea is usually heavier and louder than normal breathing (Sola-Soler et al 2005). Accordingly, patients with OSA exhibit more irregularities during snoring in night recordings than healthy people (Zhao et al 2022). Statistical features can describe the irregularity of the data, so we calculated the corresponding statistics for the deep snoring features to represent the irregularity between snoring sounds (Khudair and Lafta 2021).…”

Section: Statistical Feature Representation Methodsmentioning

confidence: 99%

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis

Luo,

Zhao,

Liu

et al. 2024

Physiol. Meas.

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Objective: Obstructive sleep apnea (OSA) is a high-incidence disease that is seriously harmful and potentially dangerous. The objective of this study was to develop a noncontact sleep audio signal-based method for diagnosing potential obstructive sleep apnea (OSA) patients, aiming to provide a more convenient diagnostic approach compared to the traditional polysomnography (PSG) testing. Approach: The study employed a shifted window transformer model to detect snoring audio signals from whole-night sleep audio. First, a snoring detection model was trained on large-scale audio datasets. Subsequently, the deep feature statistical metrics of the detected snore audio were used to train a random forest classifier for OSA patient diagnosis. Main results:Using a self-collected dataset of 305 potential OSA patients, the proposed snore shifted-window transformer method (SST) achieved an accuracy of 85.9\%, a sensitivity of 85.3\%, and a precision of 85.6\% in OSA patient classification. These values surpassed the state-of-the-art method by 9.7\%, 10.7\%, and 7.9\%, respectively. Significance: The experimental results demonstrated that SST significantly improved the noncontact audio-based OSA diagnosis performance. The study's findings suggest a promising self-diagnosis method for potential OSA patients, potentially reducing the need for invasive and inconvenient diagnostic procedures.

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Section: Statistical Feature Representation Methodsmentioning

confidence: 99%

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis

Luo,

Zhao,

Liu

et al. 2024

Physiol. Meas.

View full text Add to dashboard Cite

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“…The snoring detection method amalgamates the features of both uniform and non-uniform divisions. Equation ( 4) explains its formulation [16].…”

Section: Classification Of Breath Sound and Snoringmentioning

confidence: 99%

Improving OSAHS Prevention Based on Multidimensional Feature Analysis of Snoring

Fang,

Liu,

Zhao

et al. 2023

Electronics

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Obstructive Sleep Apnea–Hypopnea Syndrome (OSAHS), a severe respiratory sleep disorder, presents a significant threat to human health and even endangers life. As snoring is the most noticeable symptom of OSAHS, identifying OSAHS via snoring sound analysis is vital. This study aims to analyze the time-domain and frequency-domain characteristics of snoring sounds to detect OSAHS and its severity. The snoring sounds are extracted and scrutinized from nighttime acoustic signals, with spectral energy ratio features being applied, calculated via the snore detection frequency division method. A variety of time and frequency-domain features are derived from the snoring sounds. A novel Snore Detection Cepstral Coefficient (SDCC) is proposed, based on Mel Frequency Cepstral Coefficients (MFCCs) and snore detection frequency division. Relief-F feature screening is then applied to SDCC and MFCC. Canonical Correlation Analysis (CCA) is utilized on the fusion features obtained as a result, and the results indicate the highest accuracy (97.8%) with Subspace KNN. The optimal classifier with feature combination is used for the snore model of OSASH early warnings all night, effectively recognizing and assessing OSAHS and reflecting the severity of its disease. This result, achieving high accuracy and low computational complexity, shows that the proposed method holds significant promise for developing portable sleep health detection devices.

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Analysis of Sleeping Respiratory Signal Utilizing Frequency Energy Features

Cited by 2 publications

References 12 publications

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis

SST: a snore shifted-window transformer method for potential obstructive sleep apnea patient diagnosis

Improving OSAHS Prevention Based on Multidimensional Feature Analysis of Snoring

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