Non-contact Sleep Apnea Syndrome Detection Based on What Random Forests Learned

Nakari, Iko; Kitajima, Eiki; Tajima, Yusuke; Takadama, Keiki

doi:10.1109/lifetech48969.2020.1570619209

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…OSA patients (positive) and healthy people (negative) are divided by an AHI threshold, and the proposed model uses a RF classifier to identify healthy subjects and patients with OSA. RF uses decision trees as the basic unit by integrating a large number of decision trees (Nakari et al 2020). The ith tree predicts the classification as C i , and finally, the classification decision is decided by voting, where the majority type diagnosis result is the final classification result.…”

Section: Potential Osa Patient Diagnosismentioning

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: Potential Osa Patient Diagnosismentioning

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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“…To tackle this problem, this paper focuses on the WAKE stage (i.e., shallow sleep) in the sleep stages as a new characteristic of SAS, instead of respiration as a traditional characteristic of SAS. This is because (1) the WAKE stage in the SAS patients often occurs in comparison with that in the non-SAS subjects due to apnea/hypopnea; (2) our previous research [13] found that it is difficult to detect the WAKE stage in the SAS patients by the Machine Learning (ML) model learned for the non-SAS subjects. These facts hypothesize that the characteristic of the WAKE stage in the SAS patients differs from the non-SAS subjects.…”

mentioning

confidence: 99%

Explainable Non-Contact Sleep Apnea Syndrome Detection Based on Comparison of Random Forests

Nakari,

Takadama

2024

IEEE Access

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This paper focuses on Sleep Apnea Syndrome (SAS) and proposes the novel eXplainable AI (XAI) method that extracts characteristics of SAS by comparing the datasets of the SAS patients and the non-SAS subjects. For this issue, this paper (i) employs "two" Random Forests (RFs) to respectively learn the models for the SAS patients and the non-SAS subjects to classify the WAKE/non-WAKE stage, (ii) compares the two learned RFs to find their difference as the physiological characteristic of SAS, and (iii) proposes the SAS detection method based on the difference between the two learned RFs. Through the human subject experiment of the SAS detection based on the biological vibration data acquired from the mattress sensor during sleep, the following implications have been revealed: (1) RF learned from the SAS patient data classifies the WAKE/non-WAKE stage from the viewpoint of the ''low'' frequencies of the biological vibration data, while RF learned from the non-SAS subject data classifies it from the viewpoint of its ''high'' frequencies; and (2) the SAS patients have the WAKE stage with the low frequencies of the biological vibration data caused by disturbances in the autonomic nervous system due to apnea/hypopnea, while the non-SAS subjects do not have it but have the usual WAKE stage with the high frequencies caused by large body movements, which has a potential of the new characteristic of SAS instead of respiration as a traditional characteristic of SAS.

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Cloud-based non-invasive cognitive breath monitoring system for patients in health-care system

Soni,

Shabaz,

Maaliw

et al. 2023

Int J Data Sci Anal

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Non-contact Sleep Apnea Syndrome Detection Based on What Random Forests Learned

Cited by 3 publications

References 13 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

Explainable Non-Contact Sleep Apnea Syndrome Detection Based on Comparison of Random Forests

Cloud-based non-invasive cognitive breath monitoring system for patients in health-care system

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