Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal

Wang, Bochun; Yi, Xuanyu; Gao, Jiandong; Li, Yanru; Xu, Wen; Wu, Ji; Han, Demin

doi:10.5664/jcsm.9292

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Compared to approaches that only analyze snoring sounds, the current models may be more likely to be suitable for non-snorers with OSA. 55 Some researchers proposed age- and sex-dependent models that trained using only baseline characteristics (i.e. WC, NC, and BMI).…”

Section: Discussionmentioning

confidence: 99%

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

et al. 2023

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Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

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Section: Discussionmentioning

confidence: 99%

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

et al. 2023

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“…We addressed this issue with SMOTE, similar to Huseyin Nasifoglu's study, which had a 1:5 imbalance ratio in their data. We found two studies also used single‐lead ECG for detection models, while some studies used a set of signals (Maali & Al‐Jumaily, 2013; Waxman et al, 2010) or single respiratory signals such as nasal flow (Ozdemir et al, 2016), or snore sound (B. Wang et al, 2021). Despite reflecting indirect respiratory pattern changes, single‐lead ECG signals are widely available and less intrusive in nature.…”

Section: Discussionmentioning

confidence: 99%

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine‐learning and bag‐of‐features framework

Linh

Trang

Lin

et al. 2023

Journal of Sleep Research

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SummaryObstructive sleep apnea (OSA) has a heavy health‐related burden on patients and the healthcare system. Continuous positive airway pressure (CPAP) is effective in treating OSA, but adherence to it is often inadequate. A promising solution is to detect sleep apnea events in advance, and to adjust the pressure accordingly, which could improve the long‐term use of CPAP treatment. The use of CPAP titration data may reflect a similar response of patients to therapy at home. Our study aimed to develop a machine‐learning algorithm using retrospective electrocardiogram (ECG) data and CPAP titration to forecast sleep apnea events before they happen. We employed a support vector machine (SVM), k‐nearest neighbour (KNN), decision tree (DT), and linear discriminative analysis (LDA) to detect sleep apnea events 30–90 s in advance. Preprocessed 30 s segments were time–frequency transformed to spectrograms using continuous wavelet transform, followed by feature generation using the bag‐of‐features technique. Specific frequency bands of 0.5–50 Hz, 0.8–10 Hz, and 8–50 Hz were also extracted to detect the most detected band. Our results indicated that SVM outperformed KNN, LDA, and DT across frequency bands and leading time segments. The 8–50 Hz frequency band gave the best accuracy of 98.2%, and a F1‐score of 0.93. Segments 60 s before sleep events seemed to exhibit better performance than other pre‐OSA segments. Our findings demonstrate the feasibility of detecting sleep apnea events in advance using only a single‐lead ECG signal at CPAP titration, making our proposed framework a novel and promising approach to managing obstructive sleep apnea at home.

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Screening the Risk of Obstructive Sleep Apnea by Utilizing Supervised Learning Techniques Based on Anthropometric Features and Snoring Events (Preprint)

Tsai¹,

Liu²,

Hsu³

et al. 2022

Preprint

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BACKGROUND Obstructive sleep apnea (OSA) is sleep-disordered breathing and is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. OBJECTIVE This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features (e.g., symptoms or risk factors of OSA). METHODS This retrospective study collected data on 3629 patients from Taiwan, who had undergone PSG for symptoms of OSA. Their baseline characteristics, anthropometric measures, and PSG data were obtained. The number of snoring events of PSG was further derived, and correlations among the collected variables were investigated. Next, this study utilized six common supervised machine learning techniques to establish OSA risk screening models, including random forest (RF), XGBoost, k-nearest neighbors, support vector machine, logistic regression, and naïve Bayes. First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach which had the highest accuracy in the training and validation phase was employed to perform the classification for the test dataset. Moreover, the feature importance of employed models was determined by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. RESULTS RF models manifested the highest accuracy and area under the receiver operating characteristic curve (AUC) in the training and validation phase in screening for both OSA severities (over 70% of accuracy and over 80% of AUC). Hence, we employed the RF to perform the classification of the independent test dataset, and results showed 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Next, snoring events and the visceral fat level were the most and second-most essential features of screening for OSA risk. CONCLUSIONS Snoring events and the visceral fat level were essential features of screening for OSA risk. Based on these easily assessed variables, this study established models that can be considered to apply for screening OSA risk in populations with similar craniofacial features.

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Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal

Cited by 3 publications

References 27 publications

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Detection of preceding sleep apnea using ECG spectrogram during CPAP titration night: A novel machine‐learning and bag‐of‐features framework

Screening the Risk of Obstructive Sleep Apnea by Utilizing Supervised Learning Techniques Based on Anthropometric Features and Snoring Events (Preprint)

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