1. Lin SY, Tsai CY, Majumdar A, Ho YH, Huang YW, Kao CK, Yeh SM, Hsu WH, Kuan YC, Lee KY, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Liu WT. Combining a wireless radar sleep monitoring device with deep machine learning techniques to assess obstructive sleep apnea severity. J Clin Sleep Med. 2024 Aug 1;20(8):1267-1277. doi: 10.5664/jcsm.11136. PMID: 38546033; PMCID: PMC11294131.
2. Tsai CY, Majumdar A, Wang Y, Hsu WH, Kang JH, Lee KY, Tseng CH, Kuan YC, Lee HC, Wu CJ, Houghton R, Cheong HI, Manole I, Lin YT, Li LJ, Liu WT. Machine learning model for aberrant driving behaviour prediction using heart rate variability: a pilot study involving highway bus drivers. Int J Occup Saf Ergon. 2023 Dec;29(4):1429-1439. doi: 10.1080/10803548.2022.2135281. Epub 2022 Dec 10. PMID: 36281493.
3. Kuo CF, Tsai CY, Cheng WH, Hs WH, Majumdar A, Stettler M, Lee KY, Kuan YC, Feng PH, Tseng CH, Chen KY, Kang JH, Lee HC, Wu CJ, Liu WT. Machine learning approaches for predicting sleep arousal response based on heart rate variability, oxygen saturation, and body profiles. Digit Health. 2023 Oct 13;9:20552076231205744. doi: 10.1177/20552076231205744. PMID: 37846406; PMCID: PMC10576931.
4. Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digit Health. 2023 Mar 6;9:20552076231152751. doi: 10.1177/20552076231152751. PMID: 36896329; PMCID: PMC9989412.
5. Tsai CY, Kuan YC, Hsu WH, Lin YT, Hsu CR, Lo K, Hsu WH, Majumdar A, Liu YS, Hsu SM, Ho SC, Cheng WH, Lin SY, Lee KY, Wu D, Lee HC, Wu CJ, Liu WT. Differentiation Model for Insomnia Disorder and the Respiratory Arousal Threshold Phenotype in Obstructive Sleep Apnea in the Taiwanese Population Based on Oximetry and Anthropometric Features. Diagnostics (Basel). 2021 Dec 27;12(1):50. doi: 10.3390/diagnostics12010050. PMID: 35054218; PMCID: PMC8774350.
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| Insomnia disorder (ID) and obstructive sleep apnea (OSA) with respiratory arousal threshold (ArTH) phenotypes often coexist in patients, presenting similar symptoms. However, the typical diagnosis examinations (in-laboratory polysomnography (lab-PSG) and other alternatives methods may therefore have limited differentiation capacities. Hence, this study established novel models to assist in the classification of ID and low- and high-ArTH OSA. Participants reporting insomnia as their chief complaint were enrolled. Their sleep parameters and body profile were accessed from the lab-PSG database. Based on the definition of low-ArTH OSA and ID, patients were divided into three groups, namely, the ID, low- and high-ArTH OSA groups. Various machine learning approaches, including logistic regression, k-nearest neighbors, naive Bayes, random forest (RF), and support vector machine, were trained using two types of features (Oximetry model, trained with oximetry parameters only; Combined model, trained with oximetry and anthropometric parameters). In the training stage, RF presented the highest cross-validation accuracy in both models compared with the other approaches. In the testing stage, the RF accuracy was 77.53% and 80.06% for the oximetry and combined models, respectively. The established models can be used to differentiate ID, low- and high-ArTH OSA in the population of Taiwan and those with similar craniofacial features. |