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Cardiorespiratory Model-Based Data-Driven Approach for Sleep Apnea Detection

IEEE journal of biomedical and health informatics, 2018-07, Vol.22 (4), p.1036 [Tạp chí có phản biện]

EISSN: 2168-2208 ;DOI: 10.1109/JBHI.2017.2740120 ;PMID: 28816683

Tài liệu số/Tài liệu điện tử

  • Nhan đề:
    Cardiorespiratory Model-Based Data-Driven Approach for Sleep Apnea Detection
  • Tác giả: Gutta, Sandeep ; Cheng, Qi ; Nguyen, Hoa Dinh ; Benjamin, Bruce A
  • Chủ đề: Aged ; Algorithms ; Electrocardiography - methods ; Female ; Humans ; Male ; Middle Aged ; Models, Cardiovascular ; Oxygen - blood ; Photoplethysmography ; Signal Processing, Computer-Assisted ; Sleep Apnea Syndromes - diagnosis
  • Là 1 phần của: IEEE journal of biomedical and health informatics, 2018-07, Vol.22 (4), p.1036
  • Mô tả: Obstructive sleep apnea (OSA) is a chronic sleep disorder affecting millions of people worldwide. Individuals with OSA are rarely aware of the condition and are often left untreated, which can lead to some serious health problems. Nowadays, several low-cost wearable health sensors are available that can be used to conveniently and noninvasively collect a wide range of physiological signals. In this paper, we propose a new framework for OSA detection in which we combine the wearable sensor measurement signals with the mathematical models of the cardiorespiratory system. Vector-valued Gaussian processes (GPs) are adopted to model the physiological variations among different individuals. The GP covariance is constructed using the sum of separable kernel functions, and the GP hyperparameters are estimated by maximizing the marginal likelihood function. A likelihood ratio test is proposed to detect OSA using the widely available heart rate and peripheral oxygen saturation (SpO ) measurement signals. We conduct experiments on both synthetic and real data to show the effectiveness of the proposed OSA detection framework compared to purely data-driven approaches.
  • Nơi xuất bản: United States
  • Ngôn ngữ: English
  • Số nhận dạng: EISSN: 2168-2208
    DOI: 10.1109/JBHI.2017.2740120
    PMID: 28816683
  • Nguồn: MEDLINE
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