Sleep disorders, such as insomnia can seriously impair a patient’s quali- ty of life. Existing studies have shown that insomniacs have a risk of hypertension 350 percent higher than normal sleepers. Insomnia is also a risk factor for diabe- tes, as well as anxiety and depression. Sleep measurements based on polysomnographic (PSG) signals and questionnaires are necessary for an accurate evaluation of insomnia; however PSG systems are uncomfortable and inconven- ient as they require patients to stay overnight at sleep centers. There is an increas- ing interest in portable devices, which provide the opportunity for the assessment of insomnia in a native environment (e.g. patients’ homes). Due to recent advanc- es in technology, it is now possible to continuously monitor a patient’s sleep at home and send their sleep data to a remote clinical back-end system for analysis and reporting. This chapter provides a systematic analysis on the sleep monitoring technologies that can be used for insomnia assessment and treatment. This study highlights the key technical challenges of sleep monitoring, describes different types of technologies and discusses their applications in insomnia assessment. An overview of some model-based signal processing for sleep staging and insomnia detection is presented. Lastly, this chapter ends with a discussion, which provides future directions for the deployment of effective in-home patient monitoring sys- tems for insomnia diagnosis.
|Place of Publication||Canberra|
|Publisher||University of Canberra|
|Number of pages||54|
|Publication status||Published - May 2016|