Development of a sleep stage assessment index based on heart rate variability
Date Issued
2015
Date
2015
Author(s)
Wu, Ching-Yun
Abstract
Sleeping well brings good quality of life, because human body does most of its repair and regeneration work in sleeping. Without sufficient sleep for a long time, many diseases may occur, such as, hypersomnia, melancholia, memory decrease, sexual dysfunction, cardiovascular diseases, stroke, diabetes, and cancers. Thus, it is necessary and helpful to have a better understanding of sleep. Sleep stages are largely monitored and determined by polysomnography (PSG). The PSG is generally administrated by sleep centers in large hospitals, such as the National Taiwan University Hospital. Not only is taking PSG expensive, but also is the waiting line long (patients generally have to wait for two or three months). Another major disadvantage is that subjects have to wear many sensors to collect vital signs, such as electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), and blood pressure. This may lead to some misleading results caused by uncomfortable factors (e.g. getting nervous in the hospital and wearing many sensors). The ECG is a basic vital sign. Related to sleep stages, heart rate variability (HRV), can represent the parasympathetic activities of the autonomic nervous system (ANS). Therefore, this research creates an algorithm to extract the HRV from the ECG signals to acquiring the sleep stage assessment index (SSAI). Finally, the SSAI is used to determine sleep stages using the relationship between the SSAI and sleep stages. This research also utilizes the data from the PhysioNet database to verify the HRV algorithm and the process of calculating SSAI. The physical data of 32 subjects (19 subjects with sleep apnea and 13 healthy subjects) are drawn from the database. Then, the data are divided into three phases: wake, light sleep (sleep stage 1 and stage 2), and deep sleep (sleep stage 3 and stage 4) according to hypnogram. The relationship between SSAI and sleep stags is explored through analyzing the data from PhysioNet. It is found that the SSAI is positively correlated with the three sleep phases (wake, light sleep, and deep sleep). The SSAI increases when people enter the phase of deep sleep from the phase of wake. Additionally, a Wilcoxon non-parametric statistical test is employed to determine the usefulness of the SSAI. In conclusion, the SSAI is proven to be a good reference index to inspecting sleep stages (p < 0.05). The SSAI is compared with the high frequency of HRV (HF) which has been verified as a sleep stage assessment index to examine the reliability of SSAI. The results show that SSAI could serve as a sleep stage assessment index, like HF. The correlation between SSAI and HF is moderate in wake (r = 0.6) and high in light sleep and deep sleep (r > 0.9). Moreover, SSAI is applied to healthy people, patients with sleep apnea and patients with hypersomnia. It finds that SSAI can successfully determine three phases of sleep for healthy people and patients with sleep apnea (p < 0.05). Moreover, SSAI can determine wake and sleep for the patients with hypersomnia. However, SSAI scores of light sleep and deep sleep are undifferentiated in patients with hypersomnia, because the Epworth Sleepiness Score (ESS) is a self-appraisal questionnaire. Therefore, the results might be affected by personal opinions.
Subjects
Electrocardiography (ECG)
Heart rate variability (HRV)
Polysomnography (PSG)
Hypnogram
sleep stage assessment index (SSAI)
SDGs
Type
thesis
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