Rapid eye movement period detection method based on thoracico-abdominal respiration signals in sleep period

Abstract

The invention provides a rapid eye movement period detection method based on chest-abdomen type respiration signals in a sleep period, which comprises the following steps of: calculating correlation between chest respiration signals and abdomen respiration signals, particularly calculating sliding window correlation coefficients of the two respiration signals; and calculating a mean value and a standard deviation of the correlation coefficient sequence to detect the rapid eye movement period.

Description

Technical Field

[0001] The present application relates to sleep staging technology, and in particular to a rapid eye movement detection method based on chest and abdominal breathing signals during sleep. Background Art

[0002] Sleep is an important physiological process that is indispensable in human life. Rapid Eye Movement (REM), as a special stage of sleep, has unique physiological and psychological characteristics and has an important impact on human physical and mental health and cognitive function. In-depth research on REM is of great significance for understanding sleep mechanisms, diagnosis and treatment of sleep disorders, and cognitive neuroscience.

[0003] For the detection of REM period, polysomnography (PSG) is currently one of the most commonly used and accurate methods for marking REM period. It places multiple electrodes on the head, face, body and other parts of the sleeper, and simultaneously records multiple physiological signals such as electroencephalogram (EEG), electrooculogram (EOG), electromyography, electrocardiography, breathing, etc. According to the standards of the American Academy of Sleep Medicine (AASM), the marking of REM period usually needs to meet the following conditions at the same time: EEG shows low-voltage, mixed-frequency EEG activity, EOG detects rapid eye movements, and the mandibular EEG activity is at a low level, indicating muscle relaxation. However, PSG testing needs to be carried out in a professional sleep laboratory, and the subjects need to sleep in an unfamiliar environment, which may affect the sleep quality and the accuracy of the test results; the equipment is complex and the operation is cumbersome, requiring professional technicians to operate and analyze the data; the test cost is high and time-consuming, and generally requires the subjects to be monitored overnight in the laboratory.

[0004] In addition to PSG devices, some studies have used wearable devices and EEG monitoring devices to detect REM. Among them, wearable devices use sensors such as accelerometers and gyroscopes to monitor the body's movement and posture changes, combined with data such as heart rate changes monitored by heart rate sensors, and use algorithms to infer sleep stages, including rapid eye movement. This method is easy to implement, and subjects can monitor sleep in a familiar environment without affecting their daily lives; it can continuously monitor sleep data for multiple days and provide long-term sleep information. However, the algorithm in this method is mainly based on big data models and statistical laws, and has limited adaptability to individual differences. In addition, for EEG monitoring devices, the principle is to analyze sleep status by collecting scalp EEG signals. During the rapid eye movement period, EEG activity will show specific frequency and waveform changes, such as low-amplitude, mixed-frequency EEG signals, etc., and these features are identified to determine whether it is in the rapid eye movement period. Compared with wearable devices, EEG monitoring devices have higher accuracy in detecting sleep stages, especially for the judgment of rapid eye movement period, which has a better reference value; it can monitor the changes in EEG signals in real time, providing a direct basis for studying brain activity during sleep. However, there are similarities between the EEG signals during REM and those during wakefulness in terms of frequency characteristics and EEG activities in eye movement-related brain areas.

[0005] If there were a more convenient and simple method for accurately detecting and identifying the REM phase, it would be of great significance for research and clinical applications in the fields of sleep health and neuroscience. Summary of the invention

[0006] The present application aims to propose a rapid eye movement detection method.

[0007] The rapid eye movement detection method based on chest and abdominal breathing signals during sleep of the present application includes: The correlation between the chest breathing signal and the abdominal breathing signal of the subject is calculated, and the rapid eye movement period of the subject is determined by quantitative evaluation of the correlation.

[0008] Preferably, the correlation is represented by a correlation coefficient or a cross-correlation.

[0009] Preferably, the correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

[0010] Preferably, the quantitative evaluation of the correlation is performed by at least one of the following methods: mean, standard deviation, short-time zero-crossing rate, short-time relative energy, median, and quartile.

[0011] Preferably, the quantitative assessment of the correlation is performed by means of mean and standard deviation.

[0012] Preferably, when the mean of the correlation coefficient is less than 0.5 and the standard deviation is greater than 0.5, the sleep stage is determined to be the rapid eye movement stage.

[0013] The present application determines the REM period by utilizing the correlation between the chest breathing signal and the abdominal breathing signal and the correlation between the REM period, and can significantly distinguish the REM period from other sleep stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 These are the chest and abdominal respiratory signals corresponding to different sleep stages and their short-term correlation coefficients. DETAILED DESCRIPTION

[0015] Figure 1 In the figure, a is the chest breathing signal during sleep, b is the abdominal breathing signal during sleep, a and b are signals collected synchronously during sleep, c is the sliding window correlation coefficient sequence of the two breathing signals a and b, specifically, by adding sliding windows to the chest breathing signal and the abdominal breathing signal respectively (the window size is 10 seconds, and the sliding window step is 1 second), the correlation coefficient between the two breathing signals in different windows is calculated to form a correlation coefficient sequence, i.e., the waveform data in c; d and e are the mean time series and standard deviation time series of the sliding window of the correlation coefficient sequence c (the window size is 100 seconds, and the sliding window step is 1 second), f is the difference between the mean time series and the standard deviation time series, g is the manually labeled sleep stage, which is the clinical gold standard for sleep staging, and the red part is the manually labeled REM stage.

[0016] observe Figure 1 In c, d, and e, it is found that during REM staging, the correlation coefficient of chest and abdominal respiratory signals tends to decrease as a whole, and the fluctuation amplitude is large. Based on this, the mean d (indicating a small amplitude) and standard deviation e (indicating a large fluctuation) of the correlation coefficient corresponding to the REM staging process are calculated, and the difference between the two is taken to detect the REM staging. On this basis, the threshold value of the difference sequence f is set to 0.5. When the value of the sequence in f is less than the threshold, the sleep stage is judged to be REM staging.

[0017] The above findings indicate that the correlation between chest and abdominal respiratory signals during REM is significantly different from that of other sleep stages. This correlation can be expressed by correlation coefficients (Pearson correlation coefficient, Spearman correlation coefficient) and cross-correlation. For quantitative evaluation of correlation, the method can be one or more of the mean, standard deviation, short-term zero-crossing rate, short-term relative energy, median, quartiles, etc.

[0018] Unless otherwise defined, all technical and / or scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which the invention relates. The materials, methods and examples mentioned in this application are illustrative only and not limiting.

[0019] Although the present invention has been described in conjunction with specific embodiments, within the spirit of the invention of this application, those skilled in the art may make appropriate substitutions, modifications and changes, and such substitutions, modifications and changes still fall within the scope of protection of this application.

Claims

1. A rapid eye movement detection method based on chest and abdominal breathing signals during sleep, comprising: The correlation between the chest breathing signal and the abdominal breathing signal of the subject is calculated, and the rapid eye movement period of the subject is determined by quantitative evaluation of the correlation.

2. The rapid eye movement detection method based on chest and abdominal breathing signals during sleep according to claim 1, characterized in that: The correlation is expressed by a correlation coefficient or a cross-correlation.

3. The rapid eye movement detection method based on chest and abdominal breathing signals during sleep according to claim 2, characterized in that: The correlation coefficient is a Pearson correlation coefficient or a Spearman correlation coefficient.

4. The rapid eye movement detection method based on chest and abdominal breathing signals during sleep according to claim 1, characterized in that: The quantitative evaluation of the correlation is performed by at least one method selected from the group consisting of mean, standard deviation, short-time zero-crossing rate, short-time relative energy, median, and quartile.

5. The rapid eye movement detection method based on chest and abdominal breathing signals during sleep according to claim 4, characterized in that: Quantitative assessment of the correlation was performed using mean and standard deviation.

6. The rapid eye movement detection method based on chest and abdominal breathing signals during sleep according to claim 5, characterized in that: When the mean of the correlation coefficient is less than 0.5 and the standard deviation is greater than 0.5, the sleep stage is judged to be REM.

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