Sleep staging detection method and wearable sleep staging detection device

Abstract

The invention discloses a sleep staging detection method. The sleep staging detection method comprises the steps that human's heart rate variability signals and wrist pulse triaxial acceleration dataare collected; the heart rate variability signals and the triaxial acceleration data are partitioned according to time spans, a plurality of feature parameters are extracted from the heart rate variability signals and the triaxial acceleration data in each time span; the feature parameters in one time span are input into a sleep staging detection model, and a sleep stage in one time span is obtained; the sleep stages in all the time spans are counted so as to obtain the sleep stages in the whole sleep time. The invention further discloses a wearable sleep staging detection device. In the sleepstaging detection method and the wearable sleep staging detection device, the wrist pulse acceleration information and the heart rate variability signals in the sleep process of the human body are integrated, the sleep staging accuracy can be improved, and the sensitivity can be enhanced.

Description

technical field [0001] The invention relates to a sleep staging method and a staging detection device, in particular to a sleep staging detection method and a wearable sleep staging detection device. Background technique [0002] Sleep is an important physiological activity that plays a key role in the physical and spiritual self-recovery of the human body. In recent years, with the acceleration of the pace of society, people's work and life pressures are increasing day by day, and the decline in sleep quality has become a problem faced by many people, seriously affecting their physical and mental health. [0003] The basis and premise of evaluating sleep quality is sleep staging. According to the specifications issued by the American Academy of Sleep Medicine (AASM), the sleep process can be divided into wakefulness (Wakefulness, W), non-rapid eye movement (non-rapid eye movement, NREM) and rapid eye movement. period (rapid eye movement, REM). Among them, the NREM stage ...

AI Technical Summary

Problems solved by technology

However, heart rate variability parameters mainly include time domain, frequency domain and nonlinear parameters, and only some parameters have been confirmed to be correlated with sleep staging. The existing automatic sleep staging method based on heart rate variability is more accurate than PSG. rate is not very high

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