Sleep condition detection and assessment with optical fiber interferometer based on machine learning

Abstract

The prevalence of sleep disorders has increased because of the fast-paced and stressful modern lifestyle, negatively impacting the quality of human life and work efficiency. It is crucial to address sleep problems. However, the current practice of diagnosing sleep disorders using polysomnography (PSG) has limitations such as complexity, large equipment, and low portability, hindering its practicality for daily use. To overcome these challenges, in this article an optical fiber sensor is proposed as a viable solution for sleep monitoring. This device offers benefits like low power consumption, non-invasiveness, absence of interference, and real-time health monitoring. We introduce the sensor with an optical fiber interferometer to capture ballistocardiography (BCG) and electrocardiogram (ECG) signals from the human body. Furthermore, a new machine learning method is proposed for sleep condition detection. Experimental results demonstrate the superior performance of this architecture and the proposed model in monitoring and assessing sleep quality.

Keywords: Fiber optics; Health technology; Optics.

Graphical abstract

Figure 1

Overall structure of the experiment setup

Figure 2

The proposed optical fiber sensor system

Figure 3

Detail information about the proposed monitoring system

Figure 4

Basic structure of the GRNN model

Figure 5

Basic structure of the RBF model

Figure 6

The mechanism and illustration of sleep staging

Figure 7

Flow chart of GRNN building for sleep staging and assessment

Figure 8

Sleep stage detection and assessment results based on proposed LSVM model 8 h of raw ECG signal was obtained and used in experiments (class 1: deep sleep; class 2: light sleep). (A), (B), (C), (D), (E), (F), (G), and (H) refer to the detection and assessment results of sleep stages for each hour within 8 h, respectively. In every figure, the blue-green area contains class 1, and the purplish red area contains class 2. The size of different colored areas represents the proportion of different sleep stages during that time period.

Figure 9

Sleep stage detection and assessment results based on RBF model 8 h of raw ECG signal was obtained and used in experiments (class 1: deep sleep; class 2: light sleep). (A), (B), (C), (D), (E), (F), (G), and (H) refer to the detection and assessment results of sleep stages for each hour within 8 h, respectively. In every figure, the blue-green area contains class 1, and the purplish red area contains class 2. The size of different colored areas represents the proportion of different sleep stages during that time period.

Figure 10

Sleep stage detection and assessment results based on GRNN model (0: WAKE; 1: S1; 2: S2; 3: S3; 4: S4; 5: REM) (A) 4 different brainwaves extracted from raw vital signs signal; (B) Sleep stage detection and raw vital signs signal.