# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
###### Abstract  
The paper proposes a unsupervised method for detecting the three stages of sleep--wake, rapid eye movement (_REM_) sleep, and non-_REM_ sleep--using biosignals obtained from a 61 GHz single frequency modulated continuous wave (FMCW) radar. To detect the subject's sleep stages based on non-learning techniques, the breathing and movement information characteristic of each sleep stage was extracted from the radar signals of the subject acquired in the sleep state and used as the feature factor tailored to the research objective. The experimental results derived from the clinical data obtained in the actual polysomnography (PSG) environment using FMCW radar show an average of 68% similarity to the actual three sleep stages observed in PSG. These results indicate the feasibility of using the FMCW radar sensor as an alternative to the conventional PSG-based method that poses multiple limitations to sleep-stage detection. sleep stage detection; contactless; non-learning; vital detection; signal processing; FMCW radar +
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in sleep-related clinical studies. When the device cannot be worn on the wrist, it can be worn on the torso or ankle [10]. In addition, this technique allows an easy distinction between the wake and sleep states of a target based on the acceleration generated by its movement [11]. However, this technique has low reliability. Errors due to the subject's wearing conditions and hand dominance influence the sleep stage categorization results, making it difficult to identify detailed sleep stages, such as the rapid eye movement (_REM_) sleep stage [12]. Moreover, distinguishing the _REM_ sleep stage using motion sensors that detect the awake state based on the subject's body movements is difficult because additional biometric information, such as breathing and heart rate, is not easily acquired [13; 14]. Unlike acti-graph devices and motion sensors that detect only the body part(s) equipped with sensors, pressure sensors, which can measure the subject's overall movement and provide additional biometric information, such as breathing and heart rate, can be used for continuous data collection in the form of an ordinary bed. However, pressure sensors are expensive, and sleep-stage results vary depending on the subject's weight [15; 16]. In addition, acoustic sensors, which identify the sleep stage by collecting sound information caused by the subject's breathing and snoring during the test, have low measurement accuracy because of their inability to utilize the subject's motion information and the influence of noise generated by the surroundings [17]. Contactless sleep-stage detection techniques based on various radar sensors have been proposed to monitor sleep state in a more natural environment by resolving the limitations of the contact measurements involved in PSG and addressing the shortcomings of the methods introduced in previous studies. In existing studies on radar-based sleep-stage detection, various learning methods with biometric features extracted using CW radar sensors have been used to divide the sleep state into three (wake, _REM_, and NREM) [18; 19] or four stages (wake, _REM_, light sleep, and deep sleep) [20; 21]. In addition, sleep-stage-detection studies have been conducted using biometric information extracted from additional equipment, such as acoustic sensors and radar sensors, as machine learning feature factors to improve detection accuracy [22]. In addition, some studies have used UWB radar sensors to classify sleep states into three stages [23; 24]. However, these methods have limitations because of the significant amount of signal processing computation required owing to the nature of sleep tests that measure data overnight. Moreover, some studies have used two or nine radar sensors to identify sleep stages, using machine learning, where detection and classification accuracy improve as the number of feature factors increases [25; 26].