# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 4 Experimental Results
### Accuracy Analysis  
As determining the stage-specific risks associated with radar-based sleep-stage detection results was not the research objective, accuracy was evaluated using the ratio of correctly identified sleep stages to all cases. Table 2 lists the average accuracy of the proposed sleep-stage detection algorithm tested on 85 subjects. The detection accuracies for the awake, _REM_ sleep, and non-_REM_ sleep stages were 64.37%, 83.51%, and 58.98%, respectively. The average detection accuracy for all three stages was 68.91%.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Center Frequency & 60 GHz \\ Chirp duration & 300 \(\upmu\)s \\ Sampling Frequency & 1 MHz \\ Scan interval & 100 ms \\ Bandwidth & 3.75 GHz \\ Number of Tx antenna & 1 \\ Number of Rx antenna & 3 \\ \hline \end{tabular}
\end{table}
Table 1: FMCW radar specifications.  
Figure 7: Three-stage sleep-detection results using FMCW radar. (**a**) Subject 1. (**b**) Subject 2.  
The previous research [27] used a pulse-based IR-UWB radar for sleep-stage detection. Although IR-UWB radar has merits in identifying targets behind objects, it has a limited transmission power compared to FMCW radar, and its design is very complicated and the cost greatly increases due to a high-speed analog to digital converter (ADC). In addition, although the detection accuracy in [18; 20; 24; 25; 26] is higher than that of the proposed method, those were required a training procedure for sleep-stage detection. Ref. [27] used an unsupervised method, but it focused on neonates and detected only sleep and wake stages without considering the _REM_ stage. In the _REM_ sleep stage, muscle atony increases and causes sleep breathing disorders, such as sleep apnea and hypo-apnea, so it is an important stage that needs to be detected in sleep monitoring research [36]. Although the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Radar in** & **Evaluation** & **Number of** & **Use of** & **Detection** \\  & **Application** & **Criteria** & **Stage** & **Learning-Based** & **Accuracy [\%]** \\ \hline
[18] & CW Doppler & Accuracy & 4 & O & W/R/L/D \\  & & & & & 81.8/85.5/78.6/81.2 \\ \hline
[23] & IR UWB & Accuracy & 4 & O & Four-stage Average 82.6 \\ \hline
[20] & CW Doppler & Precision & & & \\  & & Recall & 3 & O & W/S-(P)86.0/(R)86.5/(F)86.2 \\  & & F1-Score & & & R/NR-(P)75.5/(R)75.4/(F)75.8 \\ \hline
[24] & IR UWB & Accuracy & 3 & O & Three-Stage Average 72.93 \\ \hline
[35] & Bio & Precision & 3 & O & Three-Stage Average 75.13 \\ \hline
[25] & Micro Doppler & Precision & 3 & O & Three-Stage Average 68.10 \\ \hline
[26] & Micro Doppler & Accuracy & 3 & O & Three-Stage Average 57.10 \\ \hline
[27] & IR UWB & Recall & 2 & X & Three-Stage Average 75.00 \\ \hline Proposed & FMCW & Accuracy & 3 & X & Three-Stage Average 68.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sleep-stage-detection accuracy.  
Figure 8: Labeling in sleep-stage-detection results. (**a**) Sleep-stage-detection results before labeling. (**b**) Sleep-stage-detection results after labeling.  
accuracy of the proposed method is not high compared with those of conventional ones, the proposed method is valuable, in that it detects three stages, including _REM_ sleep, in an unsupervised manner.