Non-Contact Respiratory Rate Detection for Sleep Apnea Analysis Using Xethru X4M200 Radar Sensor

Sleep apnea is a sleep disorder that disrupts breathing patterns during rest. A key indicator of this condition is the sudden cessation of breathing, which may occur multiple times during sleep, often without the individual realizing it. The disorder is typically linked to issues in the respiratory system and can have severe health consequences if left untreated, including fatigue, cardiovascular problems, and decreased cognitive performance.

Respiratory rate (RR)—the number of breaths per minute—is a crucial parameter in detecting abnormalities in breathing patterns, such as bradypnoea (abnormally slow breathing) and tachypnoea (abnormally fast breathing). RR varies significantly among individuals, influenced by factors such as body mass index (BMI) and sleep position. Understanding these variations is essential for accurate and early detection of sleep apnea.

Traditionally, Polysomnography (PSG) has been the gold standard for sleep apnea diagnosis. However, PSG is intrusive, requiring multiple sensors to be attached to the body, often leading to patient discomfort and potentially affecting the accuracy of the test.

To overcome these limitations, this study proposes the use of the Xethru X4M200 radar sensor as a non-contact method for detecting RR using the Doppler effect. The objective is to evaluate the effectiveness of this radar-based technique in monitoring RR, and to explore its correlation with sleep position and BMI.

Background

Sleep apnea can be classified into three types:

1. Obstructive Sleep Apnea (OSA) – caused by blockage of the airway.

2. Central Sleep Apnea (CSA) – due to lack of respiratory effort.

3. Complex Sleep Apnea – a combination of OSA and CSA.

The occurrence and severity of apnea events are influenced by anatomical and physiological factors, including obesity, which is represented by a higher BMI. Additionally, sleep posture—such as supine (lying on the back), prone (lying on the stomach), left lateral, and right lateral—can influence the RR by altering the mechanics of the chest and diaphragm.

In recent years, non-contact respiratory monitoring systems have gained attention as more comfortable alternatives. Radar sensors such as the Xethru X4M200 utilize ultra-wideband (UWB) technology to detect minute movements, such as chest expansion and contraction, allowing for precise RR monitoring without physical contact.

Methodology

Equipment Used

• Xethru X4M200 Radar Sensor: A UWB radar device capable of detecting micro-movements using the Doppler effect. It operates in the frequency range of 7.25 to 10.2 GHz and is sensitive enough to detect chest movements caused by breathing.

Participants

• 20 participants: 10 males and 10 females

• BMI values: Varied to include underweight, normal, overweight, and obese individuals

• Age range: 20–35 years

Procedure

Participants were asked to lie down in four different sleep positions:

1. Supine

2. Prone

3. Left lateral

4. Right lateral

For each position, the Xethru X4M200 was positioned above the participant’s chest at a fixed height. The sensor captured chest movement data, and RR was extracted through signal processing based on phase modulation caused by the Doppler effect.

Data was analyzed to:

• Determine RR for each sleep position

• Compare RR across different BMI categories

• Identify signs of bradypnoea or tachypnoea

Results and Discussion

1. RR Variation by Sleep Position

• Supine position generally exhibited a higher RR, likely due to airway constriction and increased breathing effort.

• Prone position showed a lower RR, possibly due to better lung expansion.

• Lateral positions (left and right) offered more balanced and stable breathing, especially for overweight participants.

2. RR and BMI Correlation

• Higher BMI individuals tended to have link lower RR in prone and supine positions, likely due to increased chest wall resistance.

• Normal BMI individuals exhibited consistent RR across all sleep positions.

• The radar was effective in detecting abnormal breathing in obese individuals, potentially aiding in early apnea detection.

3. Sensor Performance

The Xethru X4M200 successfully detected chest micro-movements across all participants and sleep positions. The non-contact nature of the radar offered the following benefits:

• Comfort: No electrodes or wearable sensors required.

• Real-time monitoring: Instant RR feedback.

• Detection of breathing abnormalities: Radar data showed clear signs of slowed or rapid breathing episodes, suggesting the presence of bradypnoea or tachypnoea.

Limitations and Future Work

Although promising, this study has limitations:

• The sample size of 20 participants is relatively small for comprehensive conclusions.

• Environmental noise and movement artifacts may affect radar signal accuracy.

• The current system does not automatically diagnose sleep apnea, only detects RR trends.

Future research directions include:

• Expanding the participant pool to cover a wider range of ages and medical conditions

• Developing machine learning algorithms for apnea event detection based on radar signals

• Integrating radar data with other non-contact sensors (e.g., thermal cameras) to enhance diagnostic accuracy

Conclusion

This study demonstrates that the Xethru X4M200 radar sensor can serve as a non-contact, Doppler-based respiratory monitoring tool for detecting RR. The results highlight a clear relationship between sleep position, BMI, and respiratory rate, offering potential for early detection of sleep apnea indicators.

The system’s ability to identify breathing irregularities without physical contact makes it a comfortable and practical alternative to traditional PSG. With further development, this technology could significantly enhance home-based sleep monitoring, benefiting both patients and healthcare providers.