ApneaRadar: A 24GHz Radar-Based Contactless Sleep Apnea Detection System

Du, Na; Liu, Kewen; Ge, Linfei; Jin, Zhang

doi:10.1109/icfst.2017.8210539

Cited by 19 publications

(9 citation statements)

References 8 publications

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“…This is greatly helpful for reducing the number of contact biomedical sensors for monitoring of a patient over a long time. For example, to monitor sleep apnea, in [2] the authors proposed a remote controlling system, which uses a 24 GHz radar.…”

Section: Introductionmentioning

confidence: 99%

Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar

et al. 2019

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Electromagnetic radars have been shown potentially to be used for remote sensing of biosignals in a more comfortable and easier way than wearable and contact devices. While there is an increasing interest in using radars for health monitoring, their performance has not been tested and reported either in practical scenarios or with acceptable low errors. Therefore, we use a frequency modulated continuous wave (FMCW) radar operating at 77 GHz in a bedroom environment to extract the respiration and heart rates of a patient, who is used to lying down on the bed. Indeed, the proposed signal processing contains advanced phase unwrapping manipulation, which is unique. In addition, the results are compared with a reliable reference sensor. Our results show that the correlations between the reference sensor and the radar estimates are in 94% and 80% for breathing and heart rates, respectively.INDEX TERMS Breathing rate monitoring, FMCW radar, heart rate monitoring, Hexoskin, mm-wave, non-contact monitoring, phase analysis, remote sensing, vital signs, TI.

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Section: Introductionmentioning

confidence: 99%

Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar

et al. 2019

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“…Antenna transmitted electromagnetics signal reflected and received signal target to detect chest movement as known in Eq (2-3), where λ and d are the distance between the target and the radar and wavelength of the transmitted signal respectively and f and A T represent the frequency and amplitude of the transmitted signal. A signal reflected by the target (the doppler signal) is received and modulated by the receiver in equation (2), where the and values are as frequencies that transmit the signals and amplitude.…”

Section: Cw Radar System For Respirationmentioning

confidence: 99%

“…One of characteristic sleep apnea is breathing shallow suddenly stops for a moment during sleep. [2]. That causes blood pressure, stroke, fail-ure heart, heart attack, diabetes, and depression [2].…”

Section: Introductionmentioning

confidence: 99%

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Ultra Wideband Radar for Respiratory Monitoring on Sleep Position

Ali²,

Suratman³

2021

JMECS

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Sleep apnea is a sleep disorder that has a relation with respiratory system during sleep. One of the sleep apnea characteristic is suddenly stop breathing during sleep. People have the different of respiratory rate (RR) which is affected by sleep positions and body mass index (BMI).There are four sleep positions affecting the respiratory rate (RR). Polysomnography (PSG) is conventionally used to analysis the sleep apnea. This technique requires body contact that might be uncomfortable for the patient. In this study, the Xethru X4M200 radar sensor is proposed as non-contact tool to detect the RR by implementing the Doppler effect. Furthermore, the relation between RR with the sleep position and the BMI are discused. For that purpose, 20 participants (10 males and 10 females) with dierent BMIs and sleep positions are examined by monitoring their chest movement. This method is able to detect the indication of bradypnoea or tachypnoea. Futher systematic study and more participants are required to confirm our results and provide better non-contact technique for RR measurement.

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“…The type of studies used in our system is type 4 sleep studies, which also refers to continuous single bio-parameter or dual-bio parameter recording. The minimum number of signals that can be used in this type 4 studies is one or two channels such as oxygen saturation and airflow [3][4][5][6][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Normally type 4 studies do not have EEG and EMG signals, so scoring sleep is not possible.…”

Section: Introductionmentioning

confidence: 99%

Sleep apnea detection using deep learning

Chaw

Kamolphiwong

Wongsritrang³

2019

Teh. glas. (Online)

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Sleep apnea is the cessation of airflow at least 10 seconds and it is the type of breathing disorder in which breathing stops at the time of sleeping. The proposed model uses type 4 sleep study which focuses more on portability and the reduction of the signals. The main limitations of type 1 full night polysomnography are time consuming and it requires much space for sleep recording such as sleep lab comparing to type 4 sleep studies. The detection of sleep apnea using deep convolutional neural network model based on SPO2 sensor is the valid alternative for efficient polysomnography and it is portable and cost effective. The total number of samples from SPO2 sensors of 50 patients that is used in this study is 190,000. The performance of the overall accuracy of sleep apnea detection is 91.3085% with the loss rate of 2.3 using cross entropy cost function using deep convolutional neural network.

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ApneaRadar: A 24GHz Radar-Based Contactless Sleep Apnea Detection System

Cited by 19 publications

References 8 publications

Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar

Remote Monitoring of Human Vital Signs Using mm-Wave FMCW Radar

Ultra Wideband Radar for Respiratory Monitoring on Sleep Position

Sleep apnea detection using deep learning

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