A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal data using Ultra-wideband Radar and proposed 1D CNN (1-Dimension Convolutional Neural Network) model that can classify and recognize five respiration patterns (Eupnea, Bradypnea, Tachypnea, Apnea, and Motion) from the signal data. Also, in the proposed model, we find the optimum parameter range through the recognition rate experiment on the combination of parameters (layer depth, size of kernel, and number of kernels). The average recognition rate of five breathing patterns experimented by applying the proposed method was 93.9%, which is about 3%~13% higher than that of conventional methods (LDA, SVM, and MLP).

[1]  S. Schumann,et al.  Compensating Artificial Airway Resistance via Active Expiration Assistance , 2016, Respiratory Care.

[2]  Timothy H Gould,et al.  Principles of artificial ventilation , 2004 .

[3]  E. M. Staderini,et al.  UWB radars in medicine , 2002 .

[4]  Adel Al-Jumaily,et al.  Doppler Radar-Based Non-Contact Health Monitoring for Obstructive Sleep Apnea Diagnosis: A Comprehensive Review , 2019, Big Data Cogn. Comput..

[5]  F. Sebat,et al.  Respiratory Rate: The Forgotten Vital Sign-Make It Count! , 2018, Joint Commission journal on quality and patient safety.

[6]  Shyamnath Gollakota,et al.  Contactless Sleep Apnea Detection on Smartphones , 2015, GetMobile Mob. Comput. Commun..

[7]  Mary Ann Weitnauer,et al.  Towards Sleep Apnea Screening with an Under-the-Mattress IR-UWB Radar Using Machine Learning , 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

[8]  Jin-Young Ha,et al.  Snoring detection using a piezo snoring sensor based on hidden Markov models , 2013, Physiological measurement.

[9]  Jae-Young Pyun,et al.  Location Detection and Tracking of Moving Targets by a 2D IR-UWB Radar System , 2015, Sensors.

[10]  W. M. Anderson,et al.  Clinical guidelines for the use of unattended portable monitors in the diagnosis of obstructive sleep apnea in adult patients. Portable Monitoring Task Force of the American Academy of Sleep Medicine. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[11]  J. Solet,et al.  Measuring sleep: accuracy, sensitivity, and specificity of wrist actigraphy compared to polysomnography. , 2013, Sleep.

[12]  Juhan Nam,et al.  Sample-Level CNN Architectures for Music Auto-Tagging Using Raw Waveforms , 2017, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Mauro Biagi,et al.  Sleep-Apnea Detection with UWB Active Sensors , 2015, 2015 IEEE International Conference on Ubiquitous Wireless Broadband (ICUWB).

[14]  Xinming Huang,et al.  Real-time non-contact infant respiratory monitoring using UWB radar , 2015, 2015 IEEE 16th International Conference on Communication Technology (ICCT).

[15]  Ismail Guvenc,et al.  UWB radar for indoor detection and ranging of moving objects: An experimental study , 2016, 2016 International Workshop on Antenna Technology (iWAT).

[16]  Ramon Villarino,et al.  Wireless Wearable Magnetometer-Based Sensor for Sleep Quality Monitoring , 2018, IEEE Sensors Journal.

[17]  Identification of different respiratory rate by a piezo polymer based nasal sensor , 2013, 2013 IEEE SENSORS.

[18]  Changzhi Li,et al.  A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring , 2013, IEEE Transactions on Microwave Theory and Techniques.

[19]  Paolo Bernardi,et al.  Design, Realization, and Test of a UWB Radar Sensor for Breath Activity Monitoring , 2014, IEEE Sensors Journal.

[20]  Monji Kherallah,et al.  A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition , 2016, ICCS.

[21]  Sung Bum Pan,et al.  Deep Learning Based on 1-D Ensemble Networks Using ECG for Real-Time User Recognition , 2019, IEEE Transactions on Industrial Informatics.

[22]  Xiaogang Wang,et al.  Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  David Girbau,et al.  ANALYSIS OF VITAL SIGNS MONITORING USING AN IR-UWB RADAR , 2010 .

[24]  R. Berry,et al.  Portable monitoring and autotitration versus polysomnography for the diagnosis and treatment of sleep apnea. , 2008, Sleep.

[25]  Ludger Grote,et al.  Validation a portable monitoring device for sleep apnea diagnosis in a population based cohort using synchronized home polysomnography. , 2006, Sleep.

[26]  R.S. Thoma,et al.  UWB short-range radar sensing - The architecture of a baseband, pseudo-noise UWB radar sensor , 2007, IEEE Instrumentation & Measurement Magazine.

[27]  Karen Ritchie,et al.  Sleep and depression. , 2005, The Journal of clinical psychiatry.

[28]  Sang Min Yoon,et al.  Human activity recognition from accelerometer data using Convolutional Neural Network , 2017, 2017 IEEE International Conference on Big Data and Smart Computing (BigComp).

[29]  Syed Aziz Shah,et al.  Breathing Rhythm Analysis in Body Centric Networks , 2018, IEEE Access.

[30]  Ying Chen,et al.  Guest Editorial Wireless Sensing Circuits and Systems for Healthcare and Biomedical Applications , 2018, IEEE J. Emerg. Sel. Topics Circuits Syst..

[31]  Sang Min Yoon,et al.  Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening † , 2018, Sensors.

[32]  Asaf Shabtai,et al.  Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks , 2018, CPS-SPC@CCS.

[33]  Weiming Tian,et al.  Accurate Analysis of Target Characteristic in Bistatic SAR Images: A Dihedral Corner Reflectors Case , 2017, Sensors.

[34]  S. Venkatesh,et al.  Implementation and analysis of respiration-rate estimation using impulse-based UWB , 2005, MILCOM 2005 - 2005 IEEE Military Communications Conference.

[35]  Srinivasan Murali,et al.  Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors , 2018, IEEE Transactions on Biomedical Circuits and Systems.