Xianda Chen
Skip Abstract Section
Abstract
Sleep apnea is a sleep disorder in which breathing is briefly and repeatedly interrupted. Polysomnography (PSG) is the standard clinical test for diagnosing sleep apnea. However, it is expensive and time-consuming which requires hospital visits, specialized wearable sensors, professional installations, and long waiting lists. To address this problem, we design a smartwatch-based system called ApneaDetector, which exploits the built-in sensors in smartwatches to detect sleep apnea. Through a clinical study, we identify features of sleep apnea captured by smartwatch, which can be leveraged by machine learning techniques for sleep apnea detection. However, there are many technical challenges such as how to extract various special patterns from the noisy and multi-axis sensing data. To address these challenges, we propose signal denoising and data calibration techniques to process the noisy data while preserving the peaks and troughs which reflect the possible apnea events. We identify the characteristics of sleep apnea such as signal spikes which can be captured by smartwatch, and propose methods to extract proper features to train machine learning models for apnea detection. Through extensive experimental evaluations, we demonstrate that our system can detect apnea events with high precision (0.9674), recall (0.9625), and F1-score (0.9649).
- Heba Abdelnasser, Khaled A Harras, and Moustafa Youssef. 2015. UbiBreathe: A Ubiquitous Non-invasive WiFi-Based Breathing Estimator. In ACM MobiHoc.Google Scholar
- Shamma Alqassim, Madhumeta Ganesh, Shaheen Khoja, Meher Zaidi, Fadi Aloul, and Assim Sagahyroon. 2012. Sleep Apnea Monitoring using Mobile Phones. In IEEE Conf. on e-Health Networking, Applications and Services (Healthcom).Google ScholarCross Ref
- Heba Aly and Moustafa Youssef. 2016. Zephyr: Ubiquitous Accurate multi-Sensor Fusion-based Respiratory Rate Estimation Using Smartphones. In IEEE INFOCOM.Google Scholar
- American Sleep Association. April 2021. Sleep and Sleep Disorder Statistics. https://www.sleepassociation.org/about-sleep/sleep-statistics/.Google Scholar
- Mark B., Sally Middleton, Odette Erskine, Peter G. Middleton, John R. Wheatley, and Colin E. Sullivan. 2014. Validation of the Sonomat: a Contactless Monitoring System used for the Diagnosis of Sleep Disordered Breathing. Sleep Journal (2014).Google Scholar
- Liqiong Chang, Jiaqi Lu, Ju Wang, Xiaojiang Chen, Dingyi Fang, Zhanyong Tang, Petteri Nurmi, and Zheng Wang. 2018. SleepGuard: Capturing Rich Sleep Information using Smartwatch Sensing Data. In ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).Google Scholar
- Cleveland Clinic. April 2021. The Normal Respiration Rate for an Adult. https://my.clevelandclinic.org/health/articles/10881-vital-signs.Google Scholar
- Peter A. Deutsch, Michael S. Simmons, and Jeanne M. Wallace. 2006. Cost-Effectiveness of Split-night Polysomnography and Home Studies in the Evaluation of Obstructive Sleep Apnea Syndrome. Journal of Clinical Sleep Medicine (2006).Google Scholar
- Ignasi Ferrer-Lluis, Yolanda Castillo-Escario, Josep Maria Montserrat, and Raimon Jané. 2020. Analysis of Smartphone Triaxial Accelerometry for Monitoring Sleep-Disordered Breathing and Sleep Position at Home. IEEE access 8 (2020), 71231--71244.Google Scholar
- FitBit. April 2021. FitBit for Sleep Tracking. http://www.fitbit.com.Google Scholar
- Tracy Flenady, Trudy Dwyer, and Judith Applegarth. 2017. Accurate Respiratory Rates Count: So Should You! Australasian Emergency Nursing Journal (2017).Google Scholar
- Daniel Foley, Sonia Ancoli-Israel, Patricia Britz, and James Walsh. 2004. Sleep Disturbances and Chronic Disease in Older Adults: Results of the 2003 National Sleep Foundation Sleep in America Survey. Journal of Psychosomatic Research (2004).Google ScholarCross Ref
- Dana E Goin and Jennifer Ahern. 2019. Identification of spikes in time series. Epidemiologic Methods (2019).Google Scholar
- Weixi Gu, Longfei Shangguan, Zheng Yang, and Yunhao Liu. 2016. Sleep Hunter: Towards Fine Grained Sleep Stage Tracking with Smartphones. IEEE Trans. on Mobile Computing (2016).Google Scholar
- Tian Hao, Guoliang Xing, and Gang Zhou. 2013. iSleep: Unobtrusive Sleep Quality Monitoring using Smartphones. In ACM SenSys.Google Scholar
- Li Haoyu, Li Jianxing, N. Arunkumar, Ahmed Faeq Hussein, and Mustafa Musa Jaber. 2019. An IoMT Cloud-based Real Time Sleep Apnea Detection Scheme by using the SpO2 Estimation Supported by Heart Rate Variability. Future Generation Computer Systems (2019).Google Scholar
- Javier Hernandez, Daniel McDuff, and Rosalind W Picard. 2015. Biowatch: Estimation of Heart and Breathing Rates from Wrist Motions. In Int'l Conf. on Pervasive Computing Technologies for Healthcare (PervasiveHealth).Google Scholar
- H.M. Sajjad Hossain, Sreenivasan R. Ramamurthy, Md Abdullah Al Hafiz Khan, and Nirmalya Roy. 2018. An Active Sleep Monitoring Framework using Wearables. ACM Trans. on Interactive Intelligent Systems (TiiS) (2018).Google Scholar
- Chen-Yu Hsu, Aayush Ahuja, Shichao Yue, Rumen Hristov, Zachary Kabelac, and Dina Katabi. 2017. Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals. In ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).Google Scholar
- Ahmed I., Pallavi P. Patwari, Pranshu A. Adavadkar, Henry Arantes, Chang Park, and David W. Carley. 2019. Validation of the MediByte Portable Monitor for the Diagnosis of Sleep Apnea in Pediatric Patients. Journal of Clinical Sleep Medicine (2019).Google Scholar
- Zhenhua Jia, Musaab Alaziz, Xiang Chi, Richard E Howard, Yanyong Zhang, Pei Zhang, Wade Trappe, Anand Sivasubramaniam, and Ning An. 2016. HB-phone: A Bed-mounted Geophone-based Heartbeat Monitoring System. In Int'l Conf. on Information Processing in Sensor Networks (IPSN).Google Scholar
- Zhenhua Jia, Amelie Bonde, Sugang Li, Chenren Xu, Jingxian Wang, Yanyong Zhang, Richard E Howard, and Pei Zhang. 2017. Monitoring a Person's Heart Rate and Respiratory Rate on a Shared Bed Using Geophones. In ACM SenSys.Google Scholar
- Christoph Kalkbrenner, Philipp Stark, Guy Kouemou, Maria-Elena Algorri, and Rainer Brucher. 2014. Sleep Monitoring using Body Sounds and Motion Tracking. In Int'l Conf. Engineering in Medicine and Biology Society.Google Scholar
- Maximilian Kerz, Amos Folarin, Nicholas Meyer, Mark Begale, James MacCabe, and Richard J Dobson. 2016. SleepSight: A Wearables-based Relapse Prevention System for Schizophrenia. In ACM Int'l Conf. on Pervasive and Ubiquitous Computing (UbiComp).Google Scholar
- Terry K. Koo and Mae Y. Li. 2016. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine (2016).Google Scholar
- Julian Leube, Johannes Zschocke, Maria Kluge, Luise Pelikan, Antonia Graf, Martin Glos, Alexander Müller, Ronny P Bartsch, Thomas Penzel, and Jan W Kantelhardt. 2020. Reconstruction of the Respiratory Signal through ECG and Wrist Accelerometer Data. Scientific Reports 10, 1 (2020), 1--12.Google ScholarCross Ref
- Daniyal Liaqat, Mohamed Abdalla, Pegah Abed-Esfahani, Moshe Gabel, Tatiana Son, Robert Wu, Andrea Gershon, Frank Rudzicz, and Eyal De Lara. 2019. WearBreathing: Real World Respiratory Rate Monitoring Using Smartwatches. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (2019).Google Scholar
- Feng Lin, Yan Zhuang, Chen Song, Aosen Wang, Yiran Li, Changzhan Gu, Changzhi Li, and Wenyao Xu. 2017. SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System. IEEE Trans on Biomedical Circuits and Systems (2017).Google Scholar
- Chen Liu, Jie Xiong, Lin Cai, Lin Feng, Xiaojiang Chen, and Dingyi Fang. 2019. Beyond Respiration: Contactless Sleep Sound-Activity Recognition Using RF Signals. In ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).Google ScholarDigital Library
- Rajalakshmi Nandakumar, Shyamnath Gollakota, and Nathaniel Watson. 2015. Contactless Sleep Apnea Detection on Smartphones. In ACM MobiSys.Google Scholar
- Richard E. Neapolitan. 2003. Learning Bayesian Networks. Prentice-Hall, Inc.Google ScholarDigital Library
- Mahesh Pal. 2005. Random forest classifier for remote sensing classification. International journal of remote sensing (2005).Google ScholarCross Ref
- James M. Parish. 2009. Sleep-Related Problems in Common Medical Conditions. Chest Journal (2009).Google Scholar
- Nuno Pombo and Nuno M Garcia. 2016. ubiSleep: An Ubiquitous Sensor System for Sleep Monitoring. In IEEE Workshop on Wireless and Mobile Computing, Networking and Communications.Google ScholarCross Ref
- Warren R., Peter D. Rochford, Fergal J. O'Donoghue, Robert J. Pierce, Parmjit Singh, and Andrew T. Thornton. 2009. The New AASM Criteria for Scoring Hypopneas: Impact on the Apnea Hypopnea Index. Sleep Journal (2009).Google Scholar
- Tauhidur Rahman, Alexander T Adams, Ruth Vinisha Ravichandran, Mi Zhang, Shwetak N Patel, Julie A Kientz, and Tanzeem Choudhury. 2015. DoppleSleep: A Contactless Unobtrusive Sleep Sensing System using Short-Range Doppler Radar. In ACM UbiComp.Google Scholar
- Yanzhi Ren, Chen Wang, Jie Yang, and Yingying Chen. 2015. Fine-grained Sleep Monitoring: Hearing your Breathing with Smartphones. In IEEE INFOCOM.Google Scholar
- S. R. Safavian and D. Landgrebe. 1991. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man, and Cybernetics (1991).Google Scholar
- Robert E Schapire. 2013. Explaining adaboost. In Empirical inference. Springer.Google Scholar
- Felix Scholkmann, Jens Boss, and Martin Wolf. 2012. An Efficient Algorithm for Automatic Peak Detection in Noisy Periodic and Quasi-Periodic Signals. Algorithms (2012).Google ScholarCross Ref
- Ivan W. Selesnick and Ilker Bayram. 2010. Total Variation Filtering. White paper. (2010).Google Scholar
- M. Shokoueinejad, C. Fernandez, E. Carroll, F. Wang, J. Levin, S. Rusk, N. Glattard, A. Mulchrone, X. Zhang, A. Xie, M. Teodorescu, J. Dempsey, and J. Webster. 2017. Sleep apnea: a review of diagnostic sensors, algorithms, and therapies. Physiological Measurement (2017).Google Scholar
- R.H. Shumway and D.S. Stoffer. 2000. Time Series Analysis and Its Applications. Springer.Google Scholar
- Sleeptracker. April 2021. Sleeptracker for tracking sleep quality. http://www.sleeptracker.com.Google Scholar
- Steven W. Smith. April 2021. The Moving Average Filter. https://www.analog.com/media/en/technical-documentation/dspbook/dsp_book_Ch15.pdf.Google Scholar
- Xiao Sun, Li Qiu, Yibo Wu, and G. Cao. 2017. ActDetector: Detecting Daily Activities Using Smartwatches. Int'l Conference on Mobile Ad-hoc and Sensor Systems (IEEE MASS) (2017).Google Scholar
- Xiao Sun, Li Qiu, Yibo Wu, Yeming Tang, and Guohong Cao. 2017. SleepMonitor: Monitoring Respiratory Rate and Body Position During Sleep Using Smartwatch. ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT) (2017).Google ScholarDigital Library
- Johan AK Suykens and Joos Vandewalle. 1999. Least squares support vector machine classifiers. Neural processing letters (1999).Google ScholarDigital Library
- Hui-Leng Tan, Leila Kheirandish-Gozal, and David Gozal. 2015. Pediatric Home Sleep Apnea Testing: Slowly Getting There. Chest Journal (2015).Google ScholarCross Ref
- Wikipedia. April 2021. Augmented Dickey-Fuller test. https://en.wikipedia.org/wiki/Augmented_Dickey-Fuller_test.Google Scholar
- Yi Yang and Guohong Cao. 2017. Characterizing and Optimizing Background Data Transfers on Smartwatches. IEEE ICNP (2017).Google ScholarCross Ref
- Zhicheng Yang, Parth H Pathak, Yunze Zeng, Xixi Liran, and Prasant Mohapatra. 2017. Vital Sign and Sleep Monitoring using Millimeterwave. ACM Trans. on Sensor Networks (TOSN) (2017).Google Scholar
- Shichao Yue, Hao He, Hao Wang, Hariharan Rahul, and Dina Katabi. 2018. Extracting Multi-Person Respiration from Entangled RF Signals. In ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).Google Scholar
- Youwei Zeng, Dan Wu, Ruiyang Gao, Tao Gu, and Daqing Zhang. 2018. FullBreathe: Full Human Respiration Detection Exploiting Complementarity of CSI Phase and Amplitude of WiFi Signals. In ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT).Google Scholar
- Youwei Zeng, Dan Wu, Jie Xiong, Jinyi Liu, Zhaopeng Liu, and Daqing Zhang. 2020. MultiSense: Enabling Multi-person Respiration Sensing with Commodity WiFi. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 3 (2020), 1--29.Google ScholarDigital Library
- Bing Zhai, Ignacio Perez-Pozuelo, Emma AD Clifton, Joao Palotti, and Yu Guan. 2020. Making sense of sleep: Multimodal sleep stage classification in a large, diverse population using movement and cardiac sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 4, 2 (2020), 1--33.Google ScholarDigital Library
- Jin Zhang, Qian Zhang, Yuanpeng Wang, and Chen Qiu. 2013. A Real-Time Auto-adjustable Smart Pillow System for Sleep Apnea Detection and Treatment. In IEEE IPSN.Google Scholar
Index Terms
- ApneaDetector: Detecting Sleep Apnea with Smartwatches
Recommendations
RF powered sleep apnea monitoring system
PETRA '15: Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive EnvironmentsSleep Apnea is a chronic and widespread problem that is characterized by periods of pauses in breathing during sleep. It can lead to several life threatening conditions if left undiagnosed. Conventional methods of diagnosing sleep apnea involve ...
Pulse Arrival Time Based Cuff-Less and 24-H Wearable Blood Pressure Monitoring and its Diagnostic Value in Hypertension
Ambulatory blood pressure monitoring (ABPM) has become an essential tool in the diagnosis and management of hypertension. Current standard ABPM devices use an oscillometric cuff-based method which can cause physical discomfort to the patients with ...
Diabetic Macular Edema Screened by Handheld Smartphone-based Retinal Camera and Artificial Intelligence
AbstractOur aim was to assess the tomographic presence of diabetic macular edema in type 2 diabetes patients screened for diabetic retinopathy with color fundus photographs and an artificial intelligence algorithm. Color fundus photographs obtained with a ...
Comments