Combining the Information of Unconstrained Electrocardiography and Ballistography in the Detection of Night-Time Heart Rate and Respiration Rate

An unobtrusive bed integrated system for monitoring physiological parameters during sleep is presented and evaluated. The system uses textile electrodes attached to a bed sheet for measuring multiple channels of electrocardiogram. The channels are also combined in order to form several additional ECG leads. One lead at a time is selected for beat-to-beat-interval detection. The system also includes force sensors located under a bed post for detecting respiration and movements. The movement information is also used to assist in heart rate detection and combining the ECG derived respiration information with respiration information derived from force sensors, is investigated. The authors tested the system with ten subjects in one hour recordings and achieved an average of 95.9% detection coverage and 99 percentile absolute error of 3.47 ms for the BB-interval signal. The relative mean absolute error of the detected respiration cycle lengths was 2.1%.

[1]  L. Lin,et al.  A concordance correlation coefficient to evaluate reproducibility. , 1989, Biometrics.

[2]  S Akselrod,et al.  Nonlinear high pass filter for R-wave detection in ECG signal. , 1997, Medical engineering & physics.

[3]  V Pichot,et al.  Screening of obstructive sleep apnea syndrome by heart rate variability analysis. , 1999, Circulation.

[4]  David P White,et al.  Assessment of a wrist-worn device in the detection of obstructive sleep apnea. , 2003, Sleep medicine.

[5]  Feng Wang,et al.  Development of a PVDF Piezopolymer Sensor for Unconstrained In-Sleep Cardiorespiratory Monitoring , 2003 .

[6]  Daniel J Buysse,et al.  Acute Stress Affects Heart Rate Variability During Sleep , 2004, Psychosomatic medicine.

[7]  M. Ishijima,et al.  Cardiopulmonary monitoring by textile electrodes without subject-awareness of being monitored , 1997, Medical and Biological Engineering and Computing.

[8]  E. Naujokat,et al.  Sleep Monitoring Through a Textile Recording System , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  N. Shiozawa,et al.  Electrocardiogram Measurement during Sleep with Wearing Clothes Using Capacitively-Coupled Electrodes , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  D. Cysarz,et al.  Comparison of Respiratory Rates Derived from Heart Rate Variability, ECG Amplitude, and Nasal/Oral Airflow , 2008, Annals of Biomedical Engineering.

[11]  David Zhang,et al.  Advanced Pattern Recognition Technologies with Applications to Biometrics , 2008 .

[12]  Kin-fai Wu,et al.  Contactless and continuous monitoring of heart electric activities through clothes on a sleeping bed , 2008, 2008 International Conference on Information Technology and Applications in Biomedicine.

[13]  Liang Wang,et al.  Behavioral Biometrics For Human Identification: Intelligent Applications , 2009 .

[14]  Daijin Kim,et al.  Automated Face Analysis: Emerging Technologies and Research , 2009 .

[15]  Vasileios Exadaktylos,et al.  Heart rate-based nighttime awakening detection , 2010, European Journal of Applied Physiology.

[16]  Matteo Matteucci,et al.  Sleep Staging Based on Signals Acquired Through Bed Sensor , 2010, IEEE Transactions on Information Technology in Biomedicine.

[17]  Steffen Leonhardt,et al.  Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms , 2011, IEEE Transactions on Information Technology in Biomedicine.

[18]  Joonas Paalasmaa,et al.  Quantifying respiratory variation with force sensor measurements , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[19]  Zhang Xu,et al.  Accurate and Rapid QRS Detection for Intelligent ECG Monitor , 2011, 2011 Third International Conference on Measuring Technology and Mechatronics Automation.

[20]  Jennifer L Martin,et al.  Wrist actigraphy. , 2011, Chest.

[21]  Cafer Avci,et al.  Performance of the EDR Methods: Evaluations Using the Mean and Instantaneous Respiration Rates , 2011, 2011 5th International Conference on Bioinformatics and Biomedical Engineering.

[22]  Merja Puurtinen,et al.  Precordial Bipolar Leads for Mobile ECG Applications , 2012 .

[23]  Mikko Peltokangas,et al.  Night-Time EKG and HRV Monitoring With Bed Sheet Integrated Textile Electrodes , 2012, IEEE Transactions on Information Technology in Biomedicine.

[24]  Mikko Peltokangas,et al.  Combining unobtrusive electrocardiography and ballistography for more accurate monitoring of sleep , 2012, 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE).

[25]  Lionel Tarassenko,et al.  Data fusion for estimating respiratory rate from a single-lead ECG , 2013, Biomed. Signal Process. Control..

[26]  Werner Wolf,et al.  Comparison of SEMG Derived Parameters and Blood Oxygen Saturation in Monitoring Neuromuscular Fatigue in Humans , 2013, Int. J. Monit. Surveillance Technol. Res..

[27]  Agis M. Papadopoulos,et al.  Energy Consumption in Greek Households During the Economic Recession , 2014, Int. J. Monit. Surveillance Technol. Res..

[28]  Christina M. Akrivopoulou Protecting the Genetic Self from Biometric Threats: Autonomy, Identity, and Genetic Privacy , 2015 .

[29]  Christina M. Akrivopoulou,et al.  Sexual Orientation, Female Genital Mutilation, and Health in Asylum Cases: International and ECHR Jurisprudence , 2015 .

[30]  International Journal of Monitoring and Surveillance Technologies Research , .