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Survey of Heart Monitoring and Sleep Monitoring Problems

Chau Nguyen. Survey of Heart Monitoring and Sleep Monitoring Problems. Heart Monitoring. Monitoring of heart signals ( ECG, heart rate, heart rate variability HRV, RR, P, QRS duration…etc)

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Survey of Heart Monitoring and Sleep Monitoring Problems

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  1. Chau Nguyen Survey of Heart Monitoring and Sleep Monitoring Problems

  2. Heart Monitoring • Monitoring of heart signals ( ECG, heart rate, heart rate variability HRV, RR, P, QRS duration…etc) • Purpose: early heart disease warning, post-heart operation rehabilitation, sleep wake classification, activity recognition or any other applications that make use of the signals of the heart

  3. ECG

  4. Portable, Wearable Heart Monitor • Why portable? • Limited contact time in the doctor office • Continuous, long term monitoring provides better evaluation • High cost involved in visiting a clinic

  5. Alive Technologies Heart Monitor • Extremely portable: small size and long battery life (~48hours) • Bluetooth enabled • 3-axis accelerometer included • Compatible with a wide variety of PC and smart-phone platforms

  6. Other heart monitors • Vivometrics (LifeShirt) • Equivital (UK) • Bodymedia • These do not integrate with PDAs

  7. N0n-commercial systems • LiveNet from MIT Wearable Computing Group (Michael Sung, Alex Pentland) • Harvard’s Code Blue Project • Disadvantages of these platforms: reliance on special PDAs running Linux or intricate sensors motes developed in-house

  8. Heart monitoring Applications • Cardiac Monitoring - University of Technology, Sydney (Remote Monitoring of Cardiac Patients after a Heart Attack or a Coronary Bypass Surgery) • Pediatric Obesity study - University of Southern California (Multimodal Sensing for Pediatric Obesity Applications)(Statistical Signal Processing) • Detecting Cardiovascular Disease - University of Pittsburgh (Detecting Cardiovascular Diseases via Real-Time Electrocardiogram Processing on a Smartphone) (Cardiac arrhythmia)

  9. Cardiac Monitoring - University of Technology, Sydney • a personalised rehabilitation application using a smart phone (PDA) and wireless (bio) sensors. • Instructs and motivates patients to follow their exercise programme and keeps track of their progress. • monitors the relevant biosignals and provides immediate feedback to the patient. • home based cardiac rehabilitation as a viable alternative to rehabilitation programmes conducted at a hospital • http://www.personalhealthmonitor.net/docs/PETRA2009.pdf

  10. Pediatric Obesity study - University of Southern California • a wireless body area network comprised of heterogeneous sensors for wearable health monitoring applications. • pediatric obesity • activity detection based on heart rate monitor and accelerometer data • Statistical analysis of experimental data for different key states (lying down, sitting, standing, walking and running • 85-95% accuracy http://sensorlab.cs.dartmouth.edu/urbansensing/papers/annavaram_urbansense08.pdf

  11. Detecting Cardiovascular Disease - University of Pittsburgh • Cardiac arrhythmia = irregular rhythmic beating of the heart; very common; may indicate an increased risk of stroke or sudden cardiac death • arrhythmias may not be detected during normal patient hospital visits • http://www.engr.pitt.edu/act/bic2009/doc/BiC-Proceedings.pdf#page=20

  12. Other Applications • Fitness training -University of Udine, Italy (A context-aware and user-adaptive wearable system for fitness training) • Video game -University of Udine, Italy (Adaptation of Graphics and Gameplay in Fitness Games by Exploiting Motion and Physiological Sensors) • Biometric Identification

  13. Heart Monitoring applied to Sleep Monitoring • Sleep-Wake identification • Why sleep monitoring? Deteriorating sleep-wake circadian cycle has been linked to onsets of heart disease • Why ECG? Non-invasive and easier to obtain as compared to Polysomnography which has traditionally been used in sleep study research. More accurate than actigraph.

  14. Polysomnography

  15. Actigraph • a relatively non-invasive method of monitoring human rest/activity cycles. • a small actigraph unit, also called an actimetry sensor, is worn by a patient to measure gross motor activity. • motor activity often under test is that of the wrist, measured by an accelerometer in a watch-like package.

  16. ECG – Existing Approach • the power spectrum of the ECG or the spectrum of HRV (heart rate variability) • Characteristic spectrum different for sleep and wake. • limitations: intensive data rate and computing power required as well as the required accuracy of the ECG signal obtained and heart rate calculated.

  17. Heart Rate Variability During Waking and Sleep in Healthy Males and Females, Sigrid Else, Thomas N. Lynn Institute for Healthcare Researchnbruch

  18. Sample Research Projects for sleep-wake classification • Automatic sleep detection using activity and facial electrodes (Finnish Institute of Occupational Health) • Automatic Sleep Stage Classification Using Electro-oculography (Tampere University of Technology)(resting potential of the retina) • Adaptive Wake and Sleep Detection for Wearable Systems(Swiss Federal Institutes of Technology)( spectral analysis of ECG, RSP with neural network)

  19. Heart Rate

  20. New approach • Heart Monitoring : integrated with BScope Portal • Sleep Monitoring: acceleration in combination with heart rate using neural network classifier

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