chau nguyen l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Survey of Heart Monitoring and Sleep Monitoring Problems PowerPoint Presentation
Download Presentation
Survey of Heart Monitoring and Sleep Monitoring Problems

Loading in 2 Seconds...

play fullscreen
1 / 21

Survey of Heart Monitoring and Sleep Monitoring Problems - PowerPoint PPT Presentation


  • 163 Views
  • Uploaded on

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)

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Survey of Heart Monitoring and Sleep Monitoring Problems' - gustav


Download Now An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
heart monitoring
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
portable wearable heart monitor
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
alive technologies heart monitor
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
other heart monitors
Other heart monitors
  • Vivometrics (LifeShirt)
  • Equivital (UK)
  • Bodymedia
  • These do not integrate with PDAs
n0n commercial systems
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
heart monitoring applications
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)
cardiac monitoring university of technology sydney
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
pediatric obesity study university of southern california
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

detecting cardiovascular disease university of pittsburgh
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
other applications
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
heart monitoring applied to sleep monitoring
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.
actigraph
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.
ecg existing approach
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.
slide17

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

sample research projects for sleep wake classification
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)
new approach
New approach
  • Heart Monitoring : integrated with BScope Portal
  • Sleep Monitoring: acceleration in combination with heart rate using neural network classifier