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Welcome to the Aging Services Technologies Laboratory
The Aging Services Technologies Laboratory is an interdisciplinary research lab focused on
developing innovative technology and systems that improve elderly people’s quality of life.
Our vision is to create the technological foundation for maintaining a high quality of life as
people age.
Our mission is to develop technology that will:
Increase quality of life
Decrease health-care costs
Be applicable to our soldiers, veterans and people with disabilities as well
Partnership Opportunities
Partnership opportunities are available for companies, institutions and investors.
QoLT Lab
Vision
To become a global leader in
performing research and innovating
technologies in increasing the quality
of life with aging.
increasing your QoL
QoLT Lab
Team
• Lakshman Tamil, Electrical Engineering
• Architecture, radio and overall management of the
project
• Subhash Banerjee, M.D., UTSW Medical Center
• Cardiology
• Gopal Gupta, Computer Science
• Software
• Larry Amman, Mathematics & Statistics
• Statistical analysis & Modeling
• Mehrdad Nourani, Electrical Engineering
• Hardware, integration & testing, ASIC/SoC design
• Hlaing Minn, Electrical Engineering
• Communication hardware design and modeling
• Vincent Ng, Computer Science
• Machine learning
increasing your QoL
Generic Body Area Network (BAN)
• Several non-invasive sensors worn on body
• Vital signs data collected and passed (via gateway) to system
database
•
Database stores, processes, analyzes data, and takes action if
required
QoLT Lab
Remote Monitoring of Vital Signs
Internet
Gateway
Monitoring center
Doctor’s office
increasing your QoL
QoLT Lab
Treatment Delayed is Treatment Denied
Individual with Chest Pain (CP)
Current Practice Standard
Symptom
Recognition
< 90 min
< 30 min
5 min
Call to
Medical System
Pre-Hospital
Biggest Challenge
ED
Cath Lab
Increasing Loss of Heart Muscle
30 ± 2.3 h
Delay in Initiation of Therapy
March 24, 2016
increasing your QoL
QuBIT Lab's Proprietary
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Generic Sensor Node Implementation
ECG Sensor Node Implementation
• Plug-and-Play ECG sensor node for Body Area Network
• Connected to PC via USB port
System Backend View of Signal
ECG Signal Processing
• ECG preprocessing and feature extraction
• LabVIEW’s wavelet toolset used
Original ECG Signal
Denoising
Baseline
Wandering
Removal
ECG Records
•
Wavelet
Detrend
Wide-band
Noise
Suppression
•
Preprocessed
ECG Data
Wavelet
Analysis sym5
wavelet
Fiducial Point Extraction
Preprocessed
ECG Data
•
Overall accuracy of
99.51% achieved on MITBIH Arrhythmia Database
QRS Complexes
Extraction
•
•
•
•
Wavelet peak and valley
detector
Adaptive Thresholding
Search-back algorithm for
possible missed peaks
Valleys right before and
after each peak (R)
determine Q and S points
To ECG Beat Classification
Denoised ECG Signal
and QRS complexes
marked
Heart Beat Classification Module Using
Support Vector Machine
PR
PR
QRS
Interval Duration
ST
Segment
Mean R-peak
Average RR Interval
Mean Power Spectral Density
Autocorrelation Value
Area under QRS
ST
Interval
One Feature Vector for each
Heart Beat
Learning
Algorithm
-- Support
Vector
Machine
Classified Heart Beats
QoLT Lab
Thank You
March 24, 2016
increasing your QoL
QuBIT Lab's Proprietary
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