Welcome to the Aging Services Technologies Laboratory

1. 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.

2. Vision To become a global leader in performing research and innovating technologies in increasing the quality of life with aging.

3. 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

4. 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

5. Remote Monitoring of Vital Signs Internet Gateway Monitoring center Doctor’s office

6. Current Practice Standard < 90 min < 30 min 5 min Cath Lab Symptom Recognition Call to Medical System Pre-Hospital ED Biggest Challenge Increasing Loss of Heart Muscle Delay in Initiation of Therapy Treatment Delayed is Treatment Denied Individual with Chest Pain (CP) 30 ± 2.3 h November 27, 2014 QuBIT Lab's Proprietary 6

7. Generic Sensor Node Implementation

8. ECG Sensor Node Implementation • Plug-and-Play ECG sensor node for Body Area Network • Connected to PC via USB port

9. System Backend View of Signal

10. ECG Signal Processing ECG preprocessing and feature extraction LabVIEW’s wavelet toolset used Denoising Original ECG Signal Baseline Wandering Removal Wide-band Noise Suppression ECG Records Preprocessed ECG Data • Wavelet Detrend • Wavelet Analysis sym5 wavelet Fiducial Point Extraction QRS Complexes Extraction Preprocessed ECG Data To ECG Beat Classification • 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 Denoised ECG Signal and QRS complexes marked • Overall accuracy of 99.51% achieved on MIT-BIH Arrhythmia Database • Overall accuracy of 99.51% achieved when applied to MIT-BIH Arrhythmia Database

11. Heart Beat Classification Module Using Support Vector Machine Feature Extraction Mean R-peak Average RR Interval Mean Power Spectral Density Autocorrelation Value Area under QRS PR ST Segment ST Interval PR Interval QRS Duration One Feature Vector for each Heart Beat Learning Algorithm-- Support Vector Machine Classified Heart Beats

12. Thank You November 27, 2014 QuBIT Lab's Proprietary 12