Sidharth Nabar , Ayan Banerjee , Sandeep K.S. Gupta, and Radha Poovendran

1. Resource-efficient and Reliable Long Term Wireless Monitoring of the Photoplethysmographic Signal SidharthNabar, Ayan Banerjee, Sandeep K.S. Gupta, and RadhaPoovendran IMPACT Lab Arizona State University NSL Lab University of Washington

2. PPG, ECG, EMG, GSR Vision Sensor Platform • Monitor a lifetime worth of physiological data • Researchers in action • Monitoring high resolution video for five years !![1] • Monitoring of physiological signals for months [2] • Advantages • Detection of random events • Discover potential cause of chronic diseases[2] • Discover reasons for behavioral traits in a person Mobile phone Gateway Base Station Monitoring Physiological Health Throughout the Lifetime of the Patient [1] http://www.youtube.com/watch?v=RE4ce4mexrU&feature=player_embedded#! [2]http://www.healthnewsdigest.com/news/Heart_Health_410/Computer_Science_Gives_a_Boost_to_Heart_Health.shtml

3. State of the Art • Periodic Signal Update • Send sample by sample and use compression schemes to compress each sample • Challenges • Storage overhead: Exabytes of information for a person • Increase form factor, hamper mobility, reduce usability • Energy Consumption: Expensive communication • Low lifetime, frequent charging required • Wireless Errors: Random bit, burst, and fading error.s • More chance for packet corruption, frequent use of retransmissions and dynamic power control, more power dissipation Reduce data transmission while maintaining the diagnostic equivalence

4. Contributions • A model based data collection technique for PPG • Reduce storage requirements • Save communication energy • Evaluation of the performance under wireless errors • Improvements to the performance

5. Peak-to-peak interval Key Observation Dicrotic notch • PPG Signal Characteristics: • PPG signals typically have a baseline morphology • A periodic structure • Frequently varying features only change the time period and width of the periodic structure • Morphology changes occur in rare cases of pathological conditions Amplitude Pulse Width Diastole Systole a) Raw PPG waveform b) PPG features Key Observation: Represent the periodic structure by a model Advantage: Enables parametric representation of the signal

6. Represent baseline using Generative Models • Mathematical model to generate data from given input parameters • Used in music, machine learning, wireless sensor networks and other areas • Input parameters can be trained on given data … G Input Parameters Output signal Time

7. DE-PPG & Tem-PPG generative models • DE-PPGWindkessel Models • Modified with an extra sinc function to model the Dichrotic notch • Tem-PPG template based models Differential Equation Model Learning: Curve fitting to learn the parameters Template Based Model Rank beats in decreasing order of number of beats that match Compute correlation of beats with all others PPG Time Series Template 3 Template 1 Template 2 Select beats to form an exclusive set of templates

8. Idea: Classification of data Collected PPG Data 80 80 80 60 60 60 30 30 30 10 10 10 0 0 50 100 150 0 0 0 50 100 150 0 100 200 300 Baseline Expected variations Unexpected patterns (e.g. Consistent heart rate, PPG morphology) (e.g. Pulse height increase with rising blood pressure) (e.g. Arrhythmia) Send raw signal samples No data sent Send feature values Majority case: Reduced data transmission Rare occurrence

9. Solution - Model Based Communication Sensed PPG G Output PPG Unreliable Wireless Channel Match? Compare Align Raw PPG samples G Feature updates Sensor Module Base Station Module Raw signal updates Physician

10. Sensor Module Below Raw Signal Update 1 1 Correlation threshold Extract shape 0 0 1 1 Extract features Parameters Sensed PPG Compare Feature Update If mismatch

11. Base Station Module DE-PPG: Curve Fitting Model Learner Tem-PPG: New Template Raw signal updates Align Output PPG Model-generated PPG Feature updates Update Parameters

12. Diagnostic Equivalence • Two signals are diagnostically equivalent if the features used for diagnosis derived from both the signals are same. • Diagnostic features: • Heart Rate • Pulse Height • Systole width • Diastole width • Dichrotic Notch Heart Rate = 1/(Peak-to-peak interval) Pulse Height Dichrotic notch Diastole Width Systole Width

13. Experimental Setup • Two data sets • Physionet database, 10 patients, normal as well as arrhythmia • IMPACT database, 10 volunteers all normal • Physionet data collected in a controlled environment • IMPACT data collected in a lab environment with motion artifacts

14. Evaluation Metrics • Compression Ratio (CR) = • We consider compression schemes for periodic signal update case to make a fair comparison • Communication Energy savings proportional to CR • Diagnostic Feature Error = • = feature value for the scheme, while = feature value for periodic signal update • Measures accuracy in feature estimation

15. Compression Results • 300:1 compression ratio • Energy savings vs feature error tradeoff 100 100 Heart Rate (bpm) Heart Rate (bpm) 80 80 60 60 0 50 100 150 200 250 300 0 50 100 150 200 250 150 175 200 225 250 Time in minutes Time in minutes 100 100 Pulse Height Pulse Height (arbitrary units) (arbitrary units) 50 50 0 0 0 50 100 150 200 250 150 175 200 225 250 0 50 100 150 200 250 300 Time in minutes Time in minutes 0.3 0.3 Systole Width (s) 0.2 Systole Width (s) 0.2 0.1 0.1 0 50 100 150 200 250 150 175 200 225 250 0 50 100 150 200 250 300 Time in minutes Time in minutes 0.8 0.8 Diastole Width (s) 0.6 Diastole Width (s) 0.6 0.4 0 50 100 150 200 250 150 175 200 225 250 0.4 0 50 100 150 200 250 300 Time in minutes Measured Time in minutes Measured DE-PPG Tem-PPG

16. Wireless Channel Errors • Packet Loss may lead to • Loss of feature updates • Loss of raw signal updates • Overall increase in diagnostic feature error • Types of errors considered • Random bit errors • Burst errors • Fading errors Since less packets are sent probability of error is low However, each packet is now even more important

17. Increased Feature Error • Diagnostic feature error increases but is comparable to the periodic signal update case Average Percentage Feature Error = mean of the errors of the 5 features Different compression ratios are considered for the periodic signal update case 30 • Model based communication 80 • Model based communication Periodic Signal Updates Periodic Signal Update 70 25 CR = 1 60 20 50 15 40 30 Model based communication Average Percentage Feature Error 30 Average Percentage Feature Error 10 Periodic Signal Updates CR = 12 25 CR = 1 CR = 1 20 CR = 40 CR = 12 CR = 40 20 5 Average Percentage Feature Error 10 CR = 40 CR = 12 15 Periodic Signal Update often has less accuracy than model based communication 0 0 Low Medium High Low Medium High 10 Network Errors due to Burst Network Error due to Fading 5 0 10^(-6) 10^(-4) 10^(-2.5) Bit Error Rate (Log scale)

18. Improvements • Resend Packets • Whenever acknowledgment for a packet is not received resend the packet • Loss in energy savings and data compression • Dynamic power adjustment • Estimate the BER and increase the radio power to improve SNR • Loss in energy saving, no compression loss

19. Performance Comparison • Improvements have more accuracy than the basic version without significant loss in energy savings. Average Diagnostic Feature Accuracy Compression Ratio

20. Conclusions • 300:1 energy savings for PPG • 93 % accuracy in diagnostic feature estimation • Rigorous experimentation under wireless errors • Model based technique is generic • Implementation for ECG using shimmer motes • PPG implementation ongoing

21. Thank You