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Resource-efficient and Reliable Long Term Wireless Monitoring of the Photoplethysmographic Signal. Sidharth Nabar , Ayan Banerjee , Sandeep K.S. Gupta, and Radha Poovendran IMPACT Lab Arizona State University NSL Lab University of Washington. PPG, ECG, EMG, GSR. Vision. Sensor Platform.

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resource efficient and reliable long term wireless monitoring of the photoplethysmographic signal

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

vision

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

state of the art
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

contributions
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
key observation

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

represent baseline using generative models
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

de ppg tem ppg generative models
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

idea classification of data
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

solution model based communication
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

sensor module
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

base station module
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

diagnostic equivalence
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

experimental setup
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
evaluation metrics
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
compression results
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

wireless channel errors
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

increased feature error
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)

improvements
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
performance comparison
Performance Comparison
  • Improvements have more accuracy than the basic version without significant loss in energy savings.

Average Diagnostic Feature Accuracy

Compression Ratio

conclusions
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