Overview BWSSN Recap Skin Sensor Signal Types DSC Brief Review DWT Brief Review ECG Signal DWT ECG DWT ECG Adapted for Wireless Transmission DSC DWT ECG

Overview• BWSSN Recap• Skin Sensor Signal Types• DSC Brief Review• DWT Brief Review• ECG Signal• DWT ECG• DWT ECG Adapted for Wireless Transmission• DSC DWT ECG Wireless Transmission

Typical Skin Sensor Network

DCN 0

S1-Temperature

S2–Blood Pressure

S3–Heart Rate

S4-Blood Oxygen

S5-Sweat: Na,Cl,K

S6-Sweat: pH, NH3, Lactate

S7-Sweat: Ca, Amino Acids S8- Glucose

Wired Connection to Internet and/or

PC

DCN 1Rm 1

DCN 2Rm 2

DCN 3Rm 3

Sensor & Data Acquisition BoardsProcessor/Radio Boards (Motes)Gateways & Network Interface

BWSSN Basic Sensor Processor Module

Communications

Signal Processing

Low Power Electronics

Biomedical Sensing

Sensing, Signal Processing, Wireless Communication Module

Wireless Comm. Node

Skin Sensor Signals

• Modules will incorporate sensing, signal processing, RF communications and low power electronics.

• Skin sensors for – Temperature: Thermistor – Heart Rate: ECG– Blood Oxygen: Pulse Oximeter– Blood Pressure– Sweat component detectors:

Distributed Source Coding (DSC)Typical Area of Application

X

Y2

Y1

Y3

Y4

Wire- less

Commnetwor

k

Wire- less

Commnetwor

k

Central Decoder

Yi = hi*X + Zi

Zi: Noise

hi: LTI Filter

SourceX

Encoder Decoder

Y000

001

010

011

100

101

110

111

{000, 111} = 00

{001, 110} = 01

{010, 101} = 10

{011, 100} = 11

X={000, 111}

Y=000

dH(X,Y) ≤1

X-Y ≤1

X=000

00 X=000000

DSC SW Example Implementation

Wireless CommunicationGeneral Data Packet Structure

PRE SPD LEN PC CRCLink Layer PDUADDRESSING

Preamble sequence

Start of Packet Delimiter

Length for decoding simplicity

Flags specify addressing mode

Data sequence number

CRC-16

DSN

Addresses according to specified mode

A typical TOSH data packet comprises the following:

7E 42 FF FF 06 11 1D 81 02 01 00 B9 07 B0 07 BE 07 B5 07 7F 00 FF 01 FF 03 00 00 00 00 00 00 00 00 00 00 00 55 86 7E

In this frame:

7E 42 ==> SYNC and TYPE bytes is the Serial framing protocol

The rest is the TOS_msg -- actual packet (detailed in file tos/types/AM.h)

TOSH Data Packet Used in Xbow Motes

Breakdown is as follows:

uint16_t addr; // FF FF

uint8_t type; // 06 //

uint8_t group; // 11

uint8_t length; // 1D

int8_t data[TOSH_DATA_LENGTH];// 81 02 01 00 B9 07 B0 07 BE 07 // B5 07 7F 00 FF 01 FF 03 00 00 // 00 00 00 00 00 00 00 00 00

uint16_t crc; // 55 86

TOSH Data Packet

Energy Consumption Reduction Via DSC

• TOSH_DATA_LENGTH is usually set equal to 29. Varying this number varies the length of the data packet.

• Each packet carries node id, voltage, temperature, light and other mote information.

• Each sensor’s data is encoded into two eight bit words.

• One objective via DSC– reduce the number of data bits per sensor that is

wirelessly transmitted from 16 to 8 with the receiver still yielding the sensor reading without loss of information.

Wavelet Transforms Introduction• Wavelet

– A small wave

• Wavelet Transforms– Convert a signal into a series of wavelets– Provide a way for analyzing waveforms, bounded in both

frequency and duration– Allow signals to be stored more efficiently than by Fourier

transform– Be able to better approximate real-world signals– Well-suited for approximating data with sharp discontinuities

• “The Forest & the Trees”– See gross features with a large "window“– See small features with a small "window”

Source: Fengxiang Qiao, Ph.D. Texas Southern University

www.missouri.edu/~sunc/TRB/ Introduction%20to%20Wavelet%20a%20Tutorial%20-%20Qiao.ppt

TIME-DOMAIN SIGNAL

• The Independent Variable is Time• The Dependent Variable is the Amplitude• Most of the Information is Hidden in the Frequency Content

0 0.5 1-1

-0.5

0

0.5

1

0 0.5 1-1

-0.5

0

0.5

1

0 0.5 1-1

-0.5

0

0.5

1

0 0.5 1-4

-2

0

2

4

10 Hz2 Hz

20 Hz2 Hz +

10 Hz +20Hz

TimeTime

Time Time

Ma

gn

itu

de

Ma

gn

itu

de

Ma

gn

itu

de

Ma

gn

itu

de

Source: Fengxiang Qiao, Ph.D. Texas Southern Universitywww.missouri.edu/~sunc/TRB/ Introduction%20to%20Wavelet%20a%20Tutorial%20-%20Qiao.ppt

STATIONARITY OF SIGNAL

0 0.2 0.4 0.6 0.8 1-3

-2

-1

0

1

2

3

0 5 10 15 20 250

100

200

300

400

500

600

Time

Ma

gn

itu

de

Ma

gn

itu

de

Frequency (Hz)

2 Hz + 10 Hz + 20Hz

Stationary

0 0.5 1-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20 250

50

100

150

200

250

Time

Ma

gn

itu

de

Ma

gn

itu

de

Frequency (Hz)

Non-Stationary

0.0-0.4: 2 Hz + 0.4-0.7: 10 Hz + 0.7-1.0: 20Hz

Occur at all times

Do not appear at all times


• Wavelet Transform– An alternative approach to the short time Fourier

transform to overcome the resolution problem – Similar to STFT: signal is multiplied with a function

• Multiresolution Analysis – Analyze the signal at different frequencies with

different resolutions– Good time resolution and poor frequency resolution at

high frequencies– Good frequency resolution and poor time resolution at

low frequencies– More suitable for short duration of higher frequency;

and longer duration of lower frequency components


PRINCIPLES OF WAVELET TRANSFORM

• Split Up the Signal into a Bunch of Signals

• Represents the Same Signal, but all Corresponding to Different Frequency Bands

• Only Providing What Frequency Bands Exists at What Time Intervals


PRINCIPLES OF WAVELET TRANSFORM

• Basically WT is a convolution of the wavelet function with the signal

• WT analyzes signal info by modifying the wavelets thru translation (location) and dilation (scale)

• Wavelet function φa,b with Scale, a and Location, b

T. Froese ECG Signal Classification using DWT: http://www.cogs.susx.ac.uk/users/tf29/publications/Froese_04_ClassificationOfECGUsingDWT_MEng.pdf

• Wavelet – Small wave– Means the window function is of finite length

• Mother Wavelet– A prototype for generating the other window functions– All the used windows are its dilated or compressed and

shifted versions

DEFINITION OF CONTINUOUS WAVELET TRANSFORM

dtst

txs

ss xx

*1

, ,CWT

Translation

(The location of the window)

Scale

Mother Wavelet


Translated 1 D Wavelet Example


DWT SCALE• Scale

– S>1: dilate the signal– S<1: compress the signal

• Low Frequency -> High Scale -> Non-detailed Global View of Signal -> Span Entire Signal

• High Frequency -> Low Scale -> Detailed View Last in Short Time

• Only Limited Interval of Scales is Necessary


Time Scaled 1D Wavelet


MATHEMATICAL EXPLANATION

dttTX

dtst

txs

ss

s

xx

,

*

1 , ,CWT

st

sts

1,

CWT can also be regarded as the inner product of the signal with a basis function

ts ,


COMPUTATION OF CWT

• Step 1: The wavelet is placed at the beginning of the signal, and set s=1 (the most compressed wavelet);

• Step 2: The wavelet function at scale “1” is multiplied by the signal, and integrated over all times; then multiplied by ;

• Step 3: Shift the wavelet to t= , and get the transform value at t= and s=1;

• Step 4: Repeat the procedure until the wavelet reaches the end of the signal;

• Step 5: Scale s is increased by a sufficiently small value, the above procedure is repeated for all s;

• Step 6: Each computation for a given s fills the single row of the time-scale plane;

• Step 7: CWT is obtained if all s are calculated.

dtst

txs

ss xx

*1

, ,CWT

s1


RESOLUTION OF TIME & FREQUENCY

Time

Frequency

Better time resolution;Poor frequency resolution

Better frequency resolution;Poor time resolution

• Each box represents a equal portion • Resolution in STFT is selected once for entire analysis

Higher Frequencies

Lower Frequencies


Discrete Wavelet Transform

• Discrete form of Wavelet function– m varies dilation,– n varies translation, – a0 is a fixed dilation step, – b0 is the location parameter > 0

• Natural way to sample parameters a and b is to use a log discretization of the ‘a’ scale and link this in turn to the size of the steps taken between ‘b’ locations


Discrete Wavelet Transform• DWT of a continuous signal via a discrete

wavelet is given by

• Tm,n are known as wavelet or detail coefficients• In the dyadic grid arrangement a0=2, and b0=1 • Discrete dyadic wavelets are usually selected to

be orthonormal (orthogonal and normalized to have unit energy)– Means that info stored in a wavelet coefficient, Tm,n is

not repeated elsewhere and allows for complete regeneration of the original signal without redundancy


Discrete Wavelet Transform• Sampling of the continuous signal is

accomplished by associating the orthonormal dyadic discrete wavelets with scaling functions

• Scaling function is convolved with the continuous signal to produce approximation coefficients which are weighted averages of the signal factored by 2m/2

• These approximation coefficients Sm,n constitute a discrete approximation of the signal at scale m


Continuous Wavelet Transform

Discrete Wavelet Transform


1d DWT Algorithm

Starting from s, 1st step produces 2 sets of coeff: Approx coeff. cA1 and Detail coeff cD1. These vectors are obtained by convolving s with LPF Lo-D for cA1 and with HPF Hi-D for cD1 followed by dyadic decimation

Source: Matlab

Matlab DWT Decomposition Algorithm

Source: Matlab

DWT Matlab Implementation

Source: Matlab

SUBBAND CODING ALGORITHM• Halves the Time Resolution

– Only half number of samples resulted

• Doubles the Frequency Resolution– The spanned frequency band halved

0-1000 Hz

D2: 250-500 Hz

D3: 125-250 Hz

Filter 1

Filter 2

Filter 3

D1: 500-1000 Hz

A3: 0-125 Hz

A1

A2

X[n]512

256

128

64

64

128

256SS

A1

A2 D2

A3 D3

D1

Matlab Illustration

Source: Matlab

Matlab Wavelet Decomposition

Source: Matlab

Matlab Wavelet Decomposition

Source: Matlab

WAVELET BASES

Wavelet Basis Functions:

21

1

241-

0

2

20

21

1- :devivativeDOG

1!2!2

DOG :order Paul

:)frequency(Morlet

edd

mm

immi

m

ee

m

mm

mmm

j

Derivative Of a GaussianM=2 is the Marr or Mexican hat wavelet

Time domain Frequency

domain

Various 1D Wavelets


Wavelet Function Criteria• For complex wavelets, FT must both be

real and vanish for negative frequencies

• Wavelet must have finite energy

• Wavelet must have zero mean meaning that wavelet has no zero frequency components

DWT of ECG

• DWT can be used to represent a discretely sampled ECG signal by a finite amount of time-invariant wavelet coefficients with enough resolution for further signal diagnostics.

• DWT can also be used to filter noise and other signal deteriorating artifacts by dropping out selected coefficients in the reconstruction process

ECG Signal Origin

http://medicalcenter.osu.edu/patientcare/healthinformation/diseasesandconditions/heartdisease/arrhythmias/•http://your-doctor.com/healthinfocenter/medical-conditions/cardiovascular/conductiontutorial.html

http://medicalcenter.osu.edu/patientcare/healthinformation/diseasesandconditions/heartdisease/arrhythmias/

http://medicalcenter.osu.edu/patientcare/healthinformation/diseasesandconditions/heartdisease/arrhythmias/

ECG Signal Origin

Cardiac conduction system: The specialized network of cells in the heart that initiates an electrical signal in the heart and carries it throughout the heart, causing it to beat.

Standard ECG Signal Detection

Ref: http://rnbob.tripod.com/index.htm

Typical ECG Signal

http://rnbob.tripod.com/index.htm

http://rnbob.tripod.com/index.htm

ECG Signal Points of Origin

[Diagram from Psychophysiology-Human Behaviour and Physiological Response

[Diagram from Psychophysiology-Human Behaviour and Physiological Response]http://home.iprimus.com.au/rboon/HeartRhythmsandHRV.htm

http://home.iprimus.com.au/rboon/HeartRhythmsandHRV.htm


ECG Wave Components

The P wave represents atrial activation; the PR interval is the time from onset of atrial activation to onset of ventricular activation. The QRS complex represents ventricular activation; the QRS duration is the duration of ventricular activation. The ST-T wave represents ventricular repolarization. The QT interval is the duration of ventricular activation and recovery. The U wave probably represents "afterdepolarizations" in the ventricles.

http://medstat.med.utah.edu/kw/ecg/mml/ecg_533.htmlhttp://medstat.med.utah.edu/kw/ecg/image_index/index.html

http://medstat.med.utah.edu/kw/ecg/mml/ecg_533.html

Terminology for Contraction Phases

Marquette Electronics Copyright 1996, http://medstat.med.utah.edu/kw/ecg/mml/ecg_compens.html http://medstat.med.utah.edu/kw/ecg/image_index/index.html

http://medstat.med.utah.edu/kw/ecg/mml/ecg_compens.html


http://medstat.med.utah.edu/kw/ecg/image_index/index.html

http://medstat.med.utah.edu/kw/ecg/mml/ecg_arrhythmia.htmlhttp://medstat.med.utah.edu/kw/ecg/image_index/index.html

http://medstat.med.utah.edu/kw/ecg/mml/ecg_arrhythmia.html




http://medstat.med.utah.edu/kw/ecg/mml/ecg_compens.htmlhttp://medstat.med.utah.edu/kw/ecg/image_index/index.html

Marquette Electronics Copyright 1996


Heart Rate Variability - HRV

Standards of Measurement, Physiological Interpretation, and Clinical Use Task Force of the European Society of Cardiology the North American Society of Pacing Electrophysiology. Correspondence to Marek Malik, PhD, MD, Chairman, Writing Committee of the Task Force, Department of Cardiological Sciences, St George's Hospital Medical School, Cranmer Terrace,

London SW17 0RE, UK. http://circ.ahajournals.org/cgi/content/full/93/5/1043

http://circ.ahajournals.org/cgi/content/full/93/5/1043

http://circ.ahajournals.org/content/vol93/issue5/images/large/hc0560905002.jpeg

Variable Units Normal Values (mean±SD)

Time Domain Analysis of Nominal 24 hours

SDNN ms 141±39

SDANN ms 127±35

RMSSD ms 27±12

HRV triangular index 37±15

Spectral Analysis of Stationary Supine 5-min Recording

Total power ms2 3466 ±1018

LF ms2 1170±416

HF ms2 975±203

LF nu 54±4

HF nu 29±3

LF/HF ratio 1.5-2.0

Normal Values of Standard Measures of HRV

Typical Autonomic Response (ANS) Affecting Heart Rate

[Diagram From The Institute of HeartMath]http://home.iprimus.com.au/rboon/HeartRhythmsandHRV.htm


Typical ECG Signal Noise and Distortions

Clean

ECG

ECG+ noise+60hz

DWT Filtered

Signal

CleanECG

ECG+ noise+60hz

DWT FilteredSignal

Sym5 Processed Signal

CleanECG

ECG+ noise+60hz

DWT FilteredSignal

Db5 Processed Signal

CleanECG

ECG+ noise+60hz

DWT FilteredSignal

Peaks

Detected for signal recovery

Sym5 Processed Signal

CleanECG

ECG+ noise+60hz

DWT FilteredSignal

PeaksDetected for signal recovery

Db5 Processed Signal

Matlab DWT Threshold Selection Choices

• Wavelet decomposition is performed at level N and 'wname' is a string containing the name of the desired orthogonal wavelet

• TPTR: Threshold Selection Rules:

– 'rigrsure' use the principle of Stein's Unbiased Risk

– 'heursure' is an heuristic variant of the first option

– 'sqtwolog' for universal threshold

– 'minimaxi' for minimax thresholding (see thselect for more information)

– SORH ('s' or 'h') is for soft or hard thresholding (see wthresh for more information).

Matlab DWT Denoising function[XD,CXD,LXD] = wden(X,TPTR,SORH,SCAL,N,'wname')

Multiplicative Threshold Rescaling

• SCAL defines multiplicative threshold rescaling: – 'one' for no rescaling

– 'sln' for rescaling using a single estimation of level noise based on first-level coefficients

– 'mln' for rescaling done using level-dependent estimation of level noise

DWT ARHOS LP Filter

R.H. Istepanian, L.J. Hadjileontiadis, S.M Panas, ECG Data Compression Using Wavelets and Higher Order Statistics Methods, IEEE Transactions on Information Technology in Biomedicine, Vol 5, No.2 June 2001

Summary Research Goals • Signal processing algorithms for wireless skin

sensors with the following optimization features and objectives– Higher signal to noise output with lower input signal

power– Common interference signals identified and cancelled– Common and typical artifacts characterized, reduced

and / or cancelled– Reduced instruction set, software memory and

hardware requirements for processing of signal

Summary Research Goals

• Utilization of multiple wireless sensors to improve desired signal via constructive signal addition from the multiple sensor outputs.

• Processor hardware and software designed and optimized for low power consumption

• Sensors to include types for measuring pulse, respiration, blood oxygenation, glucose levels, bio-impedance, skin hydration, and body temperature.

Documents

Overview BWSSN Recap Skin Sensor Signal Types DSC Brief Review DWT Brief Review ECG Signal DWT ECG DWT ECG Adapted for Wireless Transmission DSC DWT ECG