c Copyright 2012 Anna Marie Rogers Dixon

129 Pages·2012·2.88 MB·English

(cid:13)c Copyright2012
AnnaMarieRogersDixon

Understanding the Practical Limitations of Applying Analog Compressed
Sensing Systems to ECG Signals
AnnaMarieRogersDixon
Adissertationsubmittedinpartialfulfillmentofthe
requirementsforthedegreeof
DoctorofPhilosophy
UniversityofWashington
2012
ReadingCommittee:
DavidJ.Allstot,Chair
RobertB.Darling
ManiSoma
ProgramAuthorizedtoOfferDegree:
UWElectricalEngineering

UniversityofWashington
Abstract
UnderstandingthePracticalLimitationsofApplyingAnalogCompressedSensingSystemstoECG
Signals
AnnaMarieRogersDixon
ChairoftheSupervisoryCommittee:
ProfessorDavidJ.Allstot
ElectricalEngineering
Body area networks (BAN), networks of wearable and wireless physiological sensors, are ex-
pected to have a profound positive impact in healthcare. The bio-signal sensors are equipped with
ultra-low power radios communicating to a BAN personal base station, and ultimately the health-
care provider. Most of the power dissipated in a state-of-the-art bio-signal sensor occurs when the
RF power amplifier transmits data to the personal base station. Thus, a method is desired that de-
creases the amount of data to be transmitted which reduces the duty cycle of the power amplifier
andincreasestheoverallenergyefficiency.
Compressed sensing (CS) is a compression scheme capable of significantly reducing a signal
acquisition’s data rate. CS requires only a few incoherent measurements to compress signals that
are sparse in some domain. Since compressed sensing is still an emerging topic, only a handful of
CS systems have been realized in hardware. These systems have shown promising and yet limited
abilities. Theobjectiveofthisresearchistoprovidedesignerswitharoadmapthatenablesthemto
more easily make correct decisions in designing analog CS encoders and decoders for bio-signals.
By showing the impact of the considerations of this CS system on ECG signals, it will set up a
frameworkforhowtoapproachand/oranalyzethedesignofthesesystemsforallbio-signals. The
CSroadmapaccomplishesthisgoalthisbydemonstratingtheimportanceofsignalsparsity,guides
the design of sensing matrix generation, addressing the impact of several analog CS imperfections
onCScompressionandguidestheselectionofproperCSreconstructionalgorithms.

TABLEOFCONTENTS
Page
ListofFigures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
ListofTables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
ListofAcronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Chapter1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 ApplyingCompressedSensingtoWirelessBodyAreaNetworks . . . . . . . . . . 1
1.2 ThesisContributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Chapter2: Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1 CompressedSensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 AdaptiveSamplingVersusCompressedSensing . . . . . . . . . . . . . . . . . . . 13
2.3 ExistingCSEncoderSolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.4 CompressedSensingSystemPerformanceMetrics . . . . . . . . . . . . . . . . . 18
Chapter3: SparsityandSamplingBasis . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.1 ImpactofSparsity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Time-DomainSamplingBasis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.3 Wavelet-DomainSamplingBasis . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 ThresholdingandSparsity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.5 SamplingDomainConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Chapter4: TheSensingMatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1 ImpactofRandomSensingMatrixIncoherencewithSamplingMatrix . . . . . . . 43
4.2 One-BitSensingMatrixGenerationHardware . . . . . . . . . . . . . . . . . . . . 50
4.3 SensingMatrixConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
i
Chapter5: CSAnalogEncoderSystemDesign . . . . . . . . . . . . . . . . . . . . . 60
5.1 TheAnalogCSModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.2 ThresholdingandDiagnosticQuality . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.3 AnalogtoDigitalConverterNon-idealities . . . . . . . . . . . . . . . . . . . . . . 74
5.4 AnalogCSOperatorNon-idealities . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.5 AnalogCSEncoderSystemDesignConclusions . . . . . . . . . . . . . . . . . . 92
Chapter6: CSDecoderandReconstructionAlgorithms . . . . . . . . . . . . . . . . . 93
6.1 CSReconstructionAlgorithmsOverview . . . . . . . . . . . . . . . . . . . . . . 93
6.2 CSReconstructionAlgorithmsPerformance . . . . . . . . . . . . . . . . . . . . . 96
6.3 CSReconstructionECGBio-signalCaseStudy . . . . . . . . . . . . . . . . . . . 97
6.4 CSDecoderConclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
Chapter7: ConclusionsandFutureWork . . . . . . . . . . . . . . . . . . . . . . . . 101
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
AppendixA: AnalogCSRowOperatorOperation . . . . . . . . . . . . . . . . . . . . . 109
AppendixB: Binary-WeightedChargeScalingDACAbsoluteAccuracy . . . . . . . . . 112
ii
LISTOFFIGURES
FigureNumber Page
1.1 WirelessBodyAreaNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 TimeDomainSparseECGWaveform . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Bio-SignalCSCODEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 AnalogCSSystemDesignRoadmap . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1 12-BallExampleofSamplingMethods . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 CompressedSensingofanECGSignal. . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 AdaptiveSamplingappliedtoanECGSignal . . . . . . . . . . . . . . . . . . . . 14
2.4 ExistingSpectralEstimationCSEncoderSystems . . . . . . . . . . . . . . . . . . 16
2.5 ExistingBio-SignalCSEncoderSystems . . . . . . . . . . . . . . . . . . . . . . 17
2.6 AnalogCSMDAC/Integrator[1] . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.7 ECGSampleforVariousPRDValues . . . . . . . . . . . . . . . . . . . . . . . . 20
3.1 SparsityvsPRDforCSReconstructedSignal . . . . . . . . . . . . . . . . . . . . 22
3.2 SparsityvsPRDforTime-DomainECGSignal . . . . . . . . . . . . . . . . . . . 23
3.3 ECGSparsityintheWaveletDomain . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4 DifferentSamplingViewsofaTime-BasedSignal . . . . . . . . . . . . . . . . . . 26
3.5 ECGSparsityintheWaveletDomain . . . . . . . . . . . . . . . . . . . . . . . . 28
3.6 FilterBankfor(a)DWTand(b)IDWT . . . . . . . . . . . . . . . . . . . . . . . 31
3.7 SparsityvsPRDforTimeandWaveletDomainsofECGSignals . . . . . . . . . . 32
3.8 SparsityvsPRDforVaryingDWTSystemDepths . . . . . . . . . . . . . . . . . 33
3.9 SparsityvsPRDforTimeandWaveletDomainsofTime-ThresholdedECGSignals 40
4.1 TimeDomainCoherenceHistograms. . . . . . . . . . . . . . . . . . . . . . . . . 46
4.2 WaveletDomainCoherenceHistograms . . . . . . . . . . . . . . . . . . . . . . . 47
4.3 Coherencevs. PRDforTimeandWaveletSamplingBases . . . . . . . . . . . . . 49
4.4 HardwarePseudorandomNumberGenerators . . . . . . . . . . . . . . . . . . . . 52
4.5 SQNRvs. CompressionRatioforDifferentOne-BitMatrixStructures . . . . . . . 54
5.1 AnalogCSBlockDiagram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 AnalogCSRowOperator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
iii
5.3 ECGCompressedOutputMagnitudeDistribution . . . . . . . . . . . . . . . . . . 65
5.4 AnalogCSSystemwithADCNoiseModelECGSparsityvs. SNR . . . . . . . . . 67
5.5 ImpactofThresholdingonECGDiagnosticQuality . . . . . . . . . . . . . . . . . 69
5.6 EffectofThresholdingonANSI/AAMIStandardsCompliance . . . . . . . . . . . 72
5.7 SampleECGSignalCSCompressionwithoutThresholding. . . . . . . . . . . . . 73
5.8 AnalogCSSystemwithIdealSARADCandFixedPre-AmplificationECGSparsity
vs. SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.9 CSPre-processingGainvs. ECGCompressedOutputSNR . . . . . . . . . . . . . 79
5.10 Analog CS System with Ideal SAR ADC and Calibrated Pre-Amplification ECG
Sparsityvs. SNR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.11 ADCTransferCurvewithOffset . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.12 AnalogCSSystemwithADCOffsetvs. SNR . . . . . . . . . . . . . . . . . . . . 84
5.13 AnalogCSSystemwithADCGainErrorvs. SNR. . . . . . . . . . . . . . . . . . 84
5.14 Binary-WeightedChargeScalingDAC[2] . . . . . . . . . . . . . . . . . . . . . . 86
5.15 Binary-WeightedDACMaximumAbsoluteErrorvs. CapacitorTolerance . . . . . 87
5.16 Binary-WeightedDACMaximumAbsoluteErrorvs. SNR . . . . . . . . . . . . . 88
5.17 AnalogCSSystemwithAnalogCSOperatorOffsetvs. SNR . . . . . . . . . . . . 90
5.18 AnalogCSSystemwithAnalogCSOperatorGainErrorvs. SNR . . . . . . . . . 91
6.1 CSReconstructionComputationTimeComparison . . . . . . . . . . . . . . . . . 96
6.2 CSReconstructionECGSignalAccuracyComparison . . . . . . . . . . . . . . . 98
6.3 CSReconstructionComputationTimeComparison . . . . . . . . . . . . . . . . . 99
A.1 One-BitAnalogCSRowOperator . . . . . . . . . . . . . . . . . . . . . . . . . . 110
A.2 One-BitAnalogCSRowOperatorMultiplicationMode . . . . . . . . . . . . . . . 110
A.3 One-BitAnalogCSRowOperatorMultiplicationMode . . . . . . . . . . . . . . . 111
B.1 Binary-WeightedChargeScalingDAC[2] . . . . . . . . . . . . . . . . . . . . . . 112
iv