ECG Signal Delineation And Compression

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ECG Signal Delineation And Compression
T-61.181 Biomedical Signal Processing

• Chapters 6.2.6 6.3
• 18th November

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Outline

• ECG signal delineation
• Definition (What)
• Clinical and biophysical background (Why)
• Delineation as a signal processing (How)
• ECG signal compression
• General approach to data compression
• ECG signal compression (Intrabeat/Interbeat/Interl
  ead)
• Summary

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Part I.

• EGC signal delineation

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Delineation - Overview

• Aim Automatically decide/find onsets and
  offsets for every wave (P, QRS, and T) from ECG
  signal (PQRST-complex)
• Note! Experts (Cardiologist) use manual/visual
  approach

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Why?

• Why Clinically relevant parameters such as time
  intervals between waves, duration of each wave or
  composite wave forms, peak amplitudes etc. can
  be derived
• To understand this look how ECG signal is
  generated

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ECG Signal Generation
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What Are We Measuring?

• ECG gives (clinical) information from generation
  and propagation of electric signals in the heart.
• Abnormalities related to generation (arrhythmia)
  and propagation (ischemia, infarct etc.) can be
  seen in ECG-signal
• Also localization of abnormality is possible (12
  lead systems and BSM)

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Clinically Relevant Parameters
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Signal Processing Approach to Delineation (How)

• Clinical importance should now be clear
• Delineation can also be done manually by experts
  (cardiologist) ? expensive and time consuming. We
  want to do delineation automatically (signal
  processing)
• No analytical solution ? performance has to be
  evaluated with annotated databases

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Building Onset/Offset Detector

• Many algorithms simulate cardiologist manual
  delineation (ground truth) process
• Experts look 1) where the slope reduce to flat
  line 2) respect maximum upward, downward slope
• Simulate this define the boundary according to
  relative slope reduction with respect maximum
  slope ? LPD approach

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Low-Pass Differentiated (LPD)

• Signal is 1) low-pass filtered i.e. high
  frequency noise is removed (attenuated) and 2)
  differentiated dv/dt
• New signal is proportional to slope
• Operations can be done using only one FIR filter
  

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LPD cont.

• Each wave has a unique frequency band thus
  different low-pass (LP) filtering (impulse)
  responses are needed for each wave (P, QRS, and
  T)
• Design cut-off frequencies using Power Spectral
  Density (PSD)
• Differentiation amplifies (high freq.) noise and
  thus LP filtering is required

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LPD cont..

• Waves wP,QRS,T are segmented from the ith
  heart beat.
• Using initial and final extreme points thresholds
  for can be derived

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LPD cont...

• Constants are control the boundary detection they
  can be learnt from annotated database
• Search backwards from initial extreme point. When
  threshold is crossed ? onset has been detected
• Search forward from last extreme point and when
  threshold is crossed ? offset is detected.

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Part II.

• EGC signal compression

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General Data Compression

• The idea is represent the signal/information with
  fewer bits
• Any signal that contains some redundancy can be
  compressed
• Types of compression lossless and lossy
  compression
• In lossy compression preserve those features
  which carry (clinical) information

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ECG Data Compression

• Amount of data is increasing databases, number
  of ECG leads, sampling rate, amplitude resolution
  etc.
• ECG signal transmission
• Telemetry

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ECG Data Compression

• Redundancy in ECG data 1) Intrabeat 2)
  Interbeat, and 3) Interlead
• Sampling rate, number of bits, signal bandwidth,
  noise level and number of leads influence the
  outcome of compression
• Waveforms are clinically important (preserve
  them) whereas isoelectric segments are not (so)
  relevant

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Intrabeat Lossless Compression

• Not efficient has mainly historical value
• Sample is predicted as a linear combination of
  past samples and only prediction error is stored
  (smaller magnitude)

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Intrabeat Lossy CompressionDirect Method

• Basic idea Subsample the signal using parse
  sampling for flat segments and dense sampling for
  waves
• (n,x(n)), n0,...,N-1 ? (nk,x(nk)), k0,...,K-1

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Example AZTEC

• Last sampled time point is in n0
• Increment time (n) As long as signal in within
  certain amplitude limits (flat)

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Intrabeat Lossy Compression Transform Based
Methods

• Signal is represented as an expansion of basis
  functions
• Only coefficients need to be restored
• Requirement Partition of signal is needed
  (QRS-detectors)
• Method provides noise reduction

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Interbeat Lossy Compression

• Heart beats are almost identical (requires QRS
  detection, fiducial point)
• Subtract average beat and code residuals (linear
  prediction or transform)

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Interlead Compression

• Multilead (e.g. 12-lead) systems measure same
  event from different angles ? redundancy
• Extend direct and transform based method to
  multilead environment
• Extended AZTEC
• Transform concatenated signals

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Summary - part I

• Delineation automatically detect waves and
  their on- and offsets (What)
• Clinically important parameters are obtained
  (Why)
• Design algorithm that looks relative slope
  reduction (How)
• LPD-method Differentiate low-pass filtered
  signal

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Summary - part II

• Compression remove redundancy intrabeat,
  interbeat, and interlead
• Why Large amount of data, transmission and
  telemetry
• Lossless (historical) and lossy compression
• Notice which features are lost (isoelectric
  segments dont carry any clinical information)

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Summary - part II cont.

• Intrabeat 1) direct and 2) transform based
  methods
• 1) Subsample signal with non-uniform way
• 2) Use basis function (save only weights)
• Interbeat subtract average beat and code
  residuals (linear prediction or transform-coding)
• Interlead extend intrabeat methods to multilead
  environment

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Thank you!