ECG Signal Delineation And Compression

Chapters 6.2.6 – 6.3

18th November

T-61.181 Biomedical Signal Processing

Outline

I. ECG signal delineation Definition (What) Clinical and biophysical background (Why) Delineation as a signal processing (How)

II. ECG signal compression General approach to data compression ECG signal compression

(Intrabeat/Interbeat/Interlead)

III. Summary

Part I.

EGC signal delineation

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

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

ECG Signal Generation

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)

Clinically Relevant Parameters

• PR interval SA ventricles

• QT interval ventricular fibrillation

• ST segment ischemia

• QRS duration Bundle brand block depolarization

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

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

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

LPD cont..

• Waves w={P,QRS,T} are segmented from the i:th heart beat.

• Using initial and final extreme points thresholds for can be derived

oteherwise

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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.

Part II.

EGC signal compression

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

ECG Data Compression

1) Amount of data is increasing: databases, number of ECG leads, sampling rate, amplitude resolution etc.

2) ECG signal transmission

3) Telemetry

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

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)), n=0,...,N-1 (nk,x(nk)), k=0,...,K-1

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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2

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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

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 don’t carry any clinical information)

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

Thank you!