1. ECG Signal Delineation And Compression Chapters 6.2.6 – 6.3 18th November T-61.181 Biomedical Signal Processing

2. 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

3. Part I. EGC signal delineation

4. 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

5. 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

6. ECG Signal Generation

7. 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)

8. Clinically Relevant Parameters PR interval SA  ventricles QT interval ventricular fibrillation ST segment ischemia QRS duration Bundle brand block depolarization

9. 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

10. 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

11. 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 :

12. 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

13. 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

14. 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.

15. Part II. EGC signal compression

16. 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

17. 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

18. 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

19. 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):

20. Intrabeat Lossy Compression Direct 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

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

22. 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

23. Interbeat Lossy Compression Heart beats are almost identical (requires QRS detection, fiducial point) Subtract average beat and code residuals (linear prediction or transform)

24. 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

25. 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

26. 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)

27. 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

28. Thank you!