Event detection. Biosignal processing, S Autumn 2017

Transcription

1 Evet detectio Biosigal processig, 573S Autum 07

2 ECG evet detectio P wave: depolarizatio of the atrium QRS-complex: depolarizatio of vetricle T wave: repolarizatio of vetricle Each evet represets oe phase of the electrical activity of the heart fuctioig Ay deviatios from the ormal may imply importat pathophysiologic chages i the heart tissue

3 ECG evets Morphological aalysis of waveforms: first QRS evet is detected, the other waves are detected, fially their shapes ad timig are aalyzed Right budle-brach block ad hypertrophy - Wideed QRS complex + jagged shape Premature vetricular cotractio - Abormal timig ad wave shape

4 Heart rate variability: arrhythmias Tachogram: RR-iterval series Detectio of aomalies of heart rhythm

5 PVC detectio from heart rate sigal A extra beat followed by compesatory pause Simple threshold criteria are used for detectio: Maximum [%] allowed chage betwee cosecutive beat itervals Maximum [ms] allowed chage betwee cosecutive beat itervals Ectopic beat i the R-R iterval time series of a MI patiet. Ectopic beat appears as a short R-R iterval followed by a compesatory pause.

6 Detectio of QRS Pa & Tompkis Oly the basic idea is preseted here. Further details ca be foud i the origial paper: Pa J, Tompkis WJ. A real-time QRS detectio algorithm. IEEE Trasactios o Biomedical Egieerig, Vol. BME-3, No. 3, March 985, pp The algorithms are give for samplig frequecy Fs = 00Hz.

7 Detectio of QRS Badpass filterig is performed i two steps Supresses oise ad power-lie iterferece. LP filterig Fc=Hz:. HP filterig Fc=5Hz: z z z z z z z H 6 3 x x x y y y z z z z H x x x x y y

8 Detectio of QRS Derivative operator is applied ext: Supresses slow compoets: P ad T waves Amplifies fast compoets: QRS wave Squarig: y x x x 3 x 8 y x Produces positive values, further supresses T ad P, ad further amplifies QRS Movig widow itegratio: Smoothes squared sigal y Widow size N: too large value will fuse waves together, too small value will produce split peaks 4 N N i0 x i

9 Detectio of QRS Adaptive thresholdig for peak detectio Peak defiitio: a local maximum whe sigal amplitude chages directio i a small widow Defiitios: SPKI: QRS peak hight estimate NPKI: oise peak hight estimate PEAKI: curret peak uder cosideratio THRESHOLD_I: primary threshold for peak detectio THRESHOLD_I: secodary threshold for peak detectio 573S Biosigal Processig

10 Detectio of QRS Fid the ext peak from sigal: PEAKI However, if o peak is foud withi the MISSED time widow, perform as istructed o the ext slide ad come back here to cotiue from threshold update step below If PEAKI > THRESHOLD_I QRS is detected here! SPKI = PEAKI x /8 + SPKI x 7/8 Else oise peak foud istead NPKI = PEAKI x /8 + NPKI x 7/8 Update the thresholds: THRESHOLD_I = NPKI + SPKI-NPKI/4 THRESHOLD_I = THRESHOLD_I / Look for the ext peak update sigal amplitude estimate update oise amplitude estimate MISSED Th_I Th_I SPKI NPKI

11 Detectio of QRS However: if o peak PEAKI was foud withi the MISSED time widow of.66 x RR_AVERAGE from the previous beat the fid the highest peak withi the MISSED time widow if PEAKI > THRESHOLD_I a lowered QRS peak was foud SPKI = PEAKI x /4 + SPKI x 3/4 update sigal... else oise peak was foud istead NPKI = PEAKI x /8 + NPKI x 7/8 update oise... GO back to threshold updatig o previous slide RR_AVERAGE: the average beat iterval of 8 those previous beats which are withi the limits of: 0.9 x RR_AVERAGE.6 x RR_AVERAGE RR_AVERAGE is updated at every QRS detectio!

12 Detectio of QRS

13 Detectio of QRS

14 Rhythmicity aalysis of EEG Autocorrelatio fuctio, spectrum power spectral desity, PSD Cross-correlatio fuctio, cross-spectrum Coherece aalysis

15 Examples of evets: EEG EEG record ca be described i terms of: The most persistet rhythm e.g., α frequecy The presece of other rhythmic features, such as δ, θ, β frequecies Discrete features of relatively log duratio, such as a episode of spike-ad-wave activity Discrete features of relatively short duratio, such as isolated spikes or sharp waves The activity remaiig whe all previous features have bee described, backgroud activity Artifacts givig rise to ambiguity i iterpretatio K-complex Lambda wave Mu rhythm Spike Sharp waves Spike-ad-wave complexes Sleep spidle Vertex sharp wave Polyspike discharges Bad: δ θ α β f [Hz]

16 Autocorrelatio fuctio, periodogram Periodogram estimate of PSD ca be achieved through Fourier trasformig the autocorrelatio fuctio of the sigal PSD ca also be computed from DFT of the widowed sigal: N N m m j m e S 0 m N m x x N m Delay m m m 0 M j i w e w x ME S 0 M w w M E Sω ω

17 Autocorrelatio, PSD

18 Cross-correlatio fuctio, cross-spectrum Cross-correlatio fuctio betwee two sigals Are there similar waveforms i the sigals? Repetitio? Cross-spectrum Is there power i the same frequecies i the two sigals? Either: m S xy xy N N m 0 N xy m N x y m e jm m Delay m y x m m Or: X Y * S xy S xy ω ω

19 Cross-correlatio fuctio, cross-spectrum

20 Coherece fuctio Normalized cross-spectrum Is there correlatio betwee the spectral powers i various frequecies i the sigals? Values are i the rage 0- * Y X Y X xy Г xy ω ω

21 Detectio of pulses: Matched filter Covolutio of a pulse sample y ad a sigal x Time-reversed pulse sample forms the covolutio kerel A kid of cross-correlatio operatio Fids waveshape matches: similar pulse istaces as the pulse sample g M m0 x m y m High respose whe pulse preset, otherwise low respose Negative-valued resposes: the foud wave shape is upsidedow

22 Pulse detectio EEG example Spike-ad-wave complex Detectio results

23 Selected refereces Course book: Chapter 4 Joural article Pa J, Tompkis WJ. A real-time QRS detectio algorithm. IEEE Trasactios o Biomedical Egieerig, Vol. BME-3, No. 3, March 985, pp