Bài giảng Biomedical signal processing and modeling - Detection of Events
Detection of the P Wave in the ECG (2), Solution 1
1. The QRS is detected, and replaced with the baseline. The baseline is determined by analyzing a few samples preceding the QRS complex.
2. The resulting signal is bandpass filtered with –3dB points at 3 Hz and 11 Hz.
3. The end of the preceding T wave is estimated with reference to the current QRS by using QTmax = 2RR/9 + 250 ms, where RR is the interval between two successive QRS complexes.
4. The maximum and minimum values are found in all three VCG leads in the search interval from the preceding T wave to the current QRS.
5. The signal is rectified and thresholded at 50% and 75% of the maximum to obtain a ternary (three-level) signal.
6. The cross-correlation of the result is computed with a ternary template derived in a manner similar to the procedure in the previous step from a representative set of P waves.
7. The peak in the cross-correlation corresponds to the P location in the original ECG.
Detection of the P Wave in the ECG (3), Solution 2
1. The QRS is first detected by applying a threshold to L(N, w, t), with w set equal to the average QRS width.
2. The onset (start) & offset (end) points of the QRS are represented by a pulse waveform.
3. The QRS complexes in the signals are then replaced by the isoelectric baseline of the signals, the procedure is repeated with w set equal to the average T duration, and the T waves are detected.
4. The same steps are repeated to detect the P waves.
1. The QRS is detected, and replaced with the baseline. The baseline is determined by analyzing a few samples preceding the QRS complex.
2. The resulting signal is bandpass filtered with –3dB points at 3 Hz and 11 Hz.
3. The end of the preceding T wave is estimated with reference to the current QRS by using QTmax = 2RR/9 + 250 ms, where RR is the interval between two successive QRS complexes.
4. The maximum and minimum values are found in all three VCG leads in the search interval from the preceding T wave to the current QRS.
5. The signal is rectified and thresholded at 50% and 75% of the maximum to obtain a ternary (three-level) signal.
6. The cross-correlation of the result is computed with a ternary template derived in a manner similar to the procedure in the previous step from a representative set of P waves.
7. The peak in the cross-correlation corresponds to the P location in the original ECG.
Detection of the P Wave in the ECG (3), Solution 2
1. The QRS is first detected by applying a threshold to L(N, w, t), with w set equal to the average QRS width.
2. The onset (start) & offset (end) points of the QRS are represented by a pulse waveform.
3. The QRS complexes in the signals are then replaced by the isoelectric baseline of the signals, the procedure is repeated with w set equal to the average T duration, and the T waves are detected.
4. The same steps are repeated to detect the P waves.
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03/04/2025
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Nội dung text: Bài giảng Biomedical signal processing and modeling - Detection of Events
- Nguyễn Công Phương BIOMEDICAL SIGNAL PROCESSING AND MODELING Detection of Events
- Contents I. Introduction II. Concurrent, Coupled, and Correlated Processes III. Filtering for Removal of Artifacts IV. Detection of Events V. Analysis of Waveshape and Waveform Complexity VI. Frequency Domain Characterization VII.Modeling Biomedical Systems VIII.Analysis of Nonstationary and Multicomponent Signals IX. Pattern Classification and Diagnostic Decision sites.google.com/site/ncpdhbkhn 2
- Detection of Events 1. Introduction 2. Detection of Events and Waves 3. Correlation Analysis of EEG Rhythms 4. The Matched Filter 5. Detection of the P Wave in the ECG 6. Homomorphic Filtering sites.google.com/site/ncpdhbkhn 3
- Introduction (1) [Bronzino, 2006] sites.google.com/site/ncpdhbkhn 4
- Introduction (2) • Biomedical signals carry signatures of physiological events. • The part of a signal related to a specific event of interest is often referred to as an epoch. • Analysis of a signal for monitoring or diagnosis requires the identification of epochs and investigation of the corresponding events. • Detection of events is an important step in biomedical signal analysis. sites.google.com/site/ncpdhbkhn 5
- Detection Noisy ECG signal Detecti on Original ECG signal Introduction Introduction (3) sites.google.com/site/ncpdhbkhn 6
- Detection of Events 1. Introduction 2. Detection of Events and Waves a) Derivative–based methods for QRS detection b) The Pan–Tompkins algorithm for QRS detection c) Detection of the dicrotic notch 3. Correlation Analysis of EEG Rhythms 4. The Matched Filter 5. Detection of the P Wave in the ECG 6. Homomorphic Filtering sites.google.com/site/ncpdhbkhn 7
- Derivative–based methods for QRS detection (1) [Bronzino, 2006] sites.google.com/site/ncpdhbkhn 8
- Derivative–based methods for QRS detection (2) xn[]− xn [ − 2 ] y[ n ] = 2T = − − yn0[] xn [] xn [2 ] = − −+ − yn1[] xn []2 xn [ 2 ][ xn 4 ] = + yn2[]13 . yn 0 [] 11 . yn 1 [] sites.google.com/site/ncpdhbkhn 9
- Detection Second derivative ECG signal Derivative–based Derivative–based methods for QRS detection detection QRS (3) sites.google.com/site/ncpdhbkhn Detection Second derivative Noisy ECG signal 10
