A collection of ECG feature extraction algorithms for MATLAB.
| ID | Feature |
|---|---|
| F1 | first statistical moment of the T wave distribution |
| F2 | second statistical moment of the T wave distribution |
| F3 | third statistical moment of the T wave distribution |
| F4 | fourth statistical moment of the T wave distribution |
| F5 | RT distance (R peak to T peak) |
| F6 | RT mid distance - in the case of biphasic waves |
| F7 | peakedness of the T wave |
| F8 | T wave amplitude |
| F9 | slope of the ascending part of the T wave |
| F10 | slope of the descending part of the T wave |
| F11 | ratio of first half T wave energy and whole T wave energy |
| F12 | ratio of second half T wave energy and whole T wave energy |
| F13 | R peak amplitude |
| F14 | R peak energy |
| F15 | ratio R peak energy and R peak amplitude |
| F16 | ST segment change (elevation or depression) |
| F17 | flag for biphasic T waves (0: monophasic, 1: biphasic) |
| F18 | R peak area under curve |
Highpass and lowpass filtering influences the morphology of the ECG. This is why this influence was evaluated for the proposed features and recommendations are given to prevent a distortion by wrong filtering.
| F1 | F2 | F3 | F4 | F5 | F6 | F7 | F8 | F9 | |
|---|---|---|---|---|---|---|---|---|---|
| Lowpass cutoff freq. | 20 | 40 | 40 | 40 | 20 | 20 | 40 | 40 | 40 |
| Highpass cutoff freq. | 0.05 | 0.10 | 0.10 | 0.10 | 0.05 | 0.05 | 0.10 | 0.10 | 0.10 |
| F10 | F11 | F12 | F13 | F14 | F15 | F16 | F17 | F18 | |
|---|---|---|---|---|---|---|---|---|---|
| Lowpass cutoff freq. | 40 | 40 | 60 | 70 | 50 | 60 | 40 | - | 50 |
| Highpass cutoff freq. | 0.10 | 0.10 | 0.30 | 0.40 | 0.20 | 0.30 | 0.10 | - | 0.20 |
The structure of the repository is as follows:
./algorithms contains the feature extraction algorithms
./dependencies contains other projects this one is using
./examples/study contains a robustness study of the feature algorithms as well as the calculation of recommendations for filtering without interfering the results from the feature extraction
./examples/patient_data contains an example showing how a workflow with a clinical signal from [1][2] could look like
Further algorithms can be found in the development branch and will be added to the main branch in one of the next releases.
[1] R. Bousseljot, D. Kreiseler, and A. Schnabel, “Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet,” Biomedizinische Technik/Biomedical Engineering, pp. 317–318, Jan. 2009.
[2] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C. K. Peng, and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.,” Circulation, vol. 101, no. 23, pp. E215–20, Jun. 2000.