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README.md
Abstract
The Wilson Central Terminal ECG Database (WCTECGdb) contains the Wilson Central Terminal (WCT), the voltage of right arm (RA), left arm (LA) and left leg (LL), six unipolar chest leads associated with three limb leads and six precordial leads.
With our ECG device, the potentials of RA, LA, LL and six true unipolar leads are measured using the right leg as the reference point. Having this configuration, we have been able to overcome the difficulty of recording the WCT in a clinical setting. Our data confirmed once again that the WCT does not have a small value, and could be as high as 241% of lead II and has standard ECG characteristics such as the p-wave and the t-wave. The dataset comprises of 540 ten-second segments recorded from 92 patients.
Background
Precordial leads are recorded as the potential differences between the electrodes on the chest and the reference point of the body, named Wilson central terminal. The WCT was initially assumed to have null amplitude. Therefore, precordial leads are also known as unipolar leads. However, this assumption has been found to be incorrect. In other words, the so-called unipolar leads are not really unipolar, but this systematic error has been accepted widely due to the difficulty of recording the WCT signal.
Methods
Our ECG device is designed to record traditional ECG signals in addition to the nine true unipolar leads: three limb potentials (LA, RA, LL) and six unipolar precordial leads (UV1: UV6). We are able to measure the WCT signal by averaging the three limb potentials. The true unipolar leads are the raw biopotentials measured from the exploring electrodes directly referred to the right leg (RL).
Data Description
The ECG signals were recorded from 92 patients (27 were female). Each recording is segmented to ten-second sections. As the total recording duration was different for each participant, the number of segments per patient ranges from one to thirty-one. The total number of ten-second segments is 540 for all patients. The average age of the patient population is 65.23 years (with a standard deviation of 12.12 years); the majority of the patients had a history of cardiac disease and had been admitted to the hospital.
We applied the low pass filter, and dc-removal filter to clean the dataset, and improve the signal to noise ratio. Both the raw and cleaned data are included in this dataset. The raw data is specified by -Raw in the signal names (e.g. V1-Raw). Each segment contains 37 signals listed below:
-
Raw
- Three limb leads (I, II, III)
- Six precordial leads (V1: V6)
- Three limb potentials (LA, RA, LL)
- Six true unipolar chest leads (UV1: UV6)
-
Clean
- Three limb leads (I, II, III)
- Six precordial leads (V1: V6)
- Three limb potentials (LA, RA, LL)
- Six true unipolar chest leads (UV1: UV6)
- WCT
We included age, gender, and patient diagnosis in the header file for each segment. We did not receive the patient diagnosis from the hospital for ten patients. Consequently, patient diagnosis is filled as "not reported" for these patients.
The precordial leads (V1: V6) can be reconstructed using true unipolar chest leads (UV1: UV6) and the WCT signal (V1: V6= UV1: UV6 - WCT). We included synthesized precordial leads instead of directly measured signals for a total of 8 segments (files) from 5 patients due to poor signal to noise ratio and/or the final stage amplifier saturation.
List of patients with reconstructed precordial leadsPatient ID | Segment ID / [reconstructed precordial leads] |
---|---|
patient007 |
seg1 / [V2, V2-Raw] seg2 / [V2, V2-Raw] seg3/[V1, V1-Raw] |
patient008 | seg1/ [V1, V2, V1-Raw, V2-Raw] seg2/ [V1, V2, V1-Raw, V2-Raw] |
patient010 | seg1/ [V2, V2-Raw] |
patient014 | seg1/ [V2, V2-Raw] |
patient031 | seg1/ [V2, V2-Raw] |
Usage Notes
Further details of the ECG hardware can be found in the accompanying papers by Gargiulo et al [1-3]. The files are provided in WFDB format, so can be loaded using any of the WFDB software packages.
Release Notes
- We previously included only the reconstructed signals for the cleaned data. In the new version, we also reconstructed the raw data for five patients (patient007, patient008, patient010, patient014, patient031).
- We used a bandpass filter (0.05 HZ-150HZ) to clean the signals in this version, while the bandpass filter was previously (0.05 HZ-20HZ).
- Patient061: the patient diagnosis was "Non ST segment elevation myocardial infarction (NSTEMI)" in the previous version which is updated to "Supraventricular tachycardia (SVT)".
Acknowledgements
We wish to thank the patients at Campbelltown Hospital (Campbelltown, Australia) who volunteered for this study, the nurses and the ECG technicians who helped with the data collection, Mr. Colin Symons (Technical Officer at the MARCS Institute, Western Sydney University) that helped with construction of the prototype, developed the custom ECG cable required as well as the device enclosure.
This study is funded by the office for Research Engagement and Development with Industry (REDI) at the Western Sydney University and by the MARCS Institute.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Gargiulo, G. D., Varaki, E. S., Hamilton, T. J., Bifulco, P., Cesarelli, M., & Romano, M. A 9-independent-leads ECG system from 10 electrodes: A practice preserving WCT-less true unipolar ECG system. IEEE Biomedical Circuits and Systems Conference (BioCAS), 2015. doi:10.1109/biocas.2015.7348300
- Gargiulo, G. D. True Unipolar ECG Machine for Wilson Central Terminal Measurements. BioMed Research International, 2015, 1-7. doi:10.1155/2015/586397
- Gargiulo, G. D., Mcewan, A. L., Bifulco, P., Cesarelli, M., Jin, C., Tapson, J., Schaik, A. V. Towards true unipolar ECG recording without the Wilson central terminal (preliminary results). Physiological Measurement, 2013, 34(9), 991-1012. doi:10.1088/0967-3334/34/9/991
When using this resource, please cite: (show more options)
Moeinzadeh, H., & Gargiulo, G. (2019). Wilson Central Terminal ECG Database (version 1.0.1). PhysioNet. https://doi.org/10.13026/f73z-an96.
Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff,
J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000).
PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research
resource for complex physiologic signals. Circulation [Online]. 101
(23), pp. e215–e220.
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