If you wish to use this database in your research, you must cite these articles:
This work proposed the developement of a stress test analysis method for the diagnostic of the ischemic cardiopathy. For this, a new evaluation criteria and a complete analysis method was created. During this research a database was created containing the data from three ECG channels, acquired in the rehabilitation unit of the Caracas' University Hospital.
The analysis was made on 31 registries of healthy subjects and 42 ischemic patients. The ischemic patients were diagnosed using the traditional criteria by the medical staff, resulting in 19 presenting the sickness and 23 resulting false positives, showing the difficulty in the interpretation of the data.
The processing of the ECG signal included a QRS complex detection, the alignment and averaging of the heartbeats, the mesuring of the ST segment's elevation and slope and the mesuring or extraction of different parameters. These parameters were: ST segment elevation, maximum heart rate, delta ST/HR index, ST segment slope, ST/HR slope. These parameters were hierarchized using discriminant measures and the Fisher discriminant.
It was concluded that the most sensible parameters for the diagnostic were the heart rate, the delta-ST/HR index and the ST/HR slope. Two methods were used for patern recognition: closest mesure and the Fischer clasifier in order to make a preliminary evaluation of the database.
The main result of this work was finding out that the best evaluation method in the stress test is using the heart rate and the delta-ST/HR index as parameters and using a closest mesure clasifier. This method presented a 78.6% sinsibility over the 45.2% obtained using the traditional method, thus demonstraiting the potential of this approach.
In cooperation with the Caracas' University Hospital, ECG registries from derivations II, V5 and V6 were collected under the following conditions:
This MATLAB function is used to read the registries acquired using the Signal Acquisition System (SISPAS). The function definition is as follows:
These plots are used to check that the signals were correctly read.
It's important to take into account there has been no preprocessing (other than that made by SISPAS), so the signals may need some processing before they are useful.