A robust knowledge-based technique for ischemia detection in noisy ECGs

In cases where the signal-to-noise ratio (SNR) in ECGs is very poor, the correct definition of characteristics such as the isoelectric line and the J-point (beginning of the ST segment) is difficult. Inaccurate definition of those ECG characteristics can lead an automated ischemia detector to an incorrect diagnosis. We propose a method capable of extracting from noisy long duration ECG recordings those ECG characteristics that can be used for myocardial ischemia detection and analysis. We tested the performance of the method using noisy ECGs from the European Society of Cardiology ST-T database (ESC ST-T database). The results were more than satisfactory and the performance of our ischemia detector was improved in all cases. The proposed technique has low computational effort and can be executed in real time