Victor Mondelo

While cardiovascular diseases are the leading causes of death in developed countries, detection of cardiac abnormalities can reduce mortality rates, through early and accurate diagnosis. One of the main assets used to help in the diagnosis process is the electrocardiogram (ECG). A free software tool for electrocardiogram analysis and diagnosis is presented. The tool, named ECGDT, allows: (1) to detect beats present on the ECG, both in single and multi-channel levels, (2) to identify beat waves, and (3) to diagnose different cardiac abnormalities. System evaluation was performed in two ways: (1) diagnostic capabilities were tested with Receiver Operating Characteristic (ROC) analysis, and (2) Graphical Software Interface (GUI) aspects, such as attraction, efficiency, or novelty, were evaluated employing User Experience Questionnaire (UEQ) scores. For disease diagnosis, the mean Area Under the ROC Curve (AUC) was 0.821. The system was also capable of detecting 100% of several cardiac abnormalities, such as bradycardia or tachycardia. Related to the GUI, all usability estimators scored values ranged between 2.208 and 2.750 (overall positive evaluations are obtained for values over 0.8). ECGDT could serve as an aid in the diagnosis of different medical abnormalities. In addition, the suitability of the developed interface has been proven.

This paper proposes a beat detection algorithm specially tailored to be used with 12 channel ECG records. The algorithm first obtains beat positions on each channel, and then combines this information to get an improved estimate. The detection process involves two stages: 1) single-channel detection: implemented by improving one of the most popular methods (Pan-Tompkins) developed to detect beat positions; and 2) multichannel detection: an algorithm that combines the information of the beat positions obtained in each of the 12 channels. In this way, our results clearly improve those obtained with the single-channel detection method, discarding detection errors, false positives, and duplicated beats. Besides, our single-channel method significantly reduces the temporal error when estimating the positions of QRS complexes. In the multichannel detection, the assessment of our algorithm against one-channel based approaches shows a significant improvement in detection outcome (Se = 99.86%, P+ = 99.98%, RMS RR Interval Error = 25.98 ms, F-Score = 0.9992), making it a good starting point for automatic diagnosis of heart conditions.

This paper proposes a computerized heartbeat detection method in single-channel electrocardiograms (ECGs). First, the well-known Pan-Tompkins technique was implemented, and next, a channel-dependent version was developed, by adjusting threshold values and reducing false QRS detections. The algorithms were tested with the MIT-BIH Arrhythmia Database (original algorithm), and with the St. Petersburg Database (modified version). When validating the performances of the original Pan-Tompkins algorithm, we have achieved a sensitivity of Se = 99.81, at a positive predictivity (P +) = 99.85%. The F-Score was 0.9587, and the RMS RR Interval Error (RMSRRIE) resulted to be 4,480.46 ms. When analysing the performance of the modified algorithm, results provided an average value of Se = 99.92%, P + = 99.98%, F-Score = 0.9718, and a mean value of 111.05 ms. for the RMSRRIE. In conclusion, the improved Pan-Tompkins algorithm provides higher values for sensitivity and positive predictivity, increased F-Score, and it significantly reduces the temporal error when estimating the positions of QRS complexes. Thus, it could be used as a starting point to detect heartbeat positions in more sophisticated computerized detection systems.