Information Extraction from Multimodal ECG Documents

Abstract

With the rise of tools for clinical decision support,there is an increased need for automatic processing of electrocardiograms (ECG) documents. In fact, many systems have already been developed to perform signal processing tasks such as 12-lead off-line ECG analysis and real-time patient monitoring. All these applications require an accurate detection of the heart rate of the ECG. In this paper, we present the idea that the image form of ECG is actually a better medium to detect periodicity in ECG. When the ECG trace is scanned or rendered in videos, the peaks of the waveform (R-wave) is often traced thicker due to pixel dithering. We exploit the pixel thickness information, for the first time, as a reliable feature for determining periodicity. Results are presented on a database of 16,613 12-channel ECG waveforms, which demonstrate robustness and accuracy of our image-based period detection method on these ECGs of various cardiovascular diseases. 94.5% of bradycardia and tachycardia patient records are correctly identified using our estimated heart period as the disease criteria.