Real-Time Extraction and Analysis of Key Morphological Features in the Electrocardiogram, for Data Compression and Clinical Decision Support

Abstract

Massive amounts of clinical data can now be collected by stand-alone or wearable monitors over extended periods of time. One key challenge is to convert the volumes of raw data into clinically relevant and actionable information, ideally in real-time. This becomes imperative especially in the domain of wearable monitors, where power and memory constraints prevent continuous communication of raw, uncompressed data to a base station for a health care provider. We focus here on algorithmic approaches to extract clinically meaningful information from the electrocardiogram (ECG) in realtime. We use a curve-length transform to identify, and aggregate from beat to beat, physiologically relevant timing information, such as the onsets and offsets of P-waves, QRS complexes, and T-waves, along with their respective magnitudes. Each heartbeat is thus parametrized in terms of 12 variables. Assuming a nominal heart-rate of 70 beats per minute, and a sampling frequency of 250 Hz, each beat has approximately 215 samples. Reducing each beat to 12 samples thus gives an 18-fold compression. An exponentially-weighted sliding average of the identified morphological features over the preceding twenty beats is also stored. Whenever any feature deviates significantly from its stored weighted average, the algorithm registers an alarm and also retains the raw ECG data of the 5 beats immediately preceding and following the anomalous occurrence, for a later review by a clinician.