Automated detection of transient ST-segment episodes in 24 h electrocardiograms.
A novel automated system is presented for improved detection of transient ischaemic and heart rate-related ST-segment episodes in 'real-world' 24 h ambulatory ECG data. Using a combination of traditional time-domain and Karhunen-Loève transform-based approaches, the detector derives QRS complex and ST-segment morphology feature vectors and, by mimicking human examination of feature-vector time series and their trends, tracks the time-varying ST-segment reference level owing to clinically unimportant, non-ischaemic causes, such as slow drifts, axis shifts and conduction changes. The detector estimates the slowly varying ST-segment level trend, identifies step changes in the time series and subtracts the ST-segment reference level thus obtained from the ST-segment level to obtain the ST-segment deviation time series, which are suitable for detection of ST-segment episodes. The detector was developed using the Long-term ST database containing 24 h ambulatory ECG records with human-expert annotated transient ischaemic and heart rate-related ST-segment episodes. The average ST episode detection sensitivity/positive predictivity obtained when using the annotations of the annotation protocol B of the database were 78.9%/80.7%. Evaluation of the detector using the European Society of Cardiology ST-T database as a test database showed average ST episode detection sensitivity/positive predictivity of 81.3%/89.2%, which are better performances, comparable with those of the systems being developed using the European database.
Smrdel
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A
Faculty of Computer & Information Science, University of Ljubljana, Ljubljana, Slovenia.
Jager
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F
D015716:Electrocardiography, Ambulatory / Q000379:methods*; D006801:Humans; D017202:Myocardial Ischemia / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation