Key result
A novel real-time algorithm detected transient cardiac ischemia episodes with 68.26% sensitivity and 74.91% positive predictivity on the long-term ST database.
Why the study?
Does a real-time algorithm using time-domain analysis and machine learning accurately detect transient cardiac ischemic episodes from ECG signals?
Does a real-time algorithm using time-domain analysis and machine learning accurately detect transient cardiac ischemic episodes from ECG signals?
A novel real-time machine learning algorithm demonstrates high sensitivity and positive predictivity for detecting transient cardiac ischemic episodes from ECG signals, achieving results comparable to off-line methods.
May facilitate real-time ischemia monitoring; leaves open prospective clinical validation.
We propose a new algorithm to detect and classify transient cardiac ischemia episodes, designed with the goal of providing a real-time execution without penalizing the classifier accuracy much. The algorithm is based on a novel mixture of time-domain analysis and machine learning techniques, specifically bagging of decision trees, and it has been developed using a well-recognized and freely distributed database, namely the long-term ST database. The ST episode detection sensitivity/positive predictivity using the annotation protocol A for this database is 68.26%/74.91%. The sensitivity result increases until 93.97% for the most dangerous episodes in terms of duration and magnitude (annotated according to protocol C). The test of the algorithm over the freely distributed part of the European Society of Cardiology database has shown results of sensitivity and positive predictivity of 83.33% and 77.31%, respectively. Those results are close to the results obtained by related works that present approaches to detect ischemia episodes off-line, which is remarkable if we take into account that in our real-time approach, less information is available during the classification process.
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Dranca et al. (2009) studied Transient cardiac ischemia episodes. Real-time detection algorithm vs. Off-line approaches was evaluated on ST episode detection sensitivity and positive predictivity. A novel real-time algorithm detected transient cardiac ischemia episodes with 68.26% sensitivity and 74.91% positive predictivity on the long-term ST database.