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Customizing Training Dataset for Improved Heartbeat Recognition Performance in Long-Term ECG Signal Analysis

Can, Y. ; Pallauf, J. Pallauf ; Bhagavatula, B.V.K. Vijaya Kumar ; Coimbra, M.

Customizing Training Dataset for Improved Heartbeat Recognition Performance in Long-Term ECG Signal Analysis, Proc International Conf. of the IEEE Engineering in Medicine and Biology Society - EMBC, Boston, United States, Vol. 1, pp. 1 - 4, August, 2011.

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Abstract
This work presents an investigation of the potential benefits of customizing the analysis of long-term ECG signals, collected from individuals using wearable sensors, by incorporating small amount of data from these individuals in the training set of our classifiers. The global training dataset selected was from the MIT-BIH Arrhythmias Database. This proposal is validated on long-term ECG recordings collected via wearable technology in unsupervised environments, as well on the MIT-BIH Normal Sinus Rhythm Database. Results illustrate that heartbeat classification performance could improve significantly if short periods of data (e.g., data from the first 5-minutes of every 2 hours) from the specific individual are regularly selected and incorporated into the global training dataset for training a customized classifier.