Customizing the training dataset to an individual for improved heartbeat recognition performance in long-term ECG signals

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Ye, C (Author) Other; Pallauf, J (Author) Other; Kumar, BV (Author) Other; Coimbra, MT (Author) FCUP

Conference proceedings International

Title: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Pages: 3322-3325

33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011

Boston, MA, 30 August 2011 through 3 September 2011

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Other information

Authenticus ID: P-008-1KW

DOI: 10.1109/iembs.2011.6090901

Abstract (EN): 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. © 2011 IEEE.

Language: English

Type (Professor's evaluation): Scientific