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Instantaneous P- and T-wave detection: Assessment of three ECG fiducial points detection algorithms

: Leutheuser, Heike; Gradl, Stefan; Anneken, Lars; Arnold, Martin; Lang, Nadine; Achenbach, Stephan; Eskofier, Bjoern M.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2016. Proceedings : 14-17 June 2016, San Francisco, California
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-5090-3088-0 (Print)
ISBN: 978-1-5090-3087-3 (Online)
International Conference on Wearable and Implantable Body Sensor Networks (BSN) <13, 2016, San Francisco/Calif.>
Fraunhofer IIS ()

Arrhythmia detection algorithms require the exact and instantaneous detection of fiducial points in the ECG signal. These fiducial points (QRS-complex, P- and T-wave) correspond to distinct cardiac contraction phases. The performance evaluation of different fiducial points detection algorithms require the existence of large databases (DBs) encompassing reference annotations. Up to last year, P- and T-wave annotations were only available for the QT DB. This was addressed by Elgendi et al. who provided P- and T-wave annotations to the MIT-BIH arrhythmia DB. A variety of ECG fiducial points detection algorithms exists in literature, whereas, to the best knowledge of the authors, we could not identify any single-lead algorithm ready for instantaneous P- and T-wave detection. In this work, we present three P- and T-wave detection algorithms: a revised version for QRS detection using line fitting capable to detect P- and T-wave, an expeditious version of a wavelet based ECG delineation algorithm, and a fast naive fiducial points detection algorithm. The fast naive fiducial points detection algorithm performed best on both DBs with sensitivities ranging from 73.0% (P-wave detection, error interval of 40 ms) to 89.4% (T-wave detection, error interval of 80 ms). As this algorithm detects a wave event in every search window, it has to be investigated how this affects arrhythmia detection algorithms. The reference Matlab implementations are available for download to encourage the development of high-accurate and automated ECG processing algorithms for the integration in daily life using mobile computers.