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Information 2019, 10(2), 35; https://doi.org/10.3390/info10020035

Noisy ECG Signal Analysis for Automatic Peak Detection

1
INNOIT s.r.l., Via Einaudi, 10 – 70010 Locorotondo, Bari, Italy
2
Dipartimento di Ingegneria Elettrica e dell’Informazione, Politecnico di Bari, via E. Orabona, 4 – 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Received: 29 October 2018 / Revised: 17 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue eHealth and Artificial Intelligence)
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Abstract

Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively. View Full-Text
Keywords: ECG; peak detection; QRS; Hilbert transform; wavelet transform ECG; peak detection; QRS; Hilbert transform; wavelet transform
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D’Aloia, M.; Longo, A.; Rizzi, M. Noisy ECG Signal Analysis for Automatic Peak Detection. Information 2019, 10, 35.

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