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A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Univ Pavia, ITA.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 4, article id 957Article in journal (Refereed) Published
Abstract [en]

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire.

Place, publisher, year, edition, pages
MDPI , 2019. Vol. 19, no 4, article id 957
Keywords [en]
adaptive filters, auscultation techniques, auto-diagnostic system, cardiovascular pathologies, Inverse Wavelet Transform (IWT), noise cancellation, signal denoising, Time Delay Neural Networks (TDNN)
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-17760DOI: 10.3390/s19040957ISI: 000460829200208PubMedID: 30813479OAI: oai:DiVA.org:bth-17760DiVA, id: diva2:1302229
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open access

Available from: 2019-04-04 Created: 2019-04-04 Last updated: 2019-05-21Bibliographically approved

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Gradolewski, DawidJohansson, SvenKulesza, Wlodek

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