{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T20:07:28Z","timestamp":1785528448119,"version":"3.56.0"},"reference-count":53,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,24]],"date-time":"2019-02-24T00:00:00Z","timestamp":1550966400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19040957","type":"journal-article","created":{"date-parts":[[2019,2,25]],"date-time":"2019-02-25T03:06:52Z","timestamp":1551064012000},"page":"957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Wavelet Transform-Based Neural Network Denoising Algorithm for Mobile Phonocardiography"],"prefix":"10.3390","volume":"19","author":[{"given":"Dawid","family":"Gradolewski","sequence":"first","affiliation":[{"name":"Blekinge Institute of Technology, Institute of Applied Signal Processing, 371 79 Karlskrona, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7558-1490","authenticated-orcid":false,"given":"Giovanni","family":"Magenes","sequence":"additional","affiliation":[{"name":"Dipartimento di Ingegneria Industriale e dell\u2019Informazione, University of Pavia, 27100 Pavia, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sven","family":"Johansson","sequence":"additional","affiliation":[{"name":"Blekinge Institute of Technology, Institute of Applied Signal Processing, 371 79 Karlskrona, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wlodek J.","family":"Kulesza","sequence":"additional","affiliation":[{"name":"Blekinge Institute of Technology, Institute of Applied Signal Processing, 371 79 Karlskrona, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compbiomed.2016.01.017","article-title":"Phonocardiogram signal compression using sound repetition and vector quantization","volume":"71","author":"Hong","year":"2016","journal-title":"Comput. 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