| Autor: |
Alvaro Joaquin Gaona, Pedro David Arini |
| Jazyk: |
English<br />Spanish; Castilian<br />Portuguese |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
Revista Elektrón, Vol 4, Iss 2, Pp 52-57 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
2525-0159 |
| DOI: |
10.37537/rev.elektron.4.2.101.2020 |
| Popis: |
In this work, a novel stack of well-known technologies is presented to determine an automatic method to segment the heart sounds in a phonocardiogram (PCG). We will show a deep recurrent neural network (DRNN) capable of segmenting a PCG into their main components and a very specific way of extracting instantaneous frequency that will play an important role in the training and testing of the proposed model. More specifically, it involves an Long Short-Term Memory (LSTM) neural network accompanied by the Fourier Synchrosqueezed Transform (FSST) used to extract instantaneous time-frequency features from a PCG. The present approach was tested on heart sound signals longer than 5 seconds and shorter than 35 seconds from freely-available databases. This approach proved that, with a relatively small architecture, a small set of data and the right features, this method achieved an almost state-of-the-art performance, showing an average sensitivity of 89.5%, an average positive predictive value of 89.3% and an average accuracy of 91.3%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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