Aprendizaje profundo y recurrente para la segmentación de sonidos cardíacos basado en características de frecuencia instantánea
| Autor: | Alvaro Joaquin Gaona, Pedro David Arini |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
PHONOCRDIOGRAM
Signal Processing (eess.SP) FOS: Computer and information sciences Computer Science - Machine Learning Sound (cs.SD) lcsh:Computer engineering. Computer hardware Computer science phonocardiogram lcsh:TK7885-7895 Instantaneous phase Computer Science - Sound Machine Learning (cs.LG) symbols.namesake fourier transform Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Segmentation Sensitivity (control systems) Electrical Engineering and Systems Science - Signal Processing LONG SHORT-TERM MEMORY purl.org/becyt/ford/2.11 [https] Phonocardiogram Artificial neural network business.industry Pattern recognition Recurrent neural network Fourier transform purl.org/becyt/ford/2 [https] Heart sounds symbols Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business long short-term memory lcsh:TK1-9971 FOURIER SYNCHROSQUEEZED TRANSFORM Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | CONICET Digital (CONICET) Consejo Nacional de Investigaciones Científicas y Técnicas instacron:CONICET Revista Elektrón, Vol 4, Iss 2, Pp 52-57 (2020) |
| DOI: | 10.48550/arxiv.2201.11320 |
| 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 its 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 a 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%. Comment: 7 figures, 6 pages, journal |
| Databáze: | OpenAIRE |
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