Departamento de Ingeniería de Telecomunicación
URI permanente para esta comunidadhttps://hdl.handle.net/10953/39
En esta Comunidad se recogen los documentos generados por el Departamento de Ingeniería de Telecomunicación y que cumplen los requisitos de Copyright para su difusión en acceso abierto.
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Examinando Departamento de Ingeniería de Telecomunicación por Autor "Cañadas Quesada, Francisco Jesús"
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Ítem An incremental algorithm based on multichannel non-negative matrix partial co-factorization for ambient denoising in auscultation(Elsevier, 2021-11) De La Torre Cruz, Juan; Cañadas Quesada, Francisco Jesús; Martínez Muñoz, Damián; Ruiz Reyes, Nicolás; García Galán, Sebastián; Carabias Orti, Julio JoséOne of the major current limitations in the diagnosis derived from auscultation remains the ambient noise surrounding the subject, which prevents successful auscultation. Therefore, it is essential to develop robust signal processing algorithms that can extract relevant clinical information from auscultated recordings analyzing in depth the acoustic environment in order to help the decision-making process made by physicians. The aim of this study is to implement a method to remove ambient noise in biomedical sounds captured in auscultation. We propose an incremental approach based on multichannel non-negative matrix partial co-factorization (NMPCF) for ambient denoising focusing on high noisy environment with a Signal-to-Noise Ratio (SNR) 6 5 dB. The first contribution applies NMPCF assuming that ambient noise can be modelled as repetitive sound events simultaneously found in two single-channel inputs captured by means of different recording devices. The second contribution proposes an incremental algorithm, based on the previous multichannel NMPCF, that refines the estimated biomedical spectrogram throughout a set of incremental stages by eliminating most of the ambient noise that was not removed in the previous stage at the expense of preserving most of the biomedical spectral content. The ambient denoising performance of the proposed method, compared to some of the most relevant state-of-the-art methods, has been evaluated using a set of recordings composed of biomedical sounds mixed with ambient noise that typically surrounds a medical consultation room to simulate high noisy environments with a SNR from -20 dB to -5 dB. In order to analyse the drop in denoising performance of the evaluated methods when the effect of the propagation of the patient’s body material and the acoustics of the room is considered, results have been obtained with and without taking these effects into account. Experimental results report that: (i) the performance drop suffered by the proposed method is lower compared to MSS and NLMS when considering the effect of the propagation of the patient’s body material and the acoustics of the room active; (ii) unlike what happens with MSS and NLMS, the proposed method shows a stable trend of the average SDR and SIR results regardless of the type of ambient noise and the SNR level evaluated; and (iii) a remarkable advantage of the proposed method is the high robustness of the acoustic quality of the estimated biomedical sounds when the two single-channel inputs suffer from a delay between them.Ítem Combining a recursive approach via non-negative matrix factorization and Gini index sparsity to improve reliable detection of wheezing sounds(Elsevier, 2020-06-01) De La Torre Cruz, Juan; Cañadas Quesada, Francisco Jesús; Carabias Orti, Julio José; Vera Candeas, Pedro; Ruiz Reyes, NicolásAuscultation constitutes a fast, non-invasive and low-cost tool widely used to diagnose respiratory diseases in most of the health centres. However, the acoustic training and expertise acquired by the physician is still crucial to provide a reliable diagnosis of the status of the lung. Each wrong diagnosis increases the risk to the health of patients and the costs associated with the treatment of the disease detected. A wheezing detection system can be useful to the physician to minimize the subjectivity of the interpretation of the breathing sounds, misdiagnoses due to stress and elucidating complex acoustic scenes (such as louder background noises). Highlight that the presence of wheeze sounds is one of the main indicators of respiratory disorders from airway obstructions. This work presents an expert and intelligent system to detect wheeze sounds based on a recursive algorithm that combines orthogonal non-negative matrix factorization (ONMF) and the sparsity descriptor Gini index. The recursive algorithm is composed of four stages. The first stage is based on ONMF modelling to factorize the spectral bases as dissimilar as possible. The second stage clusters the ONMF bases into two categories: wheezing and normal breath. The third stage proposes a novel stopping criterion that controls the loss of wheezing spectral content at the expense of removing normal breath content in the recursive algorithm. Finally, the fourth stage determines the patient’s condition to locate the temporal intervals in which wheeze sounds are active for unhealthy patients. Experimental results report that the proposed method: (i) provides the best detection performance compared to the recent state-of-the-art wheezing detection approaches, achieving the highest robustness in noisy environments; and (ii) reliably distinguishes the patient’s condition (healthy/unhealthy). The strengths of the proposed method are the following: (i) its unsupervised nature since it does not depend on any training stage to learn in advanced the sounds of interest (wheezing). This fact could make this method attractive to be used in clinical settings because wheezing sound databases are often unavailable; and (ii) the modelling of the spectral behaviour by means of a common feature, the sparsity, that represents the typically energy distributions shown by most of the wheeze and normal breath sounds.