The automatic detection of heart failure using speech signals

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Reddy , M K , Helkkula , P , Keerthana , Y M , Kaitue , K , Minkkinen , M , Tolppanen , H , Nieminen , T & Alku , P 2021 , ' The automatic detection of heart failure using speech signals ' , Computer Speech and Language , vol. 69 , 101205 . https://doi.org/10.1016/j.csl.2021.101205

Title: The automatic detection of heart failure using speech signals
Author: Reddy, M. Kiran; Helkkula, Pyry; Keerthana, Y. Madhu; Kaitue, Kasimir; Minkkinen, Mikko; Tolppanen, Heli; Nieminen, Tuomo; Alku, Paavo
Contributor organization: Complex Disease Genetics
Institute for Molecular Medicine Finland
University of Helsinki
HUS Internal Medicine and Rehabilitation
Department of Medicine
Clinicum
Helsinki University Hospital Area
HUS Heart and Lung Center
Date: 2021-09
Language: eng
Number of pages: 11
Belongs to series: Computer Speech and Language
ISSN: 0885-2308
DOI: https://doi.org/10.1016/j.csl.2021.101205
URI: http://hdl.handle.net/10138/331881
Abstract: Heart failure (HF) is a major global health concern and is increasing in prevalence. It affects the larynx and breathing - thereby the quality of speech. In this article, we propose an approach for the automatic detection of people with HF using the speech signal. The proposed method explores mel-frequency cepstral coefficient (MFCC) features, glottal features, and their combination to distinguish HF from healthy speech. The glottal features were extracted from the voice source signal estimated using glottal inverse filtering. Four machine learning algorithms, namely, support vector machine, Extra Tree, AdaBoost, and feed-forward neural network (FFNN), were trained separately for individual features and their combination. It was observed that the MFCC features yielded higher classification accuracies compared to glottal features. Furthermore, the complementary nature of glottal features was investigated by combining these features with the MFCC features. Our results show that the FFNN classifier trained using a reduced set of glottal + MFCC features achieved the best overall performance in both speaker-dependent and speaker-independent scenarios. (C) 2021 The Author(s). Published by Elsevier Ltd.
Subject: Heart failure
coefficients
Glottal source parameters
Support vector machines
Extra tree
AdaBoost
Neural networks
DECOMPOSITION
FRAMEWORK
3112 Neurosciences
3121 General medicine, internal medicine and other clinical medicine
Peer reviewed: Yes
Rights: cc_by_nc_nd
Usage restriction: openAccess
Self-archived version: publishedVersion


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