The Automatic Detection of Heart Failure Using Speech Signals

Kiran Mittapalle*, Pyry Helkkula, Y. Madhu Keerthana, Kasimir Kaitue, Mikko Minkkinen, Heli Tolppanen, Tuomo Nieminen, Paavo Alku

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

19 Citations (Scopus)
152 Downloads (Pure)


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.
Original languageEnglish
Article number101205
Number of pages11
JournalComputer Speech and Language
Publication statusPublished - Sept 2021
MoE publication typeA1 Journal article-refereed


  • heart failure
  • mel-frequency cepstral coefficients
  • glottal source parameters
  • support vector machines
  • Extra Tree
  • AdaBoost
  • neural networks


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