Time and frequency-based approach to heart sound segmentation and classification

Jarno Makela*, Heikki Vaananen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


In this study, we propose a decision tree classifier of heart sound signals. We determined repetitive fundamental heart sound segments based on adaptive similarity value clusterization of the sound signal, and we created a set of filters for decision tree parametrization. Using the filters together with inter-segment timings, we created three sets of markers: a set utilizing both S1 and S2 identification, a set where only one segment was identified, and a set without any identified segment. An individual classification tree was trained for each marker set. As a result, our classifier attained sensitivity (Se) of 0.66 and specificity (Sp) of 0.92 and overall score of 0.79 for a hidden random (revised) subset.

Original languageEnglish
Title of host publicationComputing in Cardiology Conference, CinC 2016
Number of pages4
ISBN (Electronic)9781509008964
Publication statusPublished - 1 Mar 2016
MoE publication typeA4 Article in a conference publication
EventComputing in Cardiology Conference - Vancouver, Canada
Duration: 11 Sep 201614 Sep 2016
Conference number: 43


ConferenceComputing in Cardiology Conference
Abbreviated titleCinC

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