Segmentation of Radar-Recorded Heart Sound Signals Using Bidirectional LSTM Networks

Unpublished / Preprint
(Accepted)


Publication Details

Author(s): Shi K, Schellenberger S, Weber L, Wiedemann P, Michler F, Steigleder T, Malessa A, Lurz F, Ostgathe C, Weigel R, Kölpin A
Publication year: 2019
Language: English


Abstract

Sounds caused by the action of the heart reflect both its health as well as deficiencies and are examined by physicians since antiquity. Pathologies of the valves, e.g. insufficiencies and stenosis, cardiac effusion, arrhythmia, inflammation of the surrounding tissue and other diagnosis can be reached by experienced physicians. However, practice is needed to assess the findings correctly. Furthermore, stethoscopes do not allow for long-term monitoring of a patient. Recently, radar technology has shown the ability to perform continuous touchless and thereby burden-free heart sound measurements. In order to perform automated classification of the signals, the first and most important step is to segment the heart sounds into their physiological phases. This paper examines the use of different Long Short-Term Memory (LSTM) architectures for this purpose based on a large dataset of radar-recorded heart sounds gathered from 30 different test persons in a clinical study. The best-performing network, a bidirectional LSTM, achieves a sample-wise accuracy of 93.4% and a F1 score for the first heart sound of 95.8%.


FAU Authors / FAU Editors

Lurz, Fabian
Lehrstuhl für Technische Elektronik
Malessa, Anke Dr.
Abteilung für Palliativmedizin
Michler, Fabian
Lehrstuhl für Technische Elektronik
Ostgathe, Christoph Prof. Dr.
Professur für Palliativmedizin
Schellenberger, Sven
Lehrstuhl für Technische Elektronik
Shi, Kilin
Lehrstuhl für Technische Elektronik
Weigel, Robert Prof. Dr.-Ing.
Lehrstuhl für Technische Elektronik
Wiedemann, Philipp
Lehrstuhl für Technische Elektronik


External institutions with authors

Brandenburgische Technische Universität Cottbus-Senftenberg (BTU)
Humboldt-Universität zu Berlin

Last updated on 2019-24-04 at 06:21