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

Start Page
6677
End Page
6680
Article Number
8857863
Citation
Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS: 8857863 (2019-07)
Contribution to Conference
Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019)  
Publisher DOI
10.1109/EMBC.2019.8857863
Scopus ID
2-s2.0-85077904835
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 %.