Deep Clustering with LSTM for Vital Signs Separation in Contact-free Heart Rate Estimation

Chen Ye, Guan Gui, Tomoaki Ohtsuki

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

So far, most separation approaches of vital signs such as heartbeat and respiration, are implemented based on linear mixtures. However, some literatures have reported that non-linear mixtures actually occur in the associated applications, e.g., heart rate (HR) estimation with Doppler radar, where the simple linear demixing architecture may limit the effect of source separation. In addition, the human motions during HR measurement further complicate the mixing processes. The issue motivates us to exploit a more suitable separation approach to deal with contact-free HR estimation, considering non-linear mixtures including motions. A semi-supervised deep clustering (DC) is proposed to separate the three mixed sources of heartbeat, respiration, and motions, by segmenting the spectrogram of Doppler signal. First, through training a deep recurrent neural network (RNN) with long short-term memory (LSTM) via heartbeat/respiration-only data, the embeddings to each frame-sample from spectrogram can be acquired, which enables feature optimization in a lower dimensional space. Then, in the test phase, K-means clusters the embeddings associated with each source, to infer the masks used for spectrogram segmentation. The proposed deep clustering has three main strengths: It (i) gets rid of the restriction of mixture class, relying on data mining; (ii) can handle three-source mixtures by training two sorts of source-independent samples; (iii) only requires the mixtures from single-channel. The HR measurement experiments on subjects' sitting still and typing, validate the improvements of accuracy and robustness by our proposal, over some prevailing approaches in signal decomposition or separation.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728150895
DOI
出版ステータスPublished - 2020 6月
イベント2020 IEEE International Conference on Communications, ICC 2020 - Dublin, Ireland
継続期間: 2020 6月 72020 6月 11

出版物シリーズ

名前IEEE International Conference on Communications
2020-June
ISSN(印刷版)1550-3607

Conference

Conference2020 IEEE International Conference on Communications, ICC 2020
国/地域Ireland
CityDublin
Period20/6/720/6/11

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 電子工学および電気工学

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