Blind Source Separation on Non-Contact Heartbeat Detection by Non-Negative Matrix Factorization Algorithms

Chen Ye, Kentaro Toyoda, Tomoaki Ohtsuki

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


In non-contact heart rate (HR) monitoring via Doppler radar, the disturbance from respiration and/or body motion is treated as a key problem on the estimation of HR. This paper proposes a blind source separation (BSS) approach to mitigate the noise effect in the received radar signal, and incorporates the sparse spectrum reconstruction to achieve a high-resolution of heartbeat spectrum. The proposed BSS decomposes the spectrogram of mixture signal into original sources, including heartbeat, using non-negative matrix factorization (NMF) algorithms, through learning the complete basis spectra (BS) by a hierarchical clustering. In particular, to exploit the temporal sparsity of heartbeat component, two variants of NMF algorithms with sparseness constraints are applied as well, namely sparse NMF and weighted sparse NMF. Compared with usual BSS, our proposed BSS has three advantages: 1) clustering-induced unsupervised manner; 2) compact demixing architecture; and 3) merely requiring single-channel input data. In addition, the HR estimation method using our proposal delivers more satisfactory precision and robustness over other existing methods, which is demonstrated through the measurements of distinguishing people's activities, gaining both smallest absolute errors of HR estimation for sitting still and typewriting.

Original languageEnglish
Article number8710248
Pages (from-to)482-494
Number of pages13
JournalIEEE Transactions on Biomedical Engineering
Issue number2
Publication statusPublished - 2020 Feb


  • Doppler radar
  • blind source separation (BSS)
  • heart rate (HR)
  • non-contact monitoring
  • non-negative matrix factorization (NMF)
  • sparseness constraint

ASJC Scopus subject areas

  • Biomedical Engineering


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