A non-contact method based on multiple signal classification algorithm to reduce the measurement time for accurately heart rate detection

Abstract

Non-contact methods for the assessment of vital signs are of great interest for specialists due to the benefits obtained in both medical and special applications, such as those for surveillance, monitoring, and search and rescue. This paper investigates the possibility of implementing a digital processing algorithm based on the MUSIC (Multiple Signal Classification) parametric spectral estimation in order to reduce the observation time needed to accurately measure the heart rate. It demonstrates that, by proper dimensioning the signal subspace, the MUSIC algorithm can be optimized in order to accurately assess the heart rate during an 8-28 s time interval. The validation of the processing algorithm performance was achieved by minimizing the mean error of the heart rate after performing simultaneous comparative measurements on several subjects. In order to calculate the error the reference value of heart rate was measured using a classic measurement system through direct contact.

Super-resolution spectral estimation in short-time non-contact vital sign measurement

“…7 shows that with sufficient data length both algorithms achieve low RMSE, and that of RMFOCUSS is slightly smaller. This is due to the compromise on robust results, 16 where the parameter K in MUSIC has to be set much larger, e.g., 64 than actual number of physiological frequencies, e.g., 5, which sacrifices the estimation accuracy. When the data length is limited, the performance of both algorithms degrades.…”

“…For robust results, the parameter by which signal and noise subspace are divided is set to P = 64 for MUSIC according to Ref. 16, and the parameters of RMFOCUSS are set to q = 0.8, γ = 0.01, and λ = 5. Fig.…”

“…In 2013, the multiple signal classification (MUSIC) 15 parametric spectral estimation algorithm was applied into this field. 16 By properly dimensioning the signal subspace and noise subspace of the measured data, the MUSIC algorithm could be used to estimate the heartbeat during an 8-28 s time interval accurately. However, in practice, the parameter K which indicates the dimension of signal subspace should be chosen to be much larger than the actual number of physiological frequencies to achieve robust results, 16 which sacrifices the estimation accuracy.…”

“…16 By properly dimensioning the signal subspace and noise subspace of the measured data, the MUSIC algorithm could be used to estimate the heartbeat during an 8-28 s time interval accurately. However, in practice, the parameter K which indicates the dimension of signal subspace should be chosen to be much larger than the actual number of physiological frequencies to achieve robust results, 16 which sacrifices the estimation accuracy. Furthermore, the processing window length required by these algorithms is too long to demonstrate the time-varying characteristics of vital signals, which have great significance in intensive care, postoperative recovery of patients, and severe trauma, etc.…”

Radar Signal Processing for Sensing in Assisted Living: The Challenges Associated With Real-Time Implementation of Emerging Algorithms

“…Existing techniques for noncontact health parameters estimation are mainly based on FT analysis and spectral estimation algorithm (e.g. MUSIC, RELAX) [17][18][19]. However, due to smearing/leakage problems caused by the limited data length, FT typically is "plagued" with limited resolution.…”