Continuous and unconstrained vital signs monitoring with ballistocardiogram sensors in headrest position

Abstract

Unobtrusive and long-term monitoring of human vital signs are essential requirements for early diagnosis and prophylaxis due to many reasons, one of the most important being improving the quality of life. Currently, vital signs are continuously monitored through sensors attached to the body, such as multiple electrodes for measuring electrical activity of the heart. Such methods may be undesirable, especially for elderly, infants and other groups of people. In this paper, we introduce an improved technique for measuring heart rate from noisy ballistocardiogram signals acquired from 50 human volunteers in a sitting position using a massage chair. The signals are unobtrusively collected from a microbend fiber optic sensor embedded within the headrest of the chair, and then transmitted to a computer through a Bluetooth connection. The heart rate is computed using the multiresolution analysis of the maximal overlap discrete wavelet transform. The error between the proposed method and the reference ECG is estimated in beats per minute using the mean absolute error, where the system achieved relatively good results (7.31 ± 1.60) despite the large amount of motion artifacts produced owing to the frequent body movements and/or vibrations of the massage chair during stress relief massage. Unlike the complete ensemble empirical mode decomposition algorithm, previously employed for heart rate estimation, the suggested system is much faster. Hence, it can be used in real-time applications.

Comparative Study of Heart Rate Extraction Methods for a Novel Intelligent Mattress

“…For MODWT, the error (6.87 ± 1.94 BPM) is one of the smallest. These results confirm the findings of Sadek et al [9], showing that MODWT has good efficiency in extracting clear heartbeat peaks from the BCG. A simple peak searching algorithm applied to the output of the wavelet transform is enough to obtain satisfying results.…”

“…Jin et al [10] detected the heart rate with a peak searching algorithm using Donoho and Johnstone's [11] wavelet shrinking method with a Symlet-8 wavelet basis. Sadek et al [9] implemented the MODWT to extract the BCG signal from a microbend FOS.…”

“…In this work, we experiment the performances of this new mattress by applying four types of algorithms to retrieve the HR from the motions sensed on the FOS mattress, namely complete ensemble empirical mode decomposition with adaptative noise (CEEMDAN) [7], clustering [8], cepstrum [3], and the Maximal overlap direct wavelet transform (MODWT). The MODWT is the only one already applied on a microbend FOS mattress in a previous work [9]. Our aim is to recommend the methods that are more adapted to offline or online signal processing applications in the case of heart rate extraction.…”

“…We exploited MATLAB to implement MODWT. The approach we use is similar to that of Sadek et al [9]. The 4th level smooth coefficients of the wavelet decomposition are used to extract the BCG.…”

“…The CEEMDAN is an optimized method frequently used for EMD. The CEEMDAN, in particular, shows excellent results in extracting the HR from still subjects [9]. We use the CEEMDAN algorithm provided by Torres et al [21] and we set the parameters according to a noise standard deviation of 0.2, a number of realizations of 100 and a maximum number of iterations of 30 [7].…”