Ballistocardiogram (BCG) signal is a physiological signal, reflecting heart mechanical status. It can be measured without any electrodes touching subject's body surface and can realize physiological monitoring ubiquitously. However, BCG signal is so weak that it would often be interferred by superimposed noises. For measuring BCG signal effectively, we proposed an approach using joint time-frequency distribution and empirical mode decomposition (EMD) for BCG signal denoising. We set up an adaptive optimal kernel for BCG signal and extracted BCG signals components using it. Then we de-noised the BCG signal by combing empirical mode decomposition with it. Simulation results showed that the proposed method overcome the shortcomings of empirical mode decomposition for the signals with identical frequency content at different times, realized the filtering for BCG signal and also reconstructed the characteristics of BCG.
Algorithms ; Ballistocardiography ; methods ; Humans ; Monitoring, Physiologic

In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.
Algorithms ; Ballistocardiography ; Heart Rate ; Humans ; Signal Processing, Computer-Assisted

Based on the fact that the respiratory component modulates the cardiac cycle component in the ballistocardiogram (BCG) signal, we propose a method that detects respiratory with time-frequency analysis for the sitting ballistocardiography system. Firstly, we demodulated the BCG signal by using the variable frequency complex demodulation (VFCDM) to obtain the output for different center frequency of interest. Then we calculated the instantaneous frequencies and the instantaneous amplitudes by the time-frequency representation. We reconstructed the time-domain waveform of respiratory at last. In order to verify the feasibility and accuracy of this method, we applied wavelet transform and nasal thermistor signal to compare qualitatively and quantitatively. The simulation results showed that the proposed method could detect the respiratory rate from BCG signal more accurately, which provided meaningful attempt for monitoring the multiple physiological parameters synchronously and unconsciously.
Algorithms ; Ballistocardiography ; methods ; Electrocardiography ; Humans ; Monitoring, Physiologic ; methods ; Respiration ; Signal Processing, Computer-Assisted

The requirement for unconstrained monitoring of heartbeat during sleep is increasing, but the current detection devices can not meet the requirements of convenience and accuracy. This study designed an unconstrained ballistocardiogram (BCG) detection system using acceleration sensor and developed a heart rate extraction algorithm. BCG is a directional signal which is stronger and less affected by respiratory movements along spine direction than in other directions. In order to measure the BCG signal along spine direction during sleep, a 3-axis acceleration sensor was fixed on the bed to collect the vibration signals caused by heartbeat. An approximate frequency range was firstly assumed by frequency analysis to the BCG signals and segmental filtering was conducted to the original vibration signals within the frequency range. Secondly, to identify the true BCG waveform, the accurate frequency band was obtained by comparison with the theoretical waveform. The J waves were detected by BCG energy waveform and an adaptive threshold method was proposed to extract heart rates by using the information of both amplitude and period. The accuracy and robustness of the BCG detection system proposed and the algorithm developed in this study were confirmed by comparison with electrocardiogram (ECG). The test results of 30 subjects showed a high average accuracy of 99.21% to demonstrate the feasibility of the unconstrained BCG detection method based on vibration acceleration.
Acceleration ; Ballistocardiography ; Electrocardiography ; Heart Rate ; Humans ; Signal Processing, Computer-Assisted ; Vibration