Multi-signal bathroom scale to assess long-term trends in cardiovascular parameters.

This paper describes the circuits and signal processing techniques that convert an electronic bathroom scale intended for bioimpedance analysis (BIA) into a compact system to acquire the electrocardiogram (ECG), the ballistocardiogram (BCG), and the impedance plethysmogram (IPG) using only plantar measurements. The signal processing methods proposed rely on the higher quality of the IPG as compared to the ECG and BCG and they enhance the signal-to-noise ratio (SNR) of these two signals, which otherwise could be too poor in non-controlled environments. The system is suitable for long-term periodic monitoring of cardiovascular function.

Heart rate detection from an electronic weighing scale.

We propose a novel technique for beat-to-beat heart rate detection based on the ballistocardiographic (BCG) force signal from a subject standing on a common electronic weighing scale. The detection relies on sensing force variations related to the blood acceleration in the aorta, works even if wearing footwear and does not require any sensors attached to the body because it uses the load cells in the scale. We have devised an approach to estimate the sensitivity and frequency response of three commercial weighing scales to assess their capability to detect the BCG force signal. Static sensitivities ranged from 490 nV V(-1) N(-1) to 1670 nV V(-1) N(-1). The frequency response depended on the subject's mass but it was broad enough for heart rate estimation. We have designed an electronic pulse detection system based on off-the-shelf integrated circuits to sense heart-beat-related force variations of about 0.24 N. The signal-to-noise ratio of the main peaks of the force signal detected was higher than 30 dB. A Bland-Altman plot was used to compare the RR time intervals estimated from the ECG and BCG force signals for 17 volunteers. The error was +/-21 ms, which makes the proposed technique suitable for short-term monitoring of the heart rate.

Adaptive filtering of ballistocardiogram artifact from EEG signals using the dilated discrete Hermite transform.

Electroencephalogram (EEG) signals, when recorded within the strong magnetic field of an MRI scanner are subject to various artifacts, of which the ballistocardiogram (BCG) is one of the prominent ones affecting the quality of the EEG. The BCG artifact varies slightly in shape and amplitude for every cardiac cycle making it difficult to identify and remove. This paper proposes a novel method for the identification and elimination of this artifact using the shape basis functions of the new dilated discrete Hermite transform. In this study, EEG data within and outside the scanner was recorded. On removal of the BCG artifact for the EEG data recorded within the scanner, a significant reduction in amplitude at the frequencies associated with the BCG artifact was observed. In order to quantitatively assess the efficacy of this method, BCG artifact templates were added to segments of EEG signals recorded outside the scanner. These signals, when filtered using the proposed method, had no significant difference (p0.05) from the original signals, indicating that the technique satisfactorily eliminates the BCG artifact and does not introduce any distortions in the original signal. The method is computationally efficient for real-time implementation.

Moving GLM ballistocardiogram artifact reduction for EEG acquired simultaneously with fMRI.

OBJECTIVE: Simultaneous acquisition of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) enables studies of brain activity at both high temporal and high spatial resolution. However, EEG acquired in a magnetic field is contaminated by ballistocardiogram (BKG) artifact. The most commonly used method of BKG artifact reduction, averaged artifact subtraction (AAS), was not designed to account for overlapping BKG waveforms generated by adjacent beats. We describe a new method based on a moving general linear model (mGLM) that accounts for overlapping BKG waveforms. METHODS: Simultaneous EEG-fMRI at 3 Tesla was performed in nine normal human subjects (8-11 runs/subject, 5.52 min/run). Gradient switching artifact was effectively reduced using commercially supplied procedures. Cardiac beats were detected using a novel correlation detector algorithm applied to the EKG trace. BKG artifact was reduced using both mGLM and AAS. RESULTS: mGLM recovered BKG waveforms outlasting the median inter-beat interval. mGLM more effectively than AAS removed variance in the EEG attributable to BKG artifact. CONCLUSIONS: mGLM offers advantages over AAS especially in the presence of variable heart rate. SIGNIFICANCE: The BKG artifact reduction procedure described herein improves the technique of simultaneous EEG-fMRI. Potential applications include basic investigations of the relationship between scalp potentials and functional imaging signals as well as clinical localization of epileptic foci.

ICA-based procedures for removing ballistocardiogram artifacts from EEG data acquired in the MRI scanner.

Electroencephalogram (EEG) data acquired in the MRI scanner contains significant artifacts, one of the most prominent of which is ballistocardiogram (BCG) artifact. BCG artifacts are generated by movement of EEG electrodes inside the magnetic field due to pulsatile changes in blood flow tied to the cardiac cycle. Independent Component Analysis (ICA) is a statistical algorithm that is useful for removing artifacts that are linearly and independently mixed with signals of interest. Here, we demonstrate and validate the usefulness of ICA in removing BCG artifacts from EEG data acquired in the MRI scanner. In accordance with our hypothesis that BCG artifacts are physiologically independent from EEG, it was found that ICA consistently resulted in five to six independent components representing the BCG artifact. Following removal of these components, a significant reduction in spectral power at frequencies associated with the BCG artifact was observed. We also show that our ICA-based procedures perform significantly better than noise-cancellation methods that rely on estimation and subtraction of averaged artifact waveforms from the recorded EEG. Additionally, the proposed ICA-based method has the advantage that it is useful in situations where ECG reference signals are corrupted or not available.