Cancellation of respiratory artifact in impedance cardiography

A method for suppressing respiratory noise in impedance cardiography and comprehensive assessment of noise reduction performance

Cardiac Output (CO) is a significant hemodynamic index for the diagnosis and treatment of cardiovascular diseases. Impedance cardiography (ICG) is one of the non-invasive methods widely investigated for its simplicity, cost-effectiveness and ability to measure cardiac output continuously. However, measured ICG signals are contaminated by the respiratory artefact leading to difficulties in determining characteristic points in the signal waveform, thereby lowering the accuracy of measurement results. Thus, suppressing this artefact plays an important role in ICG signal processing. This paper aims to propose a method of noise filtering to improve the quality of the signal as well as a model to evaluate the noise filtering efficiency of the method. The proposed algorithm showed promising results with the output SNR values of 21.99 ± 3.20 dB, 20.40 ± 2.88 dB, 15.57 ± 4.79 dB for normal breathing, forced breathing, and rapid breathing respectively. The root mean square percentage error (RMSPE) values of the output signals processed by the proposed algorithm compared to the standard ICG signal source for normal breathing, forced breathing, and rapid breathing are 24.13 ± 22.66%, 18.09 ± 12.98%, 32.13 ± 20.40% respectively. The quality-enhanced ICG signal could be effective tool for assisting doctors to detect cardiac abnormalities via evaluating the morphology of ICG signal waveforms as well as improving accuracy in calculating beat-to-beat and averaged hemodynamic parameters.

Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions

Currently, nearly 6 in 10 US adults are suffering from at least one chronic condition. Wearable technology could help in controlling the health care costs by remote monitoring and early detection of disease worsening. However, in recent years, there have been disappointments in wearable technology with respect to reliability, lack of feedback, or lack of user comfort. One of the promising sensor techniques for wearable monitoring of chronic disease is bioimpedance, which is a noninvasive, versatile sensing method that can be applied in different ways to extract a wide range of health care parameters. Due to the changes in impedance caused by either breathing or blood flow, time-varying signals such as respiration and cardiac output can be obtained with bioimpedance. A second application area is related to body composition and fluid status (eg, pulmonary congestion monitoring in patients with heart failure). Finally, bioimpedance can be used for continuous and real-time imaging (eg, during mechanical ventilation). In this viewpoint, we evaluate the use of wearable bioimpedance monitoring for application in chronic conditions, focusing on the current status, recent improvements, and challenges that still need to be tackled.

Cardiac sympathetic activity during recovery as an indicator of sympathetic activity during task performance

The goals of this research were to analyze cardiac sympathetic recovery patterns and evaluate whether sympathetic cardiac responses to a task challenge can be predicted using residual cardiac activity measured directly after the task (that is, during the recovery period). In two studies (total N = 181), we measured cardiac sympathetic activity, quantified as pre-ejection period and RB interval, during both task performance and the 2-min recovery period following the task. Additional analyses examined effects on the RZ interval. We found that sympathetic recovery from a task was rather quick: Cardiovascular recovery occurred within the first 30 s of the recovery period. Nevertheless, residual cardiac activity during the recovery period had predictive power for task-related cardiac activity. This suggests that sympathetic cardiac activity during recovery may serve as a useful indicator of task-related cardiac sympathetic activity. We discuss the implications of these findings for practical applications and the design of future studies.

Process techniques for human thoracic electrical bio-impedance signal in remote healthcare systems

Analysis of thoracic electrical bio-impedance (TEB) facilitates heart stroke volume in sudden cardiac arrest. This Letter proposes several efficient and computationally simplified adaptive algorithms to display high-resolution TEB component. In a clinical environment, TEB signal encounters with various physiological and non-physiological phenomenon, which masks the tiny features that are important in identifying the intensity of the stroke. Moreover, computational complexity is an important parameter in a modern wearable healthcare monitoring tool. Hence, in this Letter, the authors propose a new signal conditioning technique for TEB enhancement in remote healthcare systems. For this, the authors have chosen higher order adaptive filter as a basic element in the process of TEB. To improve filtering capability, convergence speed, to reduce computational complexity of the signal conditioning technique, the authors apply data normalisation and clipping the data regressor. The proposed implementations are tested on real TEB signals. Finally, simulation results confirm that proposed regressor clipped normalised higher order filter is suitable for a practical healthcare system.

Cardiorespiratory interactions: Noncontact assessment using laser Doppler vibrometry

The application of a noncontact physiological recording technique, based on the method of laser Doppler vibrometry (LDV), is described. The effectiveness of the LDV method as a physiological recording modality lies in the ability to detect very small movements of the skin, associated with internal mechanophysiological activities. The method is validated for a range of cardiovascular variables, extracted from the contour of the carotid pulse waveform as a function of phase of the respiration cycle. Data were obtained from 32 young healthy participants, while resting and breathing spontaneously. Individual beats were assigned to four segments, corresponding with inspiration and expiration peaks and transitional periods. Measures relating to cardiac and vascular dynamics are shown to agree with the pattern of effects seen in the substantial body of literature based on human and animal experiments, and with selected signals recorded simultaneously with conventional sensors. These effects include changes in heart rate, systolic time intervals, and stroke volume. There was also some evidence for vascular adjustments over the respiration cycle. The effectiveness of custom algorithmic approaches for extracting the key signal features was confirmed. The advantages of the LDV method are discussed in terms of the metrological properties and utility in psychophysiological research. Although used here within a suite of conventional sensors and electrodes, the LDV method can be used on a stand-alone, noncontact basis, with no requirement for skin preparation, and can be used in harsh environments including the MR scanner.

Portable bioimpedance monitor evaluation for continuous impedance measurements. Towards wearable plethysmography applications

Personalised Health Systems (PHS) that could benefit the life quality of the patients as well as decreasing the health care costs for society among other factors are arisen. The purpose of this paper is to study the capabilities of the System-on-Chip Impedance Network Analyser AD5933 performing high speed single frequency continuous bioimpedance measurements. From a theoretical analysis, the minimum continuous impedance estimation time was determined, and the AD5933 with a custom 4-Electrode Analog Front-End (AFE) was used to experimentally determine the maximum continuous impedance estimation frequency as well as the system impedance estimation error when measuring a 2R1C electrical circuit model. Transthoracic Electrical Bioimpedance (TEB) measurements in a healthy subject were obtained using 3M gel electrodes in a tetrapolar lateral spot electrode configuration. The obtained TEB raw signal was filtered in MATLAB to obtain the respiration and cardiogenic signals, and from the cardiogenic signal the impedance derivative signal (dZ/dt) was also calculated. The results have shown that the maximum continuous impedance estimation rate was approximately 550 measurements per second with a magnitude estimation error below 1% on 2R1C-parallel bridge measurements. The displayed respiration and cardiac signals exhibited good performance, and they could be used to obtain valuable information in some plethysmography monitoring applications. The obtained results suggest that the AD5933-based monitor could be used for the implementation of a portable and wearable Bioimpedance plethysmograph that could be used in applications such as Impedance Cardiography. These results combined with the research done in functional garments and textile electrodes might enable the implementation of PHS applications in a relatively short time from now.

Reconstruction of normal and abnormal gastric electrical sources using a potential based inverse method

The use of cutaneous recordings to non-invasively characterize gastric slow waves has had limited clinical acceptance, primarily due to the uncertainty in relating the recorded signal to the underlying gastric slow waves. In this study we aim to distinguish and quantitatively reconstruct different slow wave patterns using an inverse algorithm. Slow wave patterns corresponding to normal, retrograde and uncoupled activity at different frequencies were imposed on a stomach surface model. Gaussian noise (10% peak-to-peak) was added to cutaneous potentials and the Greensite-Tikhonov inverse method was used to reconstruct the potentials on the stomach. The effectiveness of the number or location of electrodes on the accuracy of the inverse solutions was investigated using four different electrode configurations. Results showed the reconstructed solutions were able to reliably distinguish the different slow wave patterns and waves with lower frequency were better correlated to the known solution than those with higher. The use of up to 228 electrodes improved the accuracy of the inverse solutions. However, the use of 120 electrodes concentrated around the stomach was able to achieve similar results. The most efficient electrode configuration for our model involved 120 electrodes with an inter-electrode distance of 32 mm.

Adaptive filtering for suppression of respiratory artifact in impedance cardiography

Impedance cardiography is a noninvasive technique, based on sensing the variation in the electrical impedance of the thorax caused by variation in the blood volume during the cardiac cycle, for monitoring the stroke volume and some other cardiovascular indices. Respiratory and motion artifacts cause baseline drift in the sensed impedance waveform, particularly during or after exercise. This paper presents an LMS-based adaptive filtering technique for suppression of respiratory artifact for improving the estimation of the indices without smearing the beat-to-beat variations. It uses a reference signal, closely related to the respiratory artifact, obtained by a least-squares approximation based B-spline fitting on the contaminated impedance cardiogram in synchronism with the respiratory phases. The technique is evaluated on signals with simulated artifacts and on signals from nine healthy subjects and five patients with cardiovascular disorders. The values of the stroke volume, estimated on beat-to-beat basis, after suppression of respiratory artifact showed a good agreement with those from Doppler echocardiography.

Automatic wavelet detrending benefits to the analysis of cardiac signals acquired in a moving wheelchair

Biomedical signals are customarily overlaid with interferences and noise, furthermore, baseline wandering is another significant drawback to their accurate interpretation, especially if the implementation platform is a wheelchair. The nonlinear processes which generate the physiologic signals, and the disturbances, regularly exclude, or limit, the usage of classical linear techniques, hence, among other options, wavelets have been used to decompose the signals. Unobtrusively acquired signals are prone to have important baseline fluctuations, namely contactless impedance plethysmogram, and ballistocardiogram, therefore making them apposite to detrending. Sensing hardware was embedded in a wheelchair to acquire these signals, given the valuable information provided about the cardiovascular system of the monitored subject. This work also reports the improvements achieved by automatic wavelet detrending application in the real-time processing of these signals. Although significant baseline wavering is acquired, important enhancements are swiftly computed without noteworthy error.