Biomedical Recordings Research Articles

Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals

Abstract Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-graining step uses variance (RCmvMFE σ 2 ) or mean (RCmvMFE μ ). We investigate the behavior of these multivariate methods on multichannel white Gaussian and 1/ f noise signals, and two publicly available biomedical recordings. Our simulations demonstrate that RCmvMFE σ 2 and RCmvMFE μ lead to more stable results and are less sensitive to the signals’ length in comparison with the other existing multivariate multiscale entropy-based methods. The classification results also show that using both the variance and mean in the coarse-graining step offers complexity profiles with complementary information for biomedical signal analysis. We also made freely available all the Matlab codes used in this paper.

ParkinsoNET: Estimation of UPDRS Score Using Hubness-Aware Feedforward Neural Networks

ABSTRACTParkinson’s disease is a worldwide, frequent, neurodegenerative disorder with increasing incidence. Speech disturbance appears during the progression of the disease. The Unified Parkinson’s Disease Rating Scale (UPDRS) is a gold-standard tool for diagnosis and follow-up of the disease. We aim at estimating the UPDRS score based on biomedical voice recordings. In this article, we study the hubness phenomenon in context of the UPDRS score estimation and propose hubness-aware error correction for feedforward neural networks to increase the accuracy of estimation. We perform experiments on publicly available datasets derived from real-voice data and show that the proposed technique systematically increases the accuracy of various feedforward neural networks.

Multiscale Two-Directional Two-Dimensional Principal Component Analysis and Its Application to High-Dimensional Biomedical Signal Classification

Goal: Time–frequency analysis incorporating the wavelet transform followed by the principal component analysis (WT-PCA) has been a powerful approach for the analysis of biomedical signals, such as electromyography (EMG), electroencephalography, electrocardiography, and Doppler ultrasound. Time–frequency coefficients at various scales were usually transformed into a 1-D array using only a single or a few signal channels. The steady improvement of biomedical recording techniques has increasingly permitted the registration of a high number of channels. However, WT-PCA is not applicable to high-dimensional recordings due to the curse of dimensionality and small sample size problem. In this study, we present a multiscale two-directional 2-D principal component analysis method for the efficient and effective extraction of essential feature information from high-dimensional signals. Multiscale matrices constructed in the first step incorporate the spatial correlation and physiological characteristics of subband signals among channels. In the second step, the two-directional 2-D PCA operates on the multiscale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify 20 hand movements using 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for a high-dimensional biomedical signal analysis.

An Adaptive Radial Basis Function Neural Network Filter for Noise Reduction in Biomedical Recordings

Electroencephalogram (EEG) recordings often experience interference by different kinds of noise, including white, muscle and baseline, severely limiting its utility. Artificial neural networks (ANNs) are effective and powerful tools for removing interference from EEGs. Several methods have been developed, but ANNs appear to be the most effective for reducing muscle and baseline contamination, especially when the contamination is greater in amplitude than the brain signal. An ANN as a filter for EEG recordings is proposed in this paper, developing a novel framework for investigating and comparing the relative performance of an ANN incorporating real EEG recordings. This method is based on a higher-order statistics-based radial basis function (RBF) network. This ANN improves the results obtained with the conventional EEG filtering techniques: wavelet, singular value decomposition, principal component analysis, adaptive filtering and independent components analysis. Average results for the RBF-based method provided a noise reduction (SIR) of (mean$$\pm $$± SD) $$\mathrm{SIR}=19.3\pm 0.3$$SIR=19.3±0.3 in contrast to traditional compared methods that, for the best case, yielded $$\mathrm{SIR}=15.2\pm 0.3$$SIR=15.2±0.3. The system has been evaluated within a wide range of EEG signals. The present study introduces a new method of reducing all EEG interference signals in one step with low EEG distortion and high noise reduction.

Modified Variational Mode Decomposition for Power Line Interference Removal in ECG Signals

Power line interferences (PLI) occurring at 50/60 Hz can corrupt the biomedical recordings like ECG signals and which leads to an improper diagnosis of disease conditions. Proper interference cancellation techniques are therefore required for the removal of these power line disturbances from biomedical recordings. The non-linear time varying characteristics of biomedical signals make the<strong> </strong>interference removal a difficult task without compromising the actual signal characteristics. In this paper, a modified variational mode decomposition based approach is proposed for PLI removal from the ECG signals. In this approach, the central frequency of an intrinsic mode function is fixed corresponding to the normalized power line disturbance frequency. The experimental results show that the PLI interference is exactly captured both in magnitude and phase and are removed. The proposed approach is experimented with ECG signal records from MIT-BIH Arrhythmia database and compared with traditional notch filtering.

Modified Variational Mode Decomposition for Power Line Interference Removal in ECG Signals

Power line interferences (PLI) occurring at 50/60 Hz can corrupt the biomedical recordings like ECG signals and which leads to an improper diagnosis of disease conditions. Proper interference cancellation techniques are therefore required for the removal of these power line disturbances from biomedical recordings. The non-linear time varying characteristics of biomedical signals make the<strong> </strong>interference removal a difficult task without compromising the actual signal characteristics. In this paper, a modified variational mode decomposition based approach is proposed for PLI removal from the ECG signals. In this approach, the central frequency of an intrinsic mode function is fixed corresponding to the normalized power line disturbance frequency. The experimental results show that the PLI interference is exactly captured both in magnitude and phase and are removed. The proposed approach is experimented with ECG signal records from MIT-BIH Arrhythmia database and compared with traditional notch filtering.

Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.

The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.

Comparison of Some EMD based Technique for Baseline Wander Correction in Fetal ECG Signa

wander (BW) is a low frequency artifact in biomedical electronic recordings. It is usually caused by patient's respiration or movement of equipments. The removal of this artifact is important in ECG recordings for reliable visual interpretation. This paper presents the implementation of Empirical Mode Decomposition (EMD), Ensemble Empirical Mode Decomposition (EEMD) and EMD based method to remove this disturbance. The EMD based technique serves as an efficient method to remove baseline wander with minimum signal distortion. The results highlights the main differences among all different methods and also show that the EMD based technique is able to remove best baseline wander. ( 01 01 ) ( t m t x p  

Ordinal symbolic analysis and its application to biomedical recordings.

Ordinal symbolic analysis opens an interesting and powerful perspective on time-series analysis. Here, we review this relatively new approach and highlight its relation to symbolic dynamics and representations. Our exposition reaches from the general ideas up to recent developments, with special emphasis on its applications to biomedical recordings. The latter will be illustrated with epilepsy data.

Fabrication of Nanostructured Platinum with Multilevel Porosity for Low Impedance Biomedical Recording and Stimulation Electrodes

We report a new lithography-free technology to fabricate porous metal electrodes with multiple levels of porosity, from macroporous – about 1μm pore diameter – down to nanoporous – 3nm diameter. The hierarchical combination of mesopores and nanopores allows deeper layers of the electrode to be better accessible for the liquid the electrode is immersed in, aiming to further reduce the impedance of microscale electrodes for biomedical applications. Up to 1000x reduction in impedance was measured in the first experiments, combined with a 230x increase in charge injection capacity. Next to presenting the new and widely applicable (beyond Pt) fabrication process, this is also the first time hierarchical porosity is used in biomedical electrode fabrication.

Designing and Implementation of Algorithms on MATLAB for Adaptive Noise Cancellation from ECG Signal

The medical monitoring devices are more sensitive for the biomedical signal recording and need more accurate results for every diagnosis. The low frequency signal is destroyed by power line interference of 50 Hz noise, this noise is also source of interference for biomedical signal recording. The frequency of power line interference 50 Hz is nearly equal to the frequency of ECG, so this 50 Hz noise can destroyed the output of ECG signal. One way to remove the noise is to filter the signal with a notch filter at 50 Hz. However, due to slight variations in the power supply to the hospital, the exact frequency of the power supply might (hypothetically) wander between 47 Hz and 53 Hz. A static filter would need to remove all the frequencies between 47 and 53 Hz, which could excessively degrade the quality of the ECG since the heart beat would also likely have frequency components in the rejected range. To circumvent this potential loss of information, an adaptive filter has been used. The adaptive filter would take input both from the patient and from the power supply directly and would thus be able to track the actual frequency of the noise as it fluctuates [2].

Photographing human subjects in biomedical disciplines: an Islamic perspective

Visual recording of human subjects is commonly used in biomedical disciplines for clinical, research, legal, academic and even personal purposes. Guidelines on practice standards of biomedical recording have been issued...

Complexity and multifractality of neuronal noise in mouse and human hippocampal epileptiform dynamics

Fractal methods offer an invaluable means of investigating turbulent nonlinearity in non-stationary biomedical recordings from the brain. Here, we investigate properties of complexity (i.e. the correlation dimension, maximum Lyapunov exponent, 1/fγ noise and approximate entropy) and multifractality in background neuronal noise-like activity underlying epileptiform transitions recorded at the intracellular and local network scales from two in vitro models: the whole-intact mouse hippocampus and lesional human hippocampal slices. Our results show evidence for reduced dynamical complexity and multifractal signal features following transition to the ictal epileptiform state. These findings suggest that pathological breakdown in multifractal complexity coincides with loss of signal variability or heterogeneity, consistent with an unhealthy ictal state that is far from the equilibrium of turbulent yet healthy fractal dynamics in the brain. Thus, it appears that background noise-like activity successfully captures complex and multifractal signal features that may, at least in part, be used to classify and identify brain state transitions in the healthy and epileptic brain, offering potential promise for therapeutic neuromodulatory strategies for afflicted patients suffering from epilepsy and other related neurological disorders.

Extracting Electric Power From Human Body For Supplying Neural Recording System

Abstract A powerful approach to the characterization of cellular electrical activity is electrical recording from cells or living tissues. The human central and / or peripheral nervous system has been a subject of study and fascination of the neuroscience and biomedical engineering communities for many decades. In this paper, we propose a new approach to feed implantable neural recording system, which based on extracting electrical power from human tissue warmth in order to supply a biomedical neural recording system. The major issue to overcome, in the design of a system that is aimed at being implant into the human body, is having a low power consumption, low noise circuit and small dimension to minimize tissue damage.

Design of low-offset low-power CMOS amplifier for biosensor application

A compacted and low-offset low-power CMOS am- plifier for biosensor application is presented in this paper. It includes a low offset Op-Amp and a high precision current reference. With a novel continuous-time DC offset rejection scheme, the IC achieves lower offset voltage and lower power consumption compared to previous designs. This configuration rejects large DC offset and drift that exist at the skin-electrode interface without the need of external components. The proposed amplifier has been implemented in SMIC 0.18-μm 1P6M CMOS technol-ogy, with an active silicon area of 100 μm by 120 μm. The back-annotated simulation results demonstrated the circuit features the systematic offset voltage less than 80 μV, the offset drift about 0.27 μV/℃ for temperature ranging from –30℃ to 100℃ and the total power dissipation consumed as low as 37.8 μW from a 1.8 V single supply. It dedicated to monitor low amplitude biomedical signals recording.

A Belt-type Biomedical Mobile Device

Objective: To investigate a belt-type, biomedical mobile device capable of measuring patients’ biomedical signals and sending the biomedical data to a remote medical server. This device was designed to measure and record ECG and motion signals continuously for a moving subject and, on in the event of an emergent situation, to notify a remote doctor of the situation by transmitting data on the emergent situation to a remote server through a CDMA network. Methods: The developed system is composed of three parts: biomedical signal acquisition, biomedical data recording, and data transmission. We conducted four types of experiment in order to evaluate the developed system’s accuracy, reliability, operability, applicability to daily life, and SMS alarm function. First, we tested the accuracy of the R-R interval by comparing the signals measured via the developed system with those via a commercialized ECG system while the subjects were sitting, standing, lying or cycling. Second, we tested the reliability of the transmitted data to the remote server when two types of emergent events are generated in the developed system using a patient simulator, and measured the battery life to determine the system life. Third, we experimentally examined the accuracy of the corresponding data transmitted to the remote server via the CDMA network when two types of event are generated for each of seven types of action (sitting, standing, standing up from the seat, ordinary walking, fast walking, cycling, and running) during daily life. Lastly, we tested the SMS alarm function. Results: The acquisition and comparison of the subjects’ biomedical signals and motion signals confirmed the accuracy, reliability, operability and applicability of the developed system to daily life. The ability of the system to monitor the ECG signals and motion signals during daily life was also demonstrated. Conclusion: The system was demonstrated to be very applicable to subjects requiring continuous monitoring for chronic disease and health management. Therefore, the developed system is expected to play an important role in building ubiquitous healthcare systems in Korea in the near future. (Journal of Korean Society of Medical Informatics 15-3, 351-358, 2009)

Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals.

Statistical Modeling and Analysis of Laser-Evoked Potentials of Electrocorticogram Recordings from Awake Humans

This article is devoted to statistical modeling and analysis of electrocorticogram (ECoG) signals induced by painful cutaneous laser stimuli, which were recorded from implanted electrodes in awake humans. Specifically, with statistical tools of factor analysis and independent component analysis, the pain-induced laser-evoked potentials (LEPs) were extracted and investigated under different controlled conditions. With the help of wavelet analysis, quantitative and qualitative analyses were conducted regarding the LEPs' attributes of power, amplitude, and latency, in both averaging and single-trial experiments. Statistical hypothesis tests were also applied in various experimental setups. Experimental results reported herein also confirm previous findings in the neurophysiology literature. In addition, single-trial analysis has also revealed many new observations that might be interesting to the neuroscientists or clinical neurophysiologists. These promising results show convincing validation that advanced signal processing and statistical analysis may open new avenues for future studies of such ECoG or other relevant biomedical recordings.

An Improved Adaptive Power Line Interference Canceller for Electrocardiography

Power line interference may severely corrupt a biomedical recording. Notch filters and adaptive cancellers have been suggested to suppress this interference. We propose an improved adaptive canceller for the reduction of the fundamental power line interference component and harmonics in electrocardiogram (ECG) recordings. The method tracks. the amplitude, phase, and frequency of all the interference components for power line frequency deviations up to about 4 Hz. A comparison is made between the performance of our method, former adaptive cancellers, and a narrow and a wide notch filter in suppressing the fundamental power line interference component. For this purpose a real ECG signal is corrupted by an artificial power line interference signal. The cleaned signal after applying all methods is compared with the original ECG signal. Our improved adaptive canceller shows a signal-to-power-line-interference ratio for the fundamental component up to 30 dB higher than that produced by the other methods. Moreover, our method is also effective for the suppression of the harmonics of the power line interference.

A Fully Integrated Neural Recording Amplifier With DC Input Stabilization

This paper presents a low-power low-noise fully integrated bandpass operational amplifier for a variety of biomedical neural recording applications. A standard two-stage CMOS amplifier in a closed-loop resistive feedback configuration provides a stable ac gain of 39.3 dB at 1 kHz. A subthreshold PMOS input transistor is utilized to clamp the large and random dc open circuit potentials that normally exist at the electrode-electrolyte interface. The low cutoff frequency of the amplifier is programmable up to 50 Hz, while its high cutoff frequency is measured to be 9.1 kHz. The tolerable dc input range is measured to be at least +/- 0.25 V with a dc rejection factor of at least 29 dB. The amplifier occupies 0.107 mm2 in die area, and dissipates 115 microW from a 3 V power supply. The total measured input-referred noise voltage in the frequency range of 0.1-10 kHz is 7.8 microVrms. It is fabricated using AMI 1.5 microm double-poly double-metal n-well CMOS process. This paper presents full characterization of the dc, ac, and noise performance of this amplifier through in vitro measurements in saline using two different neural recording electrodes.