ECG) And Electroencephalography Research Articles

A 107.6 dB-DR Three-Step Incremental ADC for Motion-Tolerate Biopotential Signals Recording.

This article describes a power-efficient, high dynamic range (DR) incremental ADC (IADC) for wearable biopotential signals recording, where DC and low-frequency disturbances such as electrode offset, 50/60 Hz interference and motion artifact must be tolerated. To achieve a wide DR, the IADC performs a three-step conversion by combining zoom-SAR and extended counting (EC) on top of a second-order incremental delta-sigma modulator (ΔΣM). The hybrid architecture notably reduces the oversampling ratio (OSR) with respect to conventional incremental ΔΣMs, while using the EC further improves the Signal-to-Noise-and-Distortion Ratio (SNDR) by 7.4 to 25.6 dB. Fabricated in a 0.18-μm CMOS technology, the IADC achieves 107.6-dB DR, 104.9-dB peak SNR, and 99.3-dB peak SNDR at 2 kS/s while dissipating 130 μW from 1.8-V (analog) / 1.2-V (digital) supply. This translates to a highly competitive FoMDR of 176.5 dB. The high-DR IADC reduces the gain of the preceding instrumentation amplifier (IA) such that significant DC and low-frequency disturbances can be tolerated. The advantages of high DR have been demonstrated by wearable Electrocardiography (ECG) and Electroencephalography (EEG) recordings under motion artifact.

Acute Effects of Various Movement Noise in Differential Learning of Rope Skipping on Brain and Heart Recovery Analyzed by Means of Multiscale Fuzzy Measure Entropy.

In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying manifold (neuro-)physiological impacts induced by different motor learning approaches. The various influences on brain or cardiac function are usually studied separately and modeled linearly. Limitations of these models have recently led to a rapidly growing application of nonlinear models. This study aimed to investigate the acute effects of various sequences of rope skipping on irregularity of the electrocardiography (ECG) and electroencephalography (EEG) signals as well as their interaction and whether these depend on different levels of active movement noise, within the framework of differential learning theory. Thirty-two males were randomly and equally distributed to one of four rope skipping conditions with similar cardiovascular but varying coordinative demand. ECG and EEG were measured simultaneously at rest before and immediately after rope skipping for 25 mins. Signal irregularity of ECG and EEG was calculated via the multiscale fuzzy measure entropy (MSFME). Statistically significant ECG and EEG brain area specific changes in MSFME were found with different pace of occurrence depending on the level of active movement noise of the particular rope skipping condition. Interaction analysis of ECG and EEG MSFME specifically revealed an involvement of the frontal, central, and parietal lobe in the interplay with the heart. In addition, the number of interaction effects indicated an inverted U-shaped trend presenting the interaction level of ECG and EEG MSFME dependent on the level of active movement noise. In summary, conducting rope skipping with varying degrees of movement variation appears to affect the irregularity of cardiac and brain signals and their interaction during the recovery phase differently. These findings provide enough incentives to foster further constructive nonlinear research in exercise-recovery relationship and to reconsider the philosophy of classical endurance training.

Performance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms.

Physiological signals like Electrocardiography (ECG) and Electroencephalography (EEG) are complex and nonlinear in nature. To retrieve diagnostic information from these, we need the help of nonlinear methods of analysis. Entropy estimation is a very popular approach in the nonlinear category, where entropy estimates are used as features for signal classification and analysis. In this study, we analyze and compare the performances of four entropy methods; namely Distribution entropy (DistEn), Shannon entropy (ShanEn), Renyi entropy (RenEn) and LempelZiv complexity (LempelZiv) as classification features to detect epileptic seizure (ES) from surface Electroencephalography (sEEG) signal. Experiments were conducted on sEEG data from 23 subjects, obtained from the CHB-MIT database of PhysioNet. ShanEn, RenEn and LempelZiv entropy are found to be potential features for accurate and consistent detection of ES from sEEG, across multiple channels and subjects.

Early Detection of Exposure to Toxic Chemicals Using Continuously Recorded Multi-Sensor Physiology.

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate medical counter measures in time. As a first step, we evaluated whether exposure to an opioid (fentanyl) or a nerve agent (VX) could be detected in freely moving guinea pigs using features from respiration, electrocardiography (ECG) and electroencephalography (EEG), where machine learning models were trained and tested on different sets (across subject classification). Results showed this to be possible with close to perfect accuracy, where respiratory features were most relevant. Exposure detection accuracy rose steeply to over 95% correct during the first five minutes after exposure. Additional models were trained to correctly classify an exposed state as being induced either by fentanyl or VX. This was possible with an accuracy of almost 95%, where EEG features proved to be most relevant. Exposure detection models that were trained on subsets of animals generalized to subsets of animals that were exposed to other dosages of different chemicals. While future work is required to validate the principle in other species and to assess the robustness of the approach under different, realistic circumstances, our results indicate that utilizing different continuously measured physiological signals for early detection and identification of toxic agents is promising.

Flexible, Air Dryable, and Fiber Modified Aerogel-Based Wet Electrode for Electrophysiological Monitoring

This study presents a novel type of wet electrode material for electrophysiological monitoring based on a conductive aerogel film. The electrode material incorporates cellulose nanocrystal and fiber as a biocompatible polymer and multi-walled carbon nanotube as a conductive filler. The fabricated electrode is fully characterized to explore the chemical, mechanical, electrical, and water absorption properties. The wet aerogel film presents suitable mechanical flexibility owing to the use of fiber enabling it to be conformal to curved surfaces like human body. The water absorption percentage of the fabricated aerogel film is extremely high (∼500%) due to the porosity of the film and hydrophilicity of the base polymer allowing it for effective wet electrode applications. The film is air dryable with a fast (∼10 min) and facile wetting process granting the electrode application for long-term, multiple use, and remote monitoring of patients. The electrical impedance range of the fabricated aerogel electrodes is relatively low (20 Ω/cm2-370 Ω/cm2) which is within the range of use for various electrophysiological monitoring purposes such as electrocardiography (ECG) and electroencephalography (EEG). Overall, the presented study introduces a novel wet electrode based on porous and electrically conductive aerogel film to be used for various biomedical applications.

On the functional connectivity between heart, muscle, and frontal brain cortex during exercise fatigability

We analyze the functional connectivity of the cortico-cardiac-muscular network during muscle fatigability due to exercise. For our experiments, we recruited ten volunteers who performed two cycling routines. Our volunteers were classified according to physical activity level as active or sedentary. First, we measured the electromyography (EMG) signal of the vastus lateralis muscle to find the volunteer’s individualized physical workload capacity at the fatigue threshold. Two to three days later, we measured the volunteers’ EMG, electrocardiography, and electroencephalography (EEG) data during the development of muscle fatigue with the individualized pedaling load previously obtained. We analyzed the EEG to identify the brain rhythm with highest power, then functional connectivity was assessed by computing the pairwise partial directed coherence of our measurements at such rhythm. Finally, we assessed significant changes in functional connectivity from start to finish of the exercise. Our results indicate that the sedentary group showed a significant decrease in connectivity, while the active group showed an increase in left-frontal connectivity. This might be explained in terms of frontal asymmetry phenomena which, for the case of active volunteers, may be a reflection of positive emotional states. Additionally, the active group showed increased self-regulatory processes in heart and muscle, while the sedentary group showed a decreased connectivity from heart to muscle together with a reduction in the muscle’s self-regulation. These last results may be explained by a preference of the body in the sedentary group to attend vital requirements instead of exercise.

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators.

Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient’s health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data’s regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters r (tolerance threshold), m (immersion dimension), and τ (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of τ, for ECG and EEG signals with different sampling frequencies (Fs), extracted from a digital repository. We considered four values of Fs (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of τ (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different (p < 0.05) for each F and τ value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with Fs and τ. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of Fs, and τ larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of Fs and τ larger than 1. Therefore, adjusting τ may be convenient for signals that were acquired with different Fs to ensure a reliable clinical classification. Furthermore, it is useful to set τ to values larger than 1 to reduce the computational cost.

Machine Learning Techniques with ECG and EEG Data: An Exploratory Study

Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.

Altered Interoceptive Processing in Generalized Anxiety Disorder-A Heartbeat-Evoked Potential Research.

Generalized anxiety disorder (GAD) is one of the most common anxiety disorders. The brain’s dysfunctional processing of interoceptive information is increasingly recognized as an important component of anxiety disorders. However, the neural mechanisms remain insufficiently understood. In the present study, patients with GAD and healthy control participants underwent an eyes-closed (EC) resting state (interoception) and eyes-open (EO) resting state (exteroception) without paying conscious attention to heartbeat. Electrocardiography (ECG) and electroencephalography (EEG) signals were recorded at the same time. The results show that in healthy controls, the heartbeat-evoked brain potential (HEP) was modulated by the conditions, with a significantly higher amplitude under EC than EO, while this was not the case in GAD patients. Further analysis revealed that the dysfunction of HEP modulation in GAD patients may be attributed to excessive interoceptive processing under EO, with a marginally higher HEP in GAD than in the healthy controls. Finally, the right prefrontal HEP amplitude during EC condition was significantly correlated with the severity of the patients’ anxiety symptoms. Our results suggest that altered cortical processing of interoceptive signals may play an important role in the pathophysiology of generalized anxiety disorder.

Carbon nanotube, poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) and Ag nanoparticle doped gelatin based electro-active hydrogel systems

In this study, gelatin-based conductive hydrogels doped with carbon nanotube (CNT), poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and silver (Ag) nanoparticle were prepared for use in biopotential measurements. Gelatin was modified by various concentrations of methacrylic anhydride (MA) to obtain photo-crosslinkable gelatin methacrylate (GelMA) polymer with low (21%), medium (44%) and high (78%) methacrylation degree. Chemical modification of gelatin was characterized by 1H-NMR spectroscopy. It was observed that mechanical properties of the GelMA hydrogels were highly dependent on methacrylation degree. Ultimate compressive stress of low, medium and high methacrylated gelatin hydrogel was measured to be 9.97 ± 0.14 kPa, 17.83 ± 1.73 kPa and 38.23 ± 2.96 kPa, respectively. Surface morphology of hydrogels was visualized by scanning electron microscope (SEM). The pore size of methacrylated gelatin hydrogel significantly reduced with the addition of CNT, PEDOT:PSS and Ag nanoparticle into the hydrogel matrix. The results of thermal gravimetric analysis showed that conductive hydrogels were of higher thermal stability than nonconductive GelMA hydrogel. The suitability of the prepared hydrogels for biopotential measurements was determined by equivalent impedance measurements. Impedance values of hydrogel samples were obtained in the frequency range of 20 Hz–500 Hz. The hydrogel materials prepared in this study exhibited high impedance at low frequencies and low impedance at high frequencies. The results of this study showed that the hydrogels produced with Ag nanoparticle are more suitable electrode materials for physiological measurement systems such as electrocardiography (ECG) and electroencephalography (EEG).

Short-Term Effects of Binaural Beats on EEG Power, Functional Connectivity, Cognition, Gait and Anxiety in Parkinson's Disease.

We applied rhythmic binaural sound to Parkinson's Disease (PD) patients to investigate its influence on several symptoms of this disease and on Electrophysiology (Electrocardiography and Electroencephalography (EEG)). We conducted a double-blind, randomized controlled study in which rhythmic binaural beats and control were administered over two randomized and counterbalanced sessions (within-subjects repeated-measures design). Patients ([Formula: see text], age [Formula: see text], stage I-III Hoehn & Yahr scale) participated in two sessions of sound stimulation for 10[Formula: see text]min separated by a minimum of 7 days. Data were collected immediately before and after both stimulations with the following results: (1) a decrease in theta activity, (2) a general decrease in Functional Connectivity (FC), and (3) an improvement in working memory performance. However, no significant changes were identified in the gait performance, heart rate or anxiety level of the patients. With regard to the control stimulation, we did not identify significant changes in the variables analyzed. The use of binaural-rhythm stimulation for PD, as designed in this study, seems to be an effective, portable, inexpensive and noninvasive method to modulate brain activity. This influence on brain activity did not induce changes in anxiety or gait parameters; however, it resulted in a normalization of EEG power (altered in PD), normalization of brain FC (also altered in PD) and working memory improvement (a normalizing effect). In summary, we consider that sound, particularly binaural-rhythmic sound, may be a co-assistant tool in the treatment of PD, however more research is needed to consider the use of this type of stimulation as an effective therapy.

Flexible microneedle array electrode using magnetorheological drawing lithography for bio-signal monitoring

Monitoring and timely intervention are extremely important in the continuous home care. Microneedle array electrode (MAE) have been employed for the long-term bio-signal monitoring without skin preparation. We developed a novel magneto-rheological drawing lithography (MRDL) method to cost-effectively fabricate a flexible micro-needle array electrode (FMAE) for the wearable bio-signal monitoring. Flexible substrate may match closely with curved skin and maintain a stable interface between skin and electrode. The formation mechanism of microneedle array (MA) by MRDL and bio-signal recording performance of FMAE were investigated. MA can be one-step drawn from the droplet array of curable magnetorheological fluid under the assist of external magnetic field. Ti/Au film was coated on the surface of solidified MA to insure the conductivity and compatibility of FMAE. 36-FMAE consists of 6×6 micro-needles with an average height of 600μm and an average tip radius of 12μm. FMAE with 36 needles (36-FMAE) shows a better bio-signal monitoring performance in some specific situations compared with flexible dry electrode (FDE) and commercial Ag/AgCl electrode. Electrode-skin interface impedance (EII) measured by 36-FMAE is the lowest at a given low input frequency and the amplitude of electrocardiography (ECG) and electroencephalography (EEG) signals recorded by 36-FMAE is the largest. 36-FMAE can collect more distinguishable features and weaken the effect of motion artifact during the dynamical ECG recording. Therefore, 36-FMAE is a promising sensor for the wearable bio-signal monitoring in home care.

A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability.

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert and drowsy states from physiological data collected from 22 healthy subjects in a driving simulator-based study. A monotonous driving environment is used to induce drowsiness in the participants. Various time and frequency domain feature were extracted from EEG including time domain statistical descriptors, complexity measures and power spectral measures. Features extracted from the ECG signal included heart rate (HR) and heart rate variability (HRV), including low frequency (LF), high frequency (HF) and LF/HF ratio. Furthermore, subjective sleepiness scale is also assessed to study its relationship with drowsiness. We used paired t-tests to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features of both modalities (EEG and ECG) are then combined to investigate the improvement in performance using support vector machine (SVM) classifier. The other main contribution of this paper is the study on channel reduction and its impact to the performance of detection. The proposed method demonstrated that combining EEG and ECG has improved the system’s performance in discriminating between alert and drowsy states, instead of using them alone. Our channel reduction analysis revealed that an acceptable level of accuracy (80%) could be achieved by combining just two electrodes (one EEG and one ECG), indicating the feasibility of a system with improved wearability compared with existing systems involving many electrodes. Overall, our results demonstrate that the proposed method can be a viable solution for a practical driver drowsiness system that is both accurate and comfortable to wear.

Fitting Distribution for Electromyography and Electroencephalography Signals Based on Goodness-of-Fit Tests

Physiological devices (PDs) like upright bicycles, steppers and treadmills act as Human Machine Interface (HMI) during rehabilitation. The main issue on how to utilize biosignals such as Electromyography (EMG), Electrocardiography (ECG) and Electroencephalography (EEG) as an inputs for HMI to control the PDs. Biosignals are stochastic and complex as they influenced by anatomical and physiological properties of muscles. To maximize the training time during exercise, the features of the biosignals (e.g., fatigue, contraction or relaxation) should be extracted to maintain the system as a reliable working condition. For that purpose, it is feasible to try out a probabilistic distribution as a feature to illustrate the pattern of muscle activation. Before the estimation of parameter distribution is conducted, we need to verify the types of distribution that fit the raw biosignals. In this study, EMG and EEG signals will be considered to find the most suitable distribution for the signals. The selected model is chosen based on a minimum error produced by two Goodness-of-Fit (GOF) tests namely Kolmogorov-Smirnov statistic, D and Anderson Darling statistic, A2. As a result, a Generalized Extreme Value (GEV) distribution is found the most appropriate distribution compared to Generalized Pareto (GP) and Exponential (EXP) distributions for describing the EMG and EEG signals.

Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model

Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving and diagnosis. In this paper, a new framework for clustering of long-term biomedical time series such as electrocardiography (ECG) and electroencephalography (EEG) signals is proposed. Specifically, local segments extracted from the time series are projected as a combination of a small number of basis elements in a trained dictionary by non-negative sparse coding. A Bag-of-Words (BoW) representation is then constructed by summing up all the sparse coefficients of local segments in a time series. Based on the BoW representation, a probabilistic topic model that was originally developed for text document analysis is extended to discover the underlying similarity of a collection of time series. The underlying similarity of biomedical time series is well captured attributing to the statistic nature of the probabilistic topic model. Experiments on three datasets constructed from publicly available EEG and ECG signals demonstrates that the proposed approach achieves better accuracy than existing state-of-the-art methods, and is insensitive to model parameters such as length of local segments and dictionary size.

Decomposition and compression for ECG and EEG signals with sequence index coding method based on matching pursuit

An efficient compression method is proposed by encoding the sequence index of atoms based on matching pursuit (MP) algorithm with over-complete Gabor dictionary, which has the merit to adjust the compression ratio (CR) according to the practical request with low distortion. It is also combined with genetic algorithm (GA) to reduce the computation complexity. Then, the validity of this method is verified by applying it in the compression of electrocardiography (ECG) and Electroencephalography (EEG) signals. The simulation results show that the CR can be achieved at 18:1 with only 1.06% and 2.15% reconstruction errors on ECG and EEG signals respectively. It has higher CR and less reconstruction errors compared to that of the traditional methods, and noise suppression effect is also presented.

Postictal increase in T-wave alternans after generalized tonic-clonic seizures

Sudden unexpected death in epilepsy (SUDEP) occurs most frequently in people with chronic uncontrolled epilepsy. Tonic-clonic seizures are a well-recognized risk factor for SUDEP. T-wave alternans (TWA) is a novel independent marker of risk of sudden cardiac death and cardiovascular mortality. The aim of this study was to determine if the level of TWA in patients with epilepsy differed after complex-partial and secondary generalized tonic-clonic seizures (sGTCS). Electrocardiography (ECG) and electroencephalography (EEG) data from patients with drug-resistant focal epilepsy who had both complex partial and sGTCS during video-EEG telemetry were retrospectively reviewed. Periictal TWA, heart rate (HR), and heart rate variability (HRV) were analyzed. ECG and EEG data of 16 patients (31.6 ± 8.7 years, nine male) with focal epilepsies were analyzed. sGTCS led to a postictal increase in TWA for 15 min as well as a higher postictal HR and decreased postictal HRV for the whole observation time of 30 min. There was no significant association between postictal TWA or HR and seizure duration or duration of the tonic-clonic phase. Our study demonstrates that derangements in autonomic function and TWA are highly prevalent after sGTCS in patients with chronic uncontrolled epilepsy. Further studies are warranted to investigate the value of TWA for risk stratification in epilepsy patients for sudden cardiac death and SUDEP.

Implementation of compressive sensing in ECG and EEG signal processing

The purpose of this paper is to exploit compressive sensing (CS) method in dealing with electrocardiography (ECG) and electroencephalography (EEG) signals at a high compression ratio. In order to get sparse data of ECG and EEG signals before being compressed, a combined scheme was presented by using wavelet transform and iterative threshold method; then, compressive sensing is executed to make the data compressed. After doing compressive sensing, Bayesian compressive sensing (BCS) is used to reconstruct the original signals. The simulation results show that compressive sensing is an effective method to make data compressed for ECG and EEG signals with high compression ratio and good quality of reconstruction. Furthermore, it shows that the proposed scheme has good denoising effects.

Prognostic value of electrocardiography and electroencephalography in patients with ischemic stroke.

Stroke is the most common disease among neurological disorders, often resulting in handicap, complications and mortality. Recent studies revealed that electrocardiography (ECG) and electroencephalography (EEG) were noninvasive but effective methods to assess the prognosis of stroke patients. The purpose of this study is to evaluate the use of EEG and ECG for the prognosis of ischemic stroke. Thirty five patients with supratentorial ischemic stroke and muscle weakness were included in the study carried out at the Ali-EbneAbitaleb Hospital, Rafsanjan, Iran. At admission, muscle power was recorded for every patient. EEG and ECG were performed on patients within 12 hours of admission. The muscle power was re-evaluated for all study cases at 3 months. The data were analyzed using SPSS software; Mann-Whitney and Kruskal-Wallis tests. Opium addiction (45.7%), hypertension (40%), and smoking (28.6%) were the most common risk factors. ECG change was seen in 71.4% of patients (ST-T change: 51.4%, QT: 22.9%). No significant correlation was seen between ECG and muscle power changes at three months among stroke patients. 57.1% of patients had mild to severe abnormality in EEG and a significant correlation was seen between EEG and muscle power changes at three months (P= 0.01). This study showed that EEG abnormality was positively correlated with poor prognosis in our patients with ischemic stroke. This study did not show any significance for ECG, which may be due to a small patient number, and further study is needed.

Anordnung zur Aufzeichnung und sofortigen Interpretation der myoelektrischen Aktivität des Magens — Elektrogastrographie — Computer-aided Measurement and Interpretation of the Myoelectrical Activity of the Stomach

Unlike other electrophysiological measurements such as electrocardiography (ECG) and electroencephalography (EEG), the cutaneous measurement of the electrical activity of the stomach (electrogastrography, EGG) is not an established clinical diagnostic procedure. To overcome common problems in acquiring very-low-frequency signals in the presence of large and unstable voltages we developed a real time data acquisition and analysis system based on linear-phase digital signal processing instead of analog filtering. FFT's computed on line from highly overlapping time sets are displayed as pseudo-3D or grey-scale graphic plots.