Signal Analysis Procedures Research Articles

Analysis of EEG signals and data acquisition methods: a review

ABSTRACT Early illness diagnosis and prediction are important goals in healthcare in order to offer timely preventive measures. The best, least invasive, and most reliable way for identifying any neurological disorder is EEG analysis. If neurological disorders could somehow be predicted in advance, patients could be saved from their detrimental consequences. With promising new advancements in machine learning-based algorithms, Early and precise prediction might induce a radical shift. Here, we present a thorough analysis of cutting-edge AI methods for exploiting EEG data for Parkinson’s disease early warning symptoms detection, sleep apnoea, drowsiness, schizophrenia, motor imagery classification, and emotion recognition, among other conditions. All of the EEG signal analysis procedures used by different authors, such as hardware software data sets, channel, frequency, epoch, preprocessing, decomposition method, features, and classification, have been compared and analysed in detail. We will point out the difficulties, gaps and limitations in the current research and suggest future avenues of research.

A robust and reliable methodology to perform GECI-based multi-time point neuronal calcium imaging within mixed cultures of human iPSC-derived cortical neurons.

Human induced pluripotent stem cells (iPSCs), with their ability to generate human neural cells (astrocytes and neurons) from patients, hold great promise for understanding the pathophysiology of major neuropsychiatric diseases such as schizophrenia and bipolar disorders, which includes alterations in cerebral development. Indeed, the in vitro neurodifferentiation of iPSCs, while recapitulating certain major stages of neurodevelopment in vivo, makes it possible to obtain networks of living human neurons. The culture model presented is particularly attractive within this framework since it involves iPSC-derived neural cells, which more specifically differentiate into cortical neurons of diverse types (in particular glutamatergic and GABAergic) and astrocytes. However, these in vitro neuronal networks, which may be heterogeneous in their degree of differentiation, remain challenging to bring to an appropriate level of maturation. It is therefore necessary to develop tools capable of analyzing a large number of cells to assess this maturation process. Calcium (Ca2+) imaging, which has been extensively developed, undoubtedly offers an incredibly good approach, particularly in its versions using genetically encoded calcium indicators. However, in the context of these iPSC-derived neural cell cultures, there is a lack of studies that propose Ca2+ imaging methods that can finely characterize the evolution of neuronal maturation during the neurodifferentiation process. In this study, we propose a robust and reliable method for specifically measuring neuronal activity at two different time points of the neurodifferentiation process in such human neural cultures. To this end, we have developed a specific Ca2+ signal analysis procedure and tested a series of different AAV serotypes to obtain expression levels of GCaMP6f under the control of the neuron-specific human synapsin1 (hSyn) promoter. The retro serotype has been found to be the most efficient in driving the expression of the GCaMP6f and is compatible with multi-time point neuronal Ca2+ imaging in our human iPSC-derived neural cultures. An AAV2/retro carrying GCaMP6f under the hSyn promoter (AAV2/retro-hSyn-GCaMP6f) is an efficient vector that we have identified. To establish the method, calcium measurements were carried out at two time points in the neurodifferentiation process with both hSyn and CAG promoters, the latter being known to provide high transient gene expression across various cell types. Our results stress that this methodology involving AAV2/retro-hSyn-GCaMP6f is suitable for specifically measuring neuronal calcium activities over multiple time points and is compatible with the neurodifferentiation process in our mixed human neural cultures.

The effect of time-varying operating condition on the crack induced impulses and its application to gearbox tooth crack diagnosis

Gearbox tooth crack diagnosis under Time-Varying Operating Conditions (TVOC) is a challenging issue. TVOC induce both Amplitude Modulation (AM) and Frequency Modulation (FM) effects into gearbox vibration signals, which results in difficulties in tooth crack diagnosis. To overcome this problem, the TVOC-induced AM and FM effects on vibration signals of gearboxes with tooth cracks need to be well studied. Many studies have been reported to investigate the influence of TVOC on gearbox vibration signals using dynamic modelling, some of which only focused on healthy gearboxes while others only considered piecewise constant operating conditions, failing to make comprehensive analyses involving both tooth cracks and TVOC. This paper aims to conduct a comprehensive study on how TVOC affect vibration signals of gearboxes with tooth cracks using dynamic simulation. Firstly, gear tooth mesh stiffness is evaluated considering both tooth crack severity progression and operating condition variations, through which gearbox vibration responses are generated under TVOC. A signal analysis procedure is proposed with its focus placed on the Crack Induced Impulses (CII), which are extracted from gearbox vibration signals using the adaptive harmonic decomposition method or its modified version. Envelope analysis is conducted on the CII to study how the CII are affected by TVOC, and a linear dependence of the AM effect of the CII on TVOC is identified. Based on the identified linear dependence, a novel Condition Indicator (CI), which is sensitive to crack growth while insensitive to operating condition variations, is proposed to track tooth crack severity progression under TVOC. The linear dependence of the AM effect of the CII on TVOC and the effectiveness of the proposed CI for tooth crack diagnosis are demonstrated using both simulated gearbox vibration signals and experimental gearbox vibration datasets.

Modified photoplethysmography signal processing and analysis procedure for obtaining reliable stiffness index reflecting arteriosclerosis severity

Objective. This study aimed to describe a modified photoplethysmography (PPG) signal processing and analysis procedure to obtain a more reliable arterial stiffness index (SI). Approach. Three parameters were used to assess the PPG signal quality without prominent diastolic waves, which are similar to a sinusoidal waveform shape. The first parameter, sinusoidal ratio (S-value), was based on frequency-domain analysis: a higher S-value indicated the presence of PPG pulse wave with unapparent diastolic peak. The second parameter was the time difference between systolic peak-to-diastolic peak and the systolic peak-to-dicrotic notch. The third parameter was the percentage of sin-like waveform in the PPG signals. The applicability of these parameters was demonstrated in 40 participants, including 11 with apparent diastolic peaks in the PPG signals and 29 with unapparent diastolic peaks. Main results. An S-value of >3.5 indicated apparent diastolic peaks in the PPG signals. In addition, a systolic peak-to-diastolic peak time difference >80% and a sin-like waveform >55% may be associated with severity of vascular aging. Significance. These parameters successfully detected low-quality PPG signals with unapparent diastolic waveform before SI calculation, thereby ensuring the accuracy of subsequent evaluation of cardiovascular-related disease and clinical risk stratification.

Unsupervised study of plethysmography signals through DTW clustering.

The study of plethysmography time series is crucial to better understand the breathing behavior of mice, in particular the influence of neurotoxins on the respiratory system. Current approaches rely on a few respiratory descriptors computed on individual breathing cycles that fail to account for the variety of breathing habits and their evolution with time. In this paper we introduce a new procedure for the automatic analysis of plethysmography signals. Our method relies on a new and robust segmentation of respiratory cycles and a DTW-based clustering algorithm to extract the most typical respiratory cycles (called reference sequences). We can then create a symbolic representation of any new recording by matching respiratory cycles to their closest reference sequence. This new representation is a visual and quantitative tool to assess the breathing behavior of mice and its evolution with time. Our method is applied to plethysmography signals collected on mice with two different genotypes and exposed to a neurotoxin. Clinical relevance This article proposes a novel approach to study plethysmography data. Our algorithm is able to accurately extract clinically meaningful respiratory cycles and the associated ventilation patterns descriptors such as tidal volume and inhalation/exhalation duration. In addition, thanks to the associated symbolic representation of signals, the temporal evolution of respiration is easily quantified. This opens a new research path to study the often slowly evolving and subtle influence of neurotoxins on the respiratory system.

Zero-crossing patterns reveal subtle epileptiform discharges in the scalp EEG

Clinical diagnosis of epilepsy depends heavily on the detection of interictal epileptiform discharges (IEDs) from scalp electroencephalographic (EEG) signals, which by purely visual means is far from straightforward. Here, we introduce a simple signal analysis procedure based on scalp EEG zero-crossing patterns which can extract the spatiotemporal structure of scalp voltage fluctuations. We analyzed simultaneous scalp and intracranial EEG recordings from patients with pharmacoresistant temporal lobe epilepsy. Our data show that a large proportion of intracranial IEDs manifest only as subtle, low-amplitude waveforms below scalp EEG background and could, therefore, not be detected visually. We found that scalp zero-crossing patterns allow detection of these intracranial IEDs on a single-trial level with millisecond temporal precision and including some mesial temporal discharges that do not propagate to the neocortex. Applied to an independent dataset, our method discriminated accurately between patients with epilepsy and normal subjects, confirming its practical applicability.

Sensorless Unbalance Diagnosis of Affiliated Rotating Chamber Based on Driving Current of Permanent Magnet Synchronous Motor

Unbalance exists widely in rotating electromechanical systems, resulting in vibrations, instability, and many other negative influences. In this article, a novel method for unbalance diagnosis of the affiliated rotating chamber in 2-DOF precision centrifuge is proposed. Unlike the widely applied sensor-based unbalance diagnosis methods, the proposed one in this article investigates the internal relationships between signals in the control loop, involving the driving current of the permanent magnet synchronous motor, the disturbance signal of the control loop as well as the unbalance signal of the rotating chamber. By using the unbalance kinematic analysis, two kinematic-mechanical-electric driving current models are established, which are based on the 2-DOF precision centrifuge with uniform motion and uniform acceleration, respectively. Based on the models, the valid information of the unbalance is included in the driving current. Furthermore, a signal analysis procedure, which includes domain conversion and ensemble empirical mode decomposition, is presented to calculate the unbalance mass and the phase of the unbalance. Finally, the effectiveness of methods is validated by experiments and simulations.

Complexity of Frontal Cortex fNIRS Can Support Alzheimer Disease Diagnosis in Memory and Visuo-Spatial Tests.

Decline in visuo-spatial skills and memory failures are considered symptoms of Alzheimer’s Disease (AD) and they can be assessed at early stages employing clinical tests. However, performance in a single test is generally not indicative of AD. Functional neuroimaging, such as functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests in an ecological setting to support diagnosis. Indeed, neuroimaging should not alter clinical practice allowing free doctor-patient interaction. However, block-designed paradigms, necessary for standard functional neuroimaging analysis, require tests adaptation. Novel signal analysis procedures (e.g., signal complexity evaluation) may be useful to establish brain signals differences without altering experimental conditions. In this study, we estimated fNIRS complexity (through Sample Entropy metric) in frontal cortex of early AD and controls during three tests that assess visuo-spatial and short-term-memory abilities (Clock Drawing Test, Digit Span Test, Corsi Block Tapping Test). A channel-based analysis of fNIRS complexity during the tests revealed AD-induced changes. Importantly, a multivariate analysis of fNIRS complexity provided good specificity and sensitivity to AD. This outcome was compared to cognitive tests performances that were predictive of AD in only one test. Our results demonstrated the capabilities of fNIRS and complexity metric to support early AD diagnosis.

Fetal Cardiac Doppler Signal Processing Techniques: Challenges and Future Research Directions.

The fetal Doppler Ultrasound (DUS) is commonly used for monitoring fetal heart rate and can also be used for identifying the event timings of fetal cardiac valve motions. In early-stage fetuses, the detected Doppler signal suffers from noise and signal loss due to the fetal movements and changing fetal location during the measurement procedure. The fetal cardiac intervals, which can be estimated by measuring the fetal cardiac event timings, are the most important markers of fetal development and well-being. To advance DUS-based fetal monitoring methods, several powerful and well-advanced signal processing and machine learning methods have recently been developed. This review provides an overview of the existing techniques used in fetal cardiac activity monitoring and a comprehensive survey on fetal cardiac Doppler signal processing frameworks. The review is structured with a focus on their shortcomings and advantages, which helps in understanding fetal Doppler cardiogram signal processing methods and the related Doppler signal analysis procedures by providing valuable clinical information. Finally, a set of recommendations are suggested for future research directions and the use of fetal cardiac Doppler signal analysis, processing, and modeling to address the underlying challenges.

OTH radar phenomenology: signal interpretation and target characterization at HF

Implicitly or explicitly, the extraction of information from radar echoes is based on a physical model of the interaction between the radar system and the target, the study of which is sometimes referred to as radar phenomenology [1]. It follows that the detail and fidelity of the physical model determine the prospective observables, the accuracy of measurement, and the fidelity of interpretation. Of course, there is little to be gained from employing a highly sophisticated model if the radar measurements have neither the dimensionality nor the dynamic range to support estimation of the parameters invoked. Contrariwise, there is much to be lost if valuable information encoded in the target echoes is overlooked because the signal analysis and interpretation procedures fail to undertake the corresponding decoding processes.

Vibration Measurement and Spectral Analysis of Chassis Frame Mounted Structure for Off-Road Wheeled Heavy Vehicles

Chassis mounted structure is a base component for shelters or containers mounted on heavy transport vehicles. When the vehicle is driven in rough terrains or during off-road transportation this structure has a significant role in protecting the sophisticated cargo and intelligent tracking systems placed inside the shelters. During off-road transportation or warhead conditions the vehicle is subjected to large unevenness in load due to road or soil irregularities in rough terrains, which causes vibrations to be induced in the vehicle. As the nature of vibrations induced in vehicle during travel on off-road or cross-country terrains is random and unpredictable, there is a concern to analyse the vibration response of chassis and chassis mounted structures is needed. Present work deals with vibration measurement and spectral analysis of a chassis mounted structure designed for off-road and commercial transport vehicles. The road profile on which the vibration measurement has been carried out includes paved road and cross-country terrain segments. The vibration measurement has been carried at three different vehicle speeds. Signal analysis procedure for the acquired test data is discussed. The chassis mounted structure under concern is intended to hold two shelters or containers. From the vibration measurement at critical locations, g-(RMS) and g-(peak) values for paved and cross-country roads have been found out. Power spectral density values have also been found on chassis and structure for the same transport situations. Major inferences include the evaluation of minimum and maximum g-values (peak and RMS) on chassis and chassis mounted structure. Power spectral density graphs are constructed from which the dominant frequency for both road profiles is found out.

A fault diagnosis methodology for rolling element bearings based on advanced signal pretreatment and autoregressive modelling

This study proposes a methodology for rolling element bearings fault diagnosis which gives a complete and highly accurate identification of the faults present. It has two main stages: signals pretreatment, which is based on several signal analysis procedures, and diagnosis, which uses a pattern-recognition process. The first stage is principally based on linear time invariant autoregressive modelling. One of the main contributions of this investigation is the development of a pretreatment signal analysis procedure which subjects the signal to noise cleaning by singular spectrum analysis and then stationarisation by differencing. So the signal is transformed to bring it close to a stationary one, rather than complicating the model to bring it closer to the signal. This type of pretreatment allows the use of a linear time invariant autoregressive model and improves its performance when the original signals are non-stationary. This contribution is at the heart of the proposed method, and the high accuracy of the diagnosis is a result of this procedure. The methodology emphasises the importance of preliminary noise cleaning and stationarisation. And it demonstrates that the information needed for fault identification is contained in the stationary part of the measured signal.The methodology is further validated using three different experimental setups, demonstrating very high accuracy for all of the applications. It is able to correctly classify nearly 100 percent of the faults with regard to their type and size. This high accuracy is the other important contribution of this methodology. Thus, this research suggests a highly accurate methodology for rolling element bearing fault diagnosis which is based on relatively simple procedures. This is also an advantage, as the simplicity of the individual processes ensures easy application and the possibility for automation of the entire process.

A temperature oscillation instrument to determine pyroelectric properties of materials at low frequencies: Towards elimination of lock-in methods.

Pyroelectric properties of materials can be accurately determined by applying a new digital signal processing method on the discrete sampled data obtained with a temperature oscillation technique. The pyroelectric coefficient is calculated from the component of the generated current 90° out of phase with respect to the sinusoidal temperature wave. The novelty of the proposed approach lies in the signal analysis procedure which implements a simple Fast Fourier transform that filters residual noise through convolution, and calculates the phase difference between the peaks of the temperature and current waves. The new idea requires relatively simple hardware and enables very accurate measurement of the pyroelectric coefficient of materials at ultra low frequencies, 1-250 mHz, without using costly lock-in amplifiers.

Correlation of Temperature-Programmed Oxidation with Microscopy for Quantitative Morphological Characterization of Thermal Cokes Produced from Pilot and Commercial Delayed Cokers

A wide series of thermal cokes obtained from commercial delayed cokers and a pilot coking plant have been investigated by polarized light microscopy observation, scanning electron microscopy (SEM), and temperature-programmed oxidation (TPO) technology. For discrimination of the characterization of the coke samples, the anisotropy degrees of the cokes are found to be considerably different. According to the series of runs in the pilot plant, the restricted mesophase development in thermal cokes could be ascribed to the increasing severity of coking conditions. A previous proposed signal analysis procedure was then applied to the TPO profiles of the cokes to quantitatively acquire parameters for further correlation. A fairly good linear dependency of optical texture index (OTI) upon the proportion of anisotropic carbon species from TPO (correlation coefficient of 0.984) was observed. All of the results obtained in this and our previous (Chen, K.; Xue, Z.; Liu, H.; Guo, A.; Wang, Z. A temperature-programmed ...

On computation of calcium cycling anomalies in cardiomyocytes data.

Induced pluripotent stem cell (iPSC) lines derived from skin fibroblasts of patients suffering from cardiac disorders were differentiated to cardiomyocytes and used to generate a data set of Ca(2+) transients of 136 recordings. The objective was to separate normal signals for later medical research from abnormal signals. We constructed a signal analysis procedure to detect peaks representing calcium cycling in signals and another procedure to classify them into either normal or abnormal peaks. Using machine learning methods we classified signals into normal or abnormal signals on the basis of peak findings in them. We compared classification results obtained to those made visually by an expert biotechnologist who assessed the signals independent of the computer method. Classification accuracies of around 85% indicated high congruence between two modes denoting the high capability and usefulness of computer based processing for the present data.

A temperature-programmed oxidation method for quantitative characterization of the thermal cokes morphology

A series of thermal cokes obtained by coking of vacuum residues and fractions have been studied by polarized light microscopy observation, scanning electron microscope (SEM), and temperature-programmed oxidation (TPO) technique. The anisotropic degrees of coke samples are found to be considerably different for discriminating characterization, which indicates that the coke morphology is closely dependent on the chemical nature of the coking materials. The restricted mesophase development in thermal coke might be ascribed to the increasing amount of heteroatoms contained in the parent coking materials. A proper signal analysis procedure of TPO profiles was obtained to discriminate the carbon species in the coke quantitatively, and then a good one-to-one correlation between determinations by polarized-light micrographs analysis and TPO method was observed, which shows that the TPO technique could be a suitable and more rapid method than polarized-light microscopy to quantitatively assess the morphology of coke, even in some situations in which the traditional method is inadequate.

Magneto-optical Kerr effect spectroscopy based study of Landé g-factor for holes in GaAs/AlGaAs single quantum wells under low magnetic fields

Results from a magneto-optical Kerr effect (MOKE) spectroscopy study of ground state heavy-hole and light-hole excitons in GaAs/Al0.3Ga0.7As single quantum wells, with widths ranging from 4.3 nm to 14 nm, are presented. A novel setup and signal analysis procedure was adopted whereby polar MOKE measurements in magnetic fields up to 1.8 T could be performed with a conventional H-frame electromagnet. A first principles simulation based procedure used for simultaneously analyzing both the measured Kerr rotation and Kerr ellipticity spectral lineshapes is described in detail. The Zeeman splitting obtained from the above analysis helped to determine the longitudinal Landé g-factors. The hole g-factors were found to vary with well width, ranging from −0.6 to 1.1 for heavy-holes and 6.5 to 8.6 for light-holes. While the heavy-hole g-factor values are in fair agreement with values expected from k·p perturbation theory, no evidence is found to support theoretical predictions of possible well width dependent giant light-hole g-factors in GaAs/AlxGa1–xAs single quantum wells.

Wavelet Denoising in Electrical Resistance Based Damage Detection of Carbon Fiber Composite Materials

As carbon fiber reinforced polymer composites (CFRPs) are multifunctional materials in which the damage is coupled with the change in material electrical resistance, the use of electrical conductivity monitoring can provide real-time information concerning the damage state through examining the change in resistance/electric potential. It has been shown that, in some recent work, resistance measurement allows the monitoring of the in-situ evolution of various internal damage nucleation and growth phenomena such as fiber fractures, interply matrix cracks and interply delamination. However, one of the common difficulties in measuring the small changes of electrical signals due to failures in the CFRPs is the contamination of extraneous noises during the detection process. In this paper, signal analysis procedures are applied to minimize or eliminate the contaminated noise during the damage detection process, as to maximize the accuracy of the damage detection process. In addition to the traditional time signal averaging technique, the Fast Fourier Transform (FFT) is applied to filter at the narrow band near the input frequency as to eliminate the unwanted noises. However, based on laboratory experiments, the authors have found that the frequency filtering techniques will retain significant inaccuracy when extraneous noises are in the neighborhood of the input frequency, and, in some cases, accurate measurement of the electrical signals can be susceptable when noise components are significantly higher than the original input signal. To overcome this drawback, the use of a joint time-frequency technique with wavelet transforms has been introduced. As a joint time-frequency signal analysis tool, the wavelet transforms offer simultaneous interpretation of the signal in both time and frequency domains which results in signal filtering effects in both time and frequency domains. Experimental results by the authors have shown significant improvement in accuracy when using the wavelets than those of the FFT and the time averaging techniques. This paper demonstrates the wavelet transform applications in electrical signal noise reduction of CFRPs as well as the use of both continuous wavelet (CWT) and discrete wavelet (DWT) transforms. It has been found that when noise is small, the DWT is more appropriate as the original signal can be reconstructed to offer better comparative information. The use of CWT will provide better results when random noises are significantly higher. Although the use of CWT does not have the ability revert back to the original signal, the comparison of the CWT coefficients can offer more valuable information concerning the damage occurrences while the repeated applications of DWT will result in accumulated errors such that the original shapes of the wavelet cannot be correctly reconstructed.

Experimentation with Fourier Transformations

Two simple experiments to demonstrate the physical meaning of Fourier expansion and Fourier transformation are presented. The only experimental setups needed for these experiments are a function generator and a laptop or a personal computer. Free downloadable end-user Matlab- based software for carrying out the experiments is made available. The two programs are designed such that no prior Matlab programming skills are assumed. The results obtained clearly demonstrate the physical idea behind the Fourier mathematical analysis technique. Such experimentation may form the bases of further advanced signal analysis procedures.

Learned vocalizations in budgerigars (Melopsittacus undulatus): The relationship between contact calls and warble song

The budgerigar (Melopsittacus undulatus) has an extraordinarily complex, learned, vocal repertoire consisting of both the long rambling warble song of males and a number of short calls produced by both sexes. In warble, the most common elements (>30%) bear a strong resemblance to the highly frequency-modulated, learned contact calls that the birds produce as single utterances. However, aside from this apparent similarity, little else is known about the relationship between contact calls and warble call elements. Here, both types of calls were recorded from four male budgerigars. Signal analysis and psychophysical testing procedures showed that the acoustic features of these two vocalizations were acoustically different and perceived as distinctive vocalizations by birds. This suggests that warble call elements are not simple insertions of contact calls but are most likely different acoustic elements, created de novo, and used solely in warble. Results show that, like contact calls, warble call elements contain information about signaler identity. The fact that contact calls and warble call elements are acoustically and perceptually distinct suggests that they probably represent two phonological systems in the budgerigar vocal repertoire, both of which arise by production learning.