Waveform Changes Research Articles

Predialysis central arterial waveform and blood pressure changes during hemodialysis

To investigate the predictive value of the central arterial waveform for intradialytic blood pressure (BP) change, a total of 152 hemodialysis patients (mean age 68 years) on a thrice-weekly hemodialysis schedule were enrolled, and at both the first and second session of the week, BP and central arterial waveform were measured every 30 min during hemodialysis. In both sessions, a 1-standard deviation increase in baseline subendocardial viability ratio (SEVR), an index of subendocardial perfusion, as well as in baseline systolic BP (SBP) was an independent predictor of maximum SBP decrease ≥ 30 mmHg during hemodialysis. When divided into four groups based on the respective median level of SEVR in the SBP ≥ median and SBP < median groups, intradialytic SBP change was different among the subgroups. Multiple logistic regression analysis revealed that, compared with the SBP < median; low SEVR group, the SBP < median; high SEVR group had lower risk, and the SBP ≥ median; low SEVR group had higher risk of SBP decrease ≥ 30 mmHg, but the risk did not differ from that in the SBP ≥ median; high SEVR group. Predialysis subendocardial perfusion evaluated by SEVR was associated with the maximum intradialytic BP decrease, and evaluation of the central arterial waveform could be used as complementary screening for intradialytic BP change.

Role of sonography in detection and evaluation of post liver transplant complications.

To evaluate the role of sonography in revealing and characterizing liver transplant complications based on gray scale and color Doppler, describe the normal Doppler findings, and discuss the significance of distinguishing normal transient changes in the spectral waveform from findings that may suggests ominous complications. We carried out a retrospective cross-sectional study at Prince Sultan Military Medical City, Riyadh, Saudi Arabia. The medical records and imaging studies of a total of 122 candidates who underwent transplantation between January 2016 to February 2022 were reviewed. Our results showed that most patients were males with the most frequent age group being those between 54-71 years. Hepatitis B virus and hepatic cellular carcinoma were the most common indications for transplants. A total of 95 patients received a graft from a living related donor. Regarding complications, biliary issues (including leaks and ducts dilation) were the second most frequent complication after collections. Vascular complications represented 7.4% of all complications and was the leading cause of death in 4.8% of cases. Among all vascular issues encountered during liver transplant, portal vein thrombosis was the most predominant. In respect to Doppler findings, portal vein velocities and resistive index of hepatic artery had re-averaged within 7-10 post-operative days in most patients. Ultrasound plays crucial role in the post-operative management of compilations, facilitating early detection, which is substantial for the graft survival.

Quantitative detection of the width and depth of subsurface defects in curved structures using ultrasonic Rayleigh waves

ABSTRACT Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications.

Genesis of ischemic ST segment changes: A study using precordial bipolar leads and regional vectorcardiograms

BackgroundPrecordial Bipolar Leads (PBLs), Weighted Unipolar Leads (WUL), and Regional Vectorcardiograms (RVCGs) are constructed using the same data recorded by a standard 12‑lead ECG, but they provide additional information not visible in the standard 12‑lead ECG (ECG) tracings. ObjectivesIn previous studies during balloon occlusion of the LAD and RCA, we observed a complete ischemic inversion of the QRS waves, with folding of the loop and ST segment shift. In the present study, we aim to investigate this abnormality using new ECG methods. We hypothesize that utilizing PBL, WUL, and RVCG in patients with ischemia caused by total acute coronary artery occlusion enables the detection of specific abnormalities—such as changes in waveform time/amplitude, the presence of the omega sign, distortion and folding of RVCG loops, and alterations in loop direction in both the transverse and frontal planes—that are not easily discernible with a standard 12‑lead ECG. This enhanced detection aids in understanding the mechanisms underlying electrocardiographic changes and may assist in managing patients when diagnostic uncertainties arise. MethodsThirty-three patients undergoing elective PTCA were studied before and after acute LAD (16 patients) or RCA (17 patients) occlusion, and their data were processed with new methods based on PBLs, WULs, and RVCGs. Results1. In acute ischemia due to occlusion of the LAD and RCA, the most important current of injury occurs in the right to left axis. This axis is underestimated by the standard 12‑lead ECG and only partially complemented by leads V3R and V4R. 2. The two-dimensional presentation detects a new sign (the omega sign), not detectable in the classic ECG, but almost always present in ischemia. It also allows for an accurate identification of the J point. 3. Ischemic R wave peak delay results in distortion and folding of the RVCG loop and causes displacement of the J point and the ST segment. 4. Wave inversion changes the loop direction in the transverse and frontal plane. ConclusionsPrecordial bipolar leads, weighted unipolar leads, and regional vectorcardiograms provide essential information omitted by the standard 12‑lead ECG.

Lithofacies identification of deep coalbed methane reservoir based on high-resolution seismic inversion

During the exploration and development of deep coalbed methane (CBM), delineating the thickness of coal seam and lithofacies of the roof and floor is one of the major challenging tasks. In past attempts, the prediction methods of these parameters have been limited to the conventional inversion. However, the effect of coal shielding on adjacent reflecting layers restricts the identification of underlying sand effectively by conventional inversion. Also, the depth at which the deep CBM zone is located (1,500–2000 m) produces a significant overlap of P-wave impedance and Vp/Vs of sands and shale which increases classification uncertainty between these two lithofacies. We proposed a new workflow for high-precision quantitative seismic interpretation of deep CBM reservoir. Not only P-wave impedance but also GR is selected as the optimized attributes for lithofacies classification. To reduce the effect of strong reflection of coal seam and identifying thin coal layers, the seismic waveform indication inversion method is used to obtain high-resolution results of P-wave impedance and GR. It uses horizontal changes in seismic waveforms to reflect lithological assemblage characteristics for facies-controlled constraints. Then, Bayesian classification theory is used to achieve three-dimensional lithofacies classification with multi-source data. To improve the continuity and accuracy of the interpreted results, a Markov chain is applied in the Bayesian rule as the spatial prior constraint. A well-associated synthetic test and field data application in Ordos Basin demonstrates the accuracy of the proposed workflow. Compared with conventional inversion, the results of proposed workflow showed higher resolution and accuracy. By providing a new solution for the identification of roof and floor lithofacies of deep CBM reservoir, this workflow aims to contribute to the better exploration and development of deep CBM.

Can the QRS morphology of outflow tract ventricular arrhythmia change when right bundle branch block emerges during sinus rhythm?

QRS morphology can change during ventricular arrhythmias (VAs) with the appearance of bundle branch block (BBB). We retrospectively investigated 195 consecutive patients who underwent an initial ablation of VA. The study inclusion criteria were VAs that were successfully ablated in the outflow tract (OT) and in whom right bundle branch block (RBBB) was induced by catheter manipulation close to the His bundle area during sinus rhythm, before any radiofrequency application. We analyzed the QRS morphology of the VAs with and without RBBB during sinus beats. Twenty-four patients (age 59±17 years, female 14) developed RBBB at some point during their procedure. The successful ablation sites of the VAs were the right ventricular outflow tract (RVOT) in 12 patients, pulmonary artery in one, left coronary cusp in five, right coronary cusp in three, right-left cusp junction in two, and great cardiac vein in two. QRS-morphology change was observed in five (20%) cases. The successful ablation sites in that group were the left coronary cusp in three cases, right coronary cusp in one, and RVOT septum in one. The QRS duration of the VAs increased during RBBB. There are some cases of OT-VAs in which the QRS waveform changes with the appearance of catheter induced RBBB. We need to be aware that when QRS morphology changes during an OT-VA ablation, it does not necessarily mean that the origin or exit of the VA has changed.

SCG variability and spectral energy distribution during normal breathing and breath hold at different lung volumes and airway pressures

Seismocardiographic (SCG) signals are chest wall vibrations induced by cardiac activity and are potentially useful for cardiac monitoring and diagnosis. SCG waveform is observed to vary with respiration, but the mechanism of these changes is poorly understood as alterations in autonomic tone, lung volume, heart location and intrathoracic pressure are all varying during the respiratory cycle. Understanding SCG variability and its sources may help reduce variability and increase SCG clinical utility. This study investigated SCG variability during breath holding (BH) at two different lung volumes (i.e., end inspiration and end expiration) and five airway pressures (i.e., 0, ± 2–4, and ± 15–20 cm H2O). Variability during normal breathing was also studied with and without grouping SCG beats into two clusters of similar waveform morphologies (performed using the K-medoid algorithm in an unsupervised machine learning fashion). The study included 15 healthy subjects (11 Females and 4 males, Age: 21 ± 2 y) where SCG, ECG, and spirometry were simultaneously acquired. SCG waveform variability was calculated at each experimental state (i.e., lung volume and airway pressure). Results showed that breath holding was more effective in reducing the intra-state variability of SCG than clustering normal breathing data. For the BH states, the intra-state variability increased as the airway pressure deviated from zero. The subaudible-to-audible energy ratio of the BH states increased as the airway pressure decreased below zero which may be related to the effect of the intrathoracic pressure on cardiac afterload and blood ejection. When combining the BH waveforms at end inspiration and end expiration states (at the same airway pressures) into one group, the intra-state variability increased, which suggests that the lung volume and associated change in heart location were a significant source of variability. The linear trend between airway pressure and waveform changes was found to be statistically significant for BH at end expiration. To confirm these findings, more studies are needed with a larger number of airway pressure levels and larger number of subjects.

Level-Dependent Subcortical Electroencephalography Responses to Continuous Speech.

The auditory brainstem response (ABR) is a measure of subcortical activity in response to auditory stimuli. The wave V peak of the ABR depends on the stimulus intensity level, and has been widely used for clinical hearing assessment. Conventional methods estimate the ABR average electroencephalography (EEG) responses to short unnatural stimuli such as clicks. Recent work has moved toward more ecologically relevant continuous speech stimuli using linear deconvolution models called temporal response functions (TRFs). Investigating whether the TRF waveform changes with stimulus intensity is a crucial step toward the use of natural speech stimuli for hearing assessments involving subcortical responses. Here, we develop methods to estimate level-dependent subcortical TRFs using EEG data collected from 21 participants listening to continuous speech presented at 4 different intensity levels. We find that level-dependent changes can be detected in the wave V peak of the subcortical TRF for almost all participants, and are consistent with level-dependent changes in click-ABR wave V. We also investigate the most suitable peripheral auditory model to generate predictors for level-dependent subcortical TRFs and find that simple gammatone filterbanks perform the best. Additionally, around 6 min of data may be sufficient for detecting level-dependent effects and wave V peaks above the noise floor for speech segments with higher intensity. Finally, we show a proof-of-concept that level-dependent subcortical TRFs can be detected even for the inherent intensity fluctuations in natural continuous speech.

Analysis and classification of power quality disturbances using variational mode decomposition and hybrid particle swarm optimization

Power quality disturbances (PQD) threaten electrical power systems, especially in distributed generation with renewable energy sources and in smart grids where PQD takes a complex form. Providing accurate information on the status and characteristics of the electrical signal facilitates the identification of practical solutions to this threat. In this paper, a variational mode decomposition (VMD) signal processing tool is proposed to analyze complex PQD. In VMD, the input signal is decomposed into different band-limited intrinsic mode functions (IMF) or non-recursively reconstructed modes. The input signal analysis by VMD, which considers the frequency values and spectral decomposition for each mode, describes the changes in the input waveform, and the IMFs help extract the behavioral patterns of these disturbances. A new hybrid particle swarm optimization-technique for order of preference by similarity to ideal solution (PSO-TOPSIS) algorithm is also proposed to classify the disturbances based on the features extracted from the signals decomposed using VMD. The performance of this method is then extensively validated by using different PQDs (including complex, stationary, and non-stationary (PQDs) and through a comparison with deep learning methods, such as convolutional and recurrent neural networks. Results show that VMD has several advantages over Fourier, wavelet, and Stockwell transforms, such as its lack of any modal aliasing effect, its capability to diagnose disturbances across four noise levels, and its ability to separate harmonics from other events. The proposed VMD in combination with PSO-TOPSIS performs more accurately than the other methods across all noise levels.

A Hybrid RBFNN-SPOA Technique for Multi-Source EV Power System with Single-Switch DC-DC Converter

With a single-switched DC-DC converter, a hybrid approach is proposed for a fast-charging (FC) dc to dc converter with the controller for an electric vehicle charging station (EVCS) that combines solar photovoltaic (PV), fuel cells (FC), and a battery energy storage system (BESS). The proposed hybrid technique integrates a radial-basis-function-neural-network (RBFNN) and a student-psychology-optimization-algorithm (SPOA); commonly known as the RBFNN-SPOA technique. The PV-FC hybrid electric vehicle systems, like controllers, electric vehicle, and the vehicle’s power source, are displayed. The proposed converter architectures have less voltage waveform changes, a low voltage stress on semiconductor power sources, and a high-voltage ratio of conversion. The proposed method is to use FC successfully at battery charge states and varied radiation levels. The FC in the controller of SPOA may change the inputs and outputs depending on the battery state of charge (SoC) and PV irradiation. Similarly, the needed input-torque for the motor is forecasted by the recalling recurrent neural network controller using several manually produced torque signals. The performance of the proposed technique is evaluated in MATLAB and is compared to ant colony optimization (ACO), crow optimization algorithm (COA), and fuzzy logic controller (FLC) approaches. According to the results, the efficiency of the proposed technique is 99.17%.

Information geometry of Wasserstein statistics on shapes and affine deformations

AbstractInformation geometry and Wasserstein geometry are two main structures introduced in a manifold of probability distributions, and they capture its different characteristics. We study characteristics of Wasserstein geometry in the framework of [32] for the affine deformation statistical model, which is a multi-dimensional generalization of the location-scale model. We compare merits and demerits of estimators based on information geometry and Wasserstein geometry. The shape of a probability distribution and its affine deformation are separated in the Wasserstein geometry, showing its robustness against the waveform perturbation in exchange for the loss in Fisher efficiency. We show that the Wasserstein estimator is the moment estimator in the case of the elliptically symmetric affine deformation model. It coincides with the information-geometrical estimator (maximum-likelihood estimator) when the waveform is Gaussian. The role of the Wasserstein efficiency is elucidated in terms of robustness against waveform change.

A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments.

The success of a nanopore experiment relies not only on the quality of the experimental design but also on the performance of the analysis program utilized to decipher the ionic perturbations necessary for understanding the fundamental molecular intricacies. An event extraction framework is developed that leverages parallel computing, efficient memory management, and vectorization, yielding significant performance enhancement. The newly developed abf-ultra-simple function extracts key parameters from the header critical for the operation of open-seek-read-close data loading architecture running on multiple cores. This underpins the swift analysis of large files where an ≈×18 improvement is found for a 100min-long file (≈4.5GB) compared to the more traditional single (cell) array data loading method. The application is benchmarked against five other analysis platforms showcasing significant performance enhancement (>2×-1120×). The integrated provisions for batch analysis enable concurrently analyzing multiple files (vital for high-bandwidth experiments). Furthermore, the application is equipped with multi-level data fitting based on abrupt changes in the event waveform. The application condenses the extracted events to a single binary file improving data portability (e.g., 16GB file with 28182 events reduces to 47.9MB-343× size reduction) and enables a multitude of post-analysis extractions to be done efficiently.

Statistical and sensitivity analysis of ultrasound signals for effective condition monitoring of electro-motors using industrial approach

This paper researches the importance of ultrasound methodology for swiftly detecting faults in electric motors and rotating machines. The primary focus of this research is on the intricate signal processing of ultrasound signals from both faulty and fault-free electro-motors. The principal goal is to conduct a comprehensive statistical investigation into signal factors, examining the effects of defect progression on the factors associated with continuously operating faulty electro-motors. In addition to the statistical analysis, this study explores the envelope-frequency spectrum of the signal under both healthy and defective conditions, employing the envelope method alongside Hilbert transformation. The objective is to thoroughly scrutinize the dynamic changes in ultrasound waveform and envelope spectrum of defective states, considering diverse degrees of defect severity over an extended time span. Moreover, the paper meticulously tracks the trajectory of factor changes over a 40-day operational period of a defective electro-motor. Additionally, the study delves into the sensitivity of the ultrasound method to impulse-wise shocks, which are recurrently observed in ultrasound signals, leading to deviations in certain signal factors from their established healthy thresholds. In response to this challenge, this paper conducts a particular analysis of signal factor sensitivity to impulse-wise noises, identifying robust factors that serve as reliable tools for firm condition monitoring. These identified factors are then presented as invaluable contributors to ensuring the precision and reliability of condition monitoring, especially in the presence of disruptive impulse-wise noises.

Clinical knowledge-based ECG abnormalities detection using dual-view CNN-Transformer and external attention mechanism

Background:Automatic abnormalities detection based on Electrocardiogram (ECG) contributes greatly to early prevention, computer aided diagnosis, and dynamic analysis of cardiovascular diseases. In order to achieve cardiologist-level performance, deep neural networks have been widely utilized to extract abstract feature representations. However, the mechanical stacking of numerous computationally intensive operations makes traditional deep neural networks suffer from inadequate learning, poor interpretability, and high complexity. Method:To address these limitations, a clinical knowledge-based ECG abnormalities detection model using dual-view CNN-Transformer and external attention mechanism is proposed by mimicking the diagnosis of the clinicians. Considering the clinical knowledge that both the detailed waveform changes within a single heartbeat and the global changes throughout the entire recording have complementary roles in abnormalities detection, we presented a dual-view CNN-Transformer to extract and fuse spatial–temporal features from different views. In addition, the locations of the ECG where abnormalities occur provide more information than other areas. Therefore, two external attention mechanisms are designed and added to the corresponding views to help the network learn efficiently. Results:Experiment results on the 9-class dataset show that the proposed model achieves an average F1-score of 0.854±0.01 with a higher interpretability and a lower complexity, outperforming the state-of-the-art model. Conclusions:Combining all these excellent features, this study provides a credible solution for automatic ECG abnormalities detection.

Discovering Love numbers through resonance excitation during extreme mass ratio inspirals

General Relativity predicts that black holes (BHs) do not possess an internal structure and consequently cannot be excited. This leads to a specific prediction about the waveform of gravitational waves (GWs) which they emit during a binary BH inspiral and to the vanishing of their Love numbers. However, if astrophysical BHs do possess an internal structure, their Love numbers would no longer vanish, and they could be excited during an inspiral by the transfer of orbital energy. This would affect the orbital period and lead to an observable imprint on the emitted GWs waveform. The effect is enhanced if one of the binary companions is resonantly excited. We discuss the conditions for resonant excitation of a hypothetical internal structure of BHs and calculate the phase change of the GWs waveform that is induced due to such resonant excitation during intermediate- and extreme-mass-ratio inspirals. We then relate the phase change to the electric quadrupolar Love number of the larger companion, which is resonantly excited by its smaller companion. We discuss the statistical error on measuring the Love number by LISA and show that, because of this phase change, the statistical error is small even for values of the Love number as small as 10−4 for moderate values of the spin parameter. Our results indicate that, for extreme-mass-ratio inspirals with moderate spin parameter, the Love number could be detected by LISA with an accuracy which is higher by up to two orders of magnitude than what can be achieved via tidal deformation effects. Thus, our results indicate that resonant excitation of the central BH during an extreme- or intermediate-mass-ratio inspirals is the most promising effect for putting bounds on, or detecting, non-vanishing tidal Love numbers of BHs.

High Wideband Digital Oscilloscope Design

In most test applications, acquisition and analysis involving simultaneous processing of analog and digital signals. However, the bandwidth of most mainstream digital oscilloscopes is limited to 100 MHz, which is unable to meet the testing needs of high-frequency signals in complex electronic systems [1], and therefore, high-bandwidth digital oscilloscopes have emerged. Based on this background, this paper designs a digital oscilloscope hardware platform with high bandwidth by integrating FPGA and ARM technologies, aiming to meet the rigorous testing requirements of modern electronic systems. The FPGA module is based on the xc7s75fgga676 chip, which is mainly responsible for ADC control, data processing and frequency measurement functions. AM5708 is selected as the ARM module to realize the trigger, time base, amplitude and automatic setting functions of the oscilloscope. In order to ensure the accuracy and fidelity of waveform changes, the Sinc function interpolation method is used. This design further improves the acquisition bandwidth and processing speed on the basis of traditional MSO (Mixed Signal Oscilloscope) oscilloscopes, which is of great significance for the acquisition and processing of high-speed signals.

Doppler ultrasound compared to shear wave elastography for assessment of liver cirrhosis

BackgroundThe progression of liver fibrosis to cirrhosis is a dynamic process necessitating non-invasive evaluation modalities. This study aims to evaluate the ability of Doppler ultrasound studies (DUS) in defining morphological and hemodynamic blood flow changes in the hepatic vasculature coinciding with advanced liver fibrosis.MethodsA prospective study was conducted on 100 patients with liver cirrhosis (F4). All cases underwent liver stiffness (LS) measurement by shear wave elastography (SWE), along with DUS to evaluate the liver texture, splenic size, hepatic artery resistive index (HARI), portal and splenic vein diameters, portal vein velocity (PVV), and hepatic vein waveform (HVV). All measures were assessed concurrently with a highly qualified single operator.ResultsPatients aged 55.5 ± 10.2 years with male predominance (72%). A highly significant correlation was found between LS by SWE and hepatic parenchymal texture, splenic size, portal vein width, and HVV (monophasic and biphasic) (p < 0.001). There were also high significant positive correlations (p < 0.001) between LS and PVV. However, there was no definitive correlation between LS and HARI, as well as splenic vein diameter.ConclusionThe widely available economic Doppler studies including portal vein velocity and hepatic vein waveform changes could be of substantial diagnostic value to liver cirrhosis.Study designProspective cohort study, employing descriptive and analytical statistics.

Development of spontaneous recurrent seizures accompanied with increased rates of interictal spikes and decreased hippocampal delta and theta activities following extended kindling in mice

Interictal epileptiform discharges refer to aberrant brain electrographic signals between seizures and feature intermittent interictal spikes (ISs), sharp waves, and/or abnormal rhythms. Recognition of these epileptiform activities by electroencephalographic (EEG) examinations greatly aids epilepsy diagnosis and localization of the seizure onset zone. ISs are a major form of interictal epileptiform discharges recognized in animal models of epilepsy. Progressive changes in IS waveforms, IS rates, and/or associated fast ripple oscillations have been shown to precede the development of spontaneous recurrent seizures (SRS) in various animal models. IS expressions in the kindling model of epilepsy have been demonstrated but IS changes during the course of SRS development in extended kindled animals remain to be detailed. We hence addressed this issue using a mouse model of kindling-induced SRS. Adult C57 black mice received twice daily hippocampal stimulations until SRS occurrence, with 24-h EEG monitoring performed following 50, 80, and ≥ 100 stimulations and after observation of SRS. In the stimulated hippocampus, increases in spontaneous ISs rates, but not in IS waveforms nor IS-associated fast ripples, along with decreased frequencies of hippocampal delta and theta rhythms, were observed before SRS onset. Comparable increases in IS rates were further observed in the unstimulated hippocampus, piriform cortex, and entorhinal cortex, but not in the unstimulated parietal cortex and dorsomedial thalamus. These data provide original evidence suggesting that increases in hippocampal IS rates, together with reductions in hippocampal delta and theta rhythms are closely associated with development of SRS in a rodent kindling model.

Inner core backtracking by seismic waveform change reversals

The solid inner core, suspended within the liquid outer core and anchored by gravity, has been inferred to rotate relative to the surface of Earth or change over years to decades based on changes in seismograms from repeating earthquakes and explosions1,2. It has a rich inner structure3–6 and influences the pattern of outer core convection and therefore Earth’s magnetic field. Here we compile 143 distinct pairs of repeating earthquakes, many within 16 multiplets, built from 121 earthquakes between 1991 and 2023 in the South Sandwich Islands. We analyse their inner-core-penetrating PKIKP waves recorded on the medium-aperture arrays in northern North America. We document that many multiplets exhibit waveforms that change and then revert at later times to match earlier events. The matching waveforms reveal times at which the inner core re-occupies the same position, relative to the mantle, as it did at some time in the past. The pattern of matches, together with previous studies, demonstrates that the inner core gradually super-rotated from 2003 to 2008, and then from 2008 to 2023 sub-rotated two to three times more slowly back through the same path. These matches enable precise and unambiguous tracking of inner core progression and regression. The resolved different rates of forward and backward motion suggest that new models will be necessary for the dynamics between the inner core, outer core and mantle.

Redefined giant somatosensory evoked potentials: Evoked epileptic complexes of excitatory and inhibitory components

ObjectiveGiant somatosensory evoked potentials (SEPs) are observed in patients with cortical myoclonus. Short-latency components (SLC), are regarded as evoked epileptic activities or paroxysmal depolarization shifts (PDSs). This study aimed to reveal the electrophysiological significance of the middle-latency component (MLC) P50 of the SEPs. MethodsTwenty-two patients with cortical myoclonus having giant SEPs (patient group) and 15 healthy controls were included in this study. Waveform changes in SEPs before and after perampanel (PER) treatment were evaluated in the patient group. The wide range, time–frequency properties underlying the waveforms were compared between the groups. ResultsAfter PER treatment, SLC was prolonged and positively correlated with PER concentration, whereas MLC showed no correlation with PER concentration. Time–frequency analysis showed a power increase (156 Hz in all patients, 624 Hz in benign adult familial myoclonus epilepsy patients) underlying SLC and a power decrease (156 Hz, 624 Hz) underlying MLC in the patient group. ConclusionsThe high-frequency power increase in SLCs and decrease in MLCs clearly reflected PDS and subsequent hyperpolarization, respectively. This relationship was similar to that of interictal epileptiform discharges, suggesting that giant SEPs evoke epileptic complexes of excitatory and inhibitory components. SignificanceMLCs of giant SEPs reflected inhibitory components.