Response Waveform Research Articles

Reducing artifacts in electrically evoked auditory potentials (eAEP)

Abstract Introduction: In auditory evoked potentials (= AEP), auditory brainstem response (= ABR) is the gold standard for objective analysis of the auditory pathway using acoustical stimulators like headphones during hearing screening and patient assessment. Where no response can be recorded, electrical stimulation can be used in pre-, intra-, or postoperative scenarios to confirm the excitability of the auditory nerve (= eAEP). In eAEP, the recording of brainstem response (= eABR) is mostly the method of choice. Sometimes in eABR, the stimulation artifact can not only harden but even prevent the detection of the response waveforms. Methods: Many factors can influence the stimulation artifact when recording an eAEP. Neurological recording systems are recommended as well as some approved AEP systems traditionally used in ENT for amplification to avoid large stimulation artifacts in the response waveforms, but able to recover in <1 ms from saturation. High stimulation rates (~90 Hz) can lead to overlapping of the response waveforms with the following stimulation artifact. In preoperative eAEP, stimulation artifacts are often high due to extra-cochlear stimulation. In intra- and postoperative eAEP, the artifact is lower and mostly similar across patients. Results: The recommended recording systems show low stimulation artifacts in almost any case for intra- and postoperative eABR. In contrast, later responses, like electrically evoked auditory middle-latency response (= eAMLR) and electrically evoked auditory late response (= eALR), are less affected by artifacts. Conclusion: The stimulation artifact can be a problematic factor. Depending on the EP system, intra- and postoperative eABR can be challenging when trying to record the response waves. Where possible, clinicians should think about eAMLR or eALR for results with less stimulation artifact. Finally, the most important factor remains using a recording system that can handle the stimulation artifact. We do not recommend recording devices with high artifacts for preoperative eABR. For intra- and postoperative eABR, the recommended systems can prevent challenging recording.

Modeling method of photovoltaic power generation grid connection based on particle swarm optimization neural network

Aiming at the complex structure, numerous equipment, intricate control and protection logic, as well as the existence of numerous unmodeled dynamics and black-box device models in photovoltaic (PV) grid-connected systems, a modeling method based on Particle Swarm Optimization Neural Network (PSO-NN) is proposed to address the inability of pure mechanism models to accurately simulate their operational dynamics. Utilizing the differences in active power response waveforms under Voltage-Frequency (Vf) control, Power-Reactive Power (PQ) control, and Droop control as criteria for control strategy identification, a PSO-NN model is constructed for PV grid-connected systems, with inputs comprising temperature, humidity, light intensity, voltage, and frequency disturbances, and outputs being active and reactive power. To validate the model's effectiveness, a PV grid-connected system model is built in a self-developed simulation software and connected to an IEEE 14-bus distribution network for simulation verification. The results demonstrate that the proposed PV grid-connected model can effectively identify the types of Vf control, PQ control, and Droop control strategies, and accurately reflect the dynamic response characteristics of active and reactive power under various voltage and frequency disturbances.

Lamb wave based damage imaging in variable thickness plates

The researches on Lamb wave based damage localization for constant thickness plates have been widely investigated in the field of structural health monitoring, while the issue of Lamb wave based damage imaging in variable thickness plates is rarely addressed. This is because the Lamb wave propagation characteristics in plates with varying thickness is more complex than that with constant thickness. In this study, a Lamb wave based damage imaging method using a sensor array is proposed for damage localization in variable thickness plates. In the process of point-by-point imaging within the interested imaging area, a signal back-propagation operator based on a Lamb wave propagation model can deal with the problem of dispersion compensation for Lamb waves propagating in variable thickness plates. The Lamb wave propagation model, which is based on the assumption that Lamb waves propagating in a variable thickness plate can be seen as that propagating in a sequence of short and constant thickness plates with different thicknesses, can predict Lamb wave response waveform in variable thickness plates. Numerical simulations and experiments on aluminum plates with linearly varying thicknesses are implemented, and the imaging results validate the effectiveness of the proposed method.

PI gain tuning for pressure-based MFCs with Gaussian mixture model

A vast number of mass flow controllers (MFCs) are used in semiconductor industry. For the stable supply, an efficient production method of MFC is required. The gain tuning of the proportional-integral (PI) control to realize a setting flow rate is essential for efficient mass production. The gains are tuned to meet the specifications required for evaluation indices of response time and overshoot amount in a step response waveform. The tuning is complicated especially for the case of pressure-based MFCs. In this paper, we propose a simple method for the PI gain tuning using the Gaussian mixture model (GMM) and the direct inverse analysis applicable to the pressure-based MFCs’ production. The relationship between the gains and evaluation indices for a standard unit of the MFC is modeled as the GMM. The direct inverse analysis calculates the difference between the standard and a test unit. Under the assumption that the difference can be compensated by a simple shift, gains likely to meet the specifications for the test unit are searched. We applied the method to seven test units. The result showed that the gains of all the test units were tuned within only a few iterations whose numbers were much less than the conventional manual tuning method, and there was no untunable unit.

Asphalt binder characterization using waveform-based viscoelastic measures and time-temperature superposition principle under large strains

In the United States, currently peak-to-peak oscillatory shear stress-strain data is used in the asphalt binder characterization protocols. This practice necessitates the measurement, usage, and reporting of only peak responses for material characterization and specification even though a sinusoidal waveform is applied. However, in this paper it is argued that with the proliferation of modified binding systems, it is critical to capture and examine the complete waveform data when subjected to both small and large strains. This approach will help properly differentiating between materials. In this study, four modified binders were tested at small and large strain levels while recording the complete response waveform. The recorded waveform data was then used to examine the linear and nonlinear viscoelastic characteristics of these binders. The orthogonal stress decomposition technique was used to delineate elastic and viscous contributions in the nonlinear regime. This study examined the time-temperature dependency under both linear and nonlinear regimes. The time-temperature shift factors obtained from linear regime were effectively applied in the nonlinear regime. Further, all the tested binders showed strain stiffening and shear thinning behavior under most of the testing conditions. This study provides useful insights about the material behavior under nonlinear regime and can serve as a benchmark for further investigation of nonlinear viscoelastic behavior of asphalt binders.

An Open-Source Deep Learning-Based GUI Toolbox For Automated Auditory Brainstem Response Analyses (ABRA).

In this paper, we introduce a new, open-source software developed in Python for analyzing Auditory Brainstem Response (ABR) waveforms. ABRs are a far-field recording of synchronous neural activity generated by the auditory fibers in the ear in response to sound, and used to study acoustic neural information traveling along the ascending auditory pathway. Common ABR data analysis practices are subject to human interpretation and are labor-intensive, requiring manual annotations and visual estimation of hearing thresholds. The proposed new Auditory Brainstem Response Analyzer (ABRA) software is designed to facilitate the analysis of ABRs by supporting batch data import/export, waveform visualization, and statistical analysis. Techniques implemented in this software include algorithmic peak finding, threshold estimation, latency estimation, time warping for curve alignment, and 3D plotting of ABR waveforms over stimulus frequencies and decibels. The excellent performance on a large dataset of ABR collected from three labs in the field of hearing research that use different experimental recording settings illustrates the efficacy, flexibility, and wide utility of ABRA.

Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework.

Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ABR waveforms in participants with varying ages and hearing levels. The research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants. The research was conducted at a tertiary academic medical center, specifically at the Clinical Audiology Center of Tsinghua Chang Gung Hospital (Beijing, China). Data from 100 participants were collected and categorized into four groups based on age and hearing level. Features from both time-domain and frequency-domain ABR signals were extracted and combined with demographic factors, such as age, sex, pure-tone thresholds, stimulus intensity, and original signal sequences to generate feature vectors. An enhanced Wide&Deep model was utilized, incorporating the Light-multi-layer perceptron (MLP) model to train the recognition of ABR waveforms. The recognition accuracy (ACC) of each model was calculated for the overall data set and each group. The ACC rates of the Light-MLP model were 97.8%, 97.2%, 93.8%, and 92.0% for Groups 1 to 4, respectively, with a weighted average ACC rate of 95.4%. For the Wide&Deep model, the ACC rates were 93.4%, 90.8%, 92.0%, and 88.3% for Groups 1 to 4, respectively, with a weighted average ACC rate of 91.0%. Both the Light-MLP model and the Wide&Deep model demonstrated excellent ACC in automatic recognition of ABR waveforms across participants with diverse ages and hearing levels. While the Wide&Deep model's performance was slightly poorer than that of the Light-MLP model, particularly due to the limited sample size, it is anticipated that with an expanded data set, the performance of Wide&Deep model may be further improved.

Nonlinear dynamic wave characteristics of optical soliton solutions in ion-acoustic wave

Traveling wave solutions are utilized to depict reaction-diffusion and analyze electrical signal transmission and propagation in space-time nonlinear fractional order partial differential equations like the space-time fractional Telegraph and Kolmogorov-Petrovsky Piskunov equations, and that are used in physical science to model combustion, biological research to model nerve impulse propagation, chemical dynamics to model concentration in order wave propagation, and plasma to model the progression of a set of duffing oscillators. In this study, the new generalized (G′/G)−expansion technique was employed to construct some novel and more universal closed-form traveling wave solutions in the sense of conformable derivatives which explain the above-stated phenomena properly. By utilizing complex fractional transformation, the ordinary differential equations are generated from fractional order differential equations. The recommended technique allowed us to produce some dynamical wave patterns of kink, single soliton, compacton, periodic shape, multiple periodic waves, anti-kink, and other structures are developed, which are shown using 3D plots and contour plots to illustrate the physical layout clearly. The traveling waveform responses can be defined in terms of functions based on trigonometry, hyperbolic operations, and rational functions and that are quick, flexible, and simple to reproduce. Furthermore, the obtained closed-form solutions for nonlinear fractional evolution equations make stability analysis and accuracy comparison amongst numerical solvers easier, which asserts that the new generalized (G′/G)−expansion technique is one of the most proficient and effective approaches.

Displacement response waveform restoration of simply supported bridge during train passage using measured acceleration integration based on linear vibration theory

This study proposes a method for restoring the displacement waveform during train passage using the acceleration integration based on linear vibration theory and noise cancellation techniques. This method replaces the low-frequency region affected by noise with a linear theoretical solution; however, this method uses the actual measurement data around the natural frequency, where nonlinearities (such as concrete member cracks and the train–bridge interaction) can be observed. The method identifies only the ratio of single axle weight to modal stiffness. Numerical experiments clarify that the method can estimate the maximum displacement of a bridge from acceleration with an error of approximately 5% or less. Improved prediction accuracy can be achieved using the proposed noise cancellation technique not only in the high-speed range but also into the lower-speed ranges as well. Furthermore, the proposed method is applied to acceleration monitoring data, and results show that the displacement waveform can be restored from the acceleration waveform.

Seismic displacement response analysis of Friction Pendulum Bearing under friction coupling and collision effects

Friction Pendulum Bearing (FPB) with shearing keys will exhibit friction-coupling effect and collision phenomenon subjected to horizontal orthogonal ground motions. This paper establishes a numerical analysis model of FPB with four shear keys and viscous damping, considering the impact of the friction coupling effect and collision between the FPB and the shear keys on its displacement response. The influence of parameters of FPB on seismic displacement response with considering collision effect under horizontal orthogonal ground motions was studied. The results suggest that adjustments in damping and equivalent stiffness exclusively impact the amplitude of the displacement response, maintaining the response waveform. Conversely, variations in friction significantly alter the response waveform, as friction induces changes in the natural frequency of FPB. A heightened collision impact is noted with reduced spring stiffness. The displacement response is particularly amplified when the collision occurs at the vicinity of peak displacement in the time-history response of FPB. When the restoring stiffness and Peak Ground Acceleration (PGA) are low, the friction coupling effect significantly reduces the amplification of displacement response caused by collision effects. Viscous damping can considerably reduce the impact of collisions on the peak displacement of FPB and prevent the collision effects from noticeably amplifying or diminishing the peak displacement of FPB.

The MBO2/FAP58 heterodimer stabilizes assembly of inner arm dynein b and reveals axoneme asymmetries involved in ciliary waveform.

Cilia generate three-dimensional waveforms required for cell motility and transport of fluid, mucus, and particles over the cell surface. This movement is driven by multiple dynein motors attached to nine outer doublet microtubules that form the axoneme. The outer and inner arm dyneins are organized into 96-nm repeats tandemly arrayed along the length of the doublets. Motility is regulated in part by projections from the two central pair microtubules that contact radial spokes located near the base of the inner dynein arms in each repeat. Although much is known about the structures and protein complexes within the axoneme, many questions remain about the regulatory mechanisms that allow the cilia to modify their waveforms in response to internal or external stimuli. Here, we used Chlamydomonas mbo (move backwards only) mutants with altered waveforms to identify at least two conserved proteins, MBO2/CCDC146 and FAP58/CCDC147, that form part of a L-shaped structure that varies between doublet microtubules. Comparative proteomics identified additional missing proteins that are altered in other motility mutants, revealing overlapping protein defects. Cryo-electron tomography and epitope tagging revealed that the L-shaped, MBO2/FAP58 structure interconnects inner dynein arms with multiple regulatory complexes, consistent with its function in modifying the ciliary waveform.

Comparative study of event-related potential responses within syllables of intra and inter phoneme classes

The present study was carried out to assess the signature neural dynamics as evinced through event-related potential (ERP) responses of the P300 waveform across different inter and intra phoneme consonant classes and to quantify the differential neural dynamical response in real-time. The ERP responses to non-sense three-syllable consonantvowel transitions CVCVCV non-words were recorded on 26 healthy individuals of either sex with an age group of 18–25 years. The first syllable of the non-sense word was always fixed, and the ERP responses of the middle syllable were recorded at the phoneme level, and the ERP responses elicited by the middle syllable were evaluated. The results of the present study documented that different consonant phoneme classes (plosives, fricatives, affricatives, liquids, and nasals) elicited differential neural dynamical responses as observed through the P300 waveform response, exhibiting a significant difference in P300 waveform amplitude across the fricatives vs. nasals experimental design.

Experimental and numerical approach to assess the dynamic performance of an inductive ignition system

This paper envisages studying the features of a conventional inductive ignition system along with its MOSFET and IGBT-based transistorized modifications. The influence of mechanical contact breaker dynamics and ignition coil characteristics on the current and voltage waveforms of the primary and secondary circuits of these studied ignition systems are mathematically and experimentally exposed. The investigation is considered necessary prior to attempting subsequent modeling and diagnostics procedures on the current-voltage performance characteristics of conventional and transistorized ignition systems. The work has demanded the development of an experimental setup based on a basic modifiable ignition system mockup and an instrumentation system to measure and analyze the voltage-current parameters of inductive ignition systems. The paper describes the design details of such instrumentation system, presents mechanical and electrical models for contact breaker and ignition circuits, then simulated to obtain base free-run response waveforms and electrical continuity behavior of the contact. A test of frequency response of the ignition coil provided additional input to the model. An experimental test of continuity of the contact, in agreement with its model, shed light on the actual excitation of the primary coil. The work comments on a sample of the registered current and voltage waveforms in primary and secondary coil windings of the ignition system at atmospheric conditions. Comparisons of waveforms and energy for mechanical contact, MOSFET and IGBT switches are made to establish them as a reference for future tests.

A Time-Saving Alternative to "Peak-Picking" Algorithms: A Gaussian Mixture Model Feature Extraction Technique for the Neurodiagnostic Auditory Brainstem Response.

The accurate and efficient analysis of neurodiagnostic auditory brainstem responses (ABR) plays a critical role in assessing auditory pathway function in human and animal research and in clinical diagnosis. Traditional analysis of the neurodiagnostic ABR analysis involves visual inspection of the waveform and manually marking peaks and troughs. Visual inspection is a tedious and time-consuming task, especially in research where there may be hundreds or thousands of waveforms to analyze. "Peak-picking" algorithms have made this task faster; however, they are prone to the same errors as visual inspection. A Gaussian mixture model-based feature extraction technique (GMM-FET) is a descriptive model of ABR morphology and an alternative to peak-picking algorithms. The GMM-FET is capable of modeling multiple waves and accounting for wave interactions, compared with other template-matching approaches that fit single waves. The present study is a secondary analysis applying the GMM-FET to 321 ABRs from adult humans from 2 datasets using different stimuli and recording parameters. Goodness-of-fit of the GMM-FET to waves I and V and surrounding waves, that is, the summating potential and waves IV and VI, was assessed, and latency and amplitude estimations by the GMM-FET were compared with estimations from visual inspection. The GMM-FET had a similar success rate to visual inspection in extracting peak latency and amplitude, and there was low RMS error and high intraclass correlation between the model and response waveform. Mean peak latency differences between the GMM-FET and visual inspection were small, suggesting the two methods chose the same peak in the majority of waveforms. The GMM-FET estimated wave I amplitudes within 0.12 µV of visual inspection, but estimated larger wave V amplitudes than visual inspection. The results suggest the GMM-FET is an appropriate method for extracting peak latencies and amplitudes for neurodiagnostic analysis of ABR waves I and V.

Estimating intracranial parameters using an inverse mathematical model with viscoelastic elements that closely predicts complex ICP morphologies

The quantitative relationship between arterial blood pressure (ABP) and intracranial pressure (ICP) waveforms has not been adequately explained. We hypothesized that the ICP waveform results from interferences between propagating and reflected pressure waves occurring in the cranium following the initiating arterial waveform. To demonstrate cranial effects on interferences between waves and generation of an ICP waveform morphology, we modified our previously reported mathematical model to include viscoelastic elements that affect propagation velocity. Using patient data, we implemented an inverse model methodology to generate simulated ICP waveforms in response to given ABP waveforms. We used an open database of traumatic brain injury patients and studied 65 pairs of ICP and ABP waveforms from 13 patients (five pairs from each). Incorporating viscoelastic elements into the model resulted in model-generated ICP waveforms that very closely resembled the measured waveforms with a 16-fold increase in similarity index relative to the model with only pure elasticity elements. The mean similarity index for the pure elasticity model was 0.06 ± 0.12 SD, compared to 0.96 ± 0.28 SD for the model with viscoelastic components. The normalized root mean squared error (NRMSE) improved substantially for the model with viscoelastic elements compared to the model with pure elastic elements (NRMSE of 2.09% ± 0.62 vs. 15.2% ± 4.8, respectively). The ability of the model to generate complex ICP waveforms indicates that the model may indeed reflect intracranial dynamics. Our results suggest that the model may allow the estimation of intracranial biomechanical parameters with potential clinical significance. It represents a first step in the estimation of inaccessible intracranial parameters.

Retrieval of Tree Height Percentiles over Rugged Mountain Areas via Target Response Waveform of Satellite Lidar

The retrieval of tree height percentiles from satellite lidar waveforms observed over mountainous areas is greatly challenging due to the broadening and overlapping of the ground return and vegetation return. To accurately represent the shape distributions of the vegetation and ground returns, the target response waveform (TRW) is resolved using a Richardson–Lucy deconvolution algorithm with adaptive iteration. Meanwhile, the ground return is identified as the TRW component within a 4.6 m ground signal extent above the end point of the TRW. Based on the cumulative TRW distribution, the height metrics of the energy percentiles of 25%, 50%, 75%, and 95% are determined using their vertical distances relative to the ground elevation in this study. To validate the proposed algorithm, we select the received waveforms of the Global Ecosystem Dynamics Investigation (GEDI) lidar over the Pahvant Mountains of central Utah, USA. The results reveal that the resolved TRWs closely resemble the actual target response waveforms from the coincident airborne lidar data, with the mean values of the coefficient of correlation, total bias, and root-mean-square error (RMSE) taking values of 0.92, 0.0813, and 0.0016, respectively. In addition, the accuracies of the derived height percentiles from the proposed algorithm are greatly improved compared with the conventional Gaussian decomposition method and the slope-adaptive waveform metrics method. The mean bias and RMSE values decrease by the mean values of 1.68 m and 2.32 m and 1.96 m and 2.72 m, respectively. This demonstrates that the proposed algorithm can eliminate the broadening and overlapping of the ground return and vegetation return and presents good potential in the extraction of forest structure parameters over rugged mountainous areas.

Inter-rater reliability of auditory brainstem response waveforms elicited by complex stimuli

Abstract Objective: The objective of this study was to assess the inter-rater reliability of peak marking for the frequency following the response portion of auditory brainstem response (ABR) waveforms elicited by complex stimuli. Materials and Methods: ABR testing was carried out on 30 adult participants with normal hearing, which generated a data set of 60 click-evoked waveforms and 60/da/-evoked waveforms. Three audiologists marked peaks for both click and/da/evoked ABR waveform data, derived from 30 participants aged 18–30 years. To assess inter-rater reliability, a two-way, random effects, single-measure model was used to calculate the intraclass correlation coefficients (ICCs). Results: The ICCs were ≥0.89 for peaks I, III, and V of the click-evoked waveforms. For the/da/evoked waveforms, the ICCs were rated good to high (≥0.83) for peaks V, A, E, F, and O, fair (0.75) for peak D but poor (≥0.45) for peak C. Conclusion: Investigations of reliability of waveform marking should be undertaken before using complex stimuli for ABR testing to prevent premature adoption of screening or diagnostic tools.

Importance of changes in abnormal muscle responses during microvascular decompression for hemifacial spasm

ObjectiveTo determine if compression sites of the facial nerve correlate with immediate postoperative outcomes in patients with hemifacial spasm (HFS), and if changes in the waveform of abnormal muscle response (AMR) during microvascular decompression (MVD) for HFS can predict the postoperative course. MethodsIn this retrospective review, we evaluated 50 patients with HFS who underwent AMR monitoring during MVD. The ratios of amplitude and duration of AMR waveforms were computed by comparing baseline with final examinations. Vascular compression sites were categorized into four portions of the facial nerve. Postoperatively, we classified patients into two groups based on symptom relief as those whose symptoms disappeared immediately (DI group), and those whose symptoms disappeared gradually (DG group). ResultsThe compression sites significantly correlated with postoperative outcomes at discharge (p < 0.001) but not with outcomes after 6 months of MVD. Lower duration ratios of AMRs from the mentalis muscle were significantly associated with an increased chance of classification into the DI group based on the results of multivariate logistic regression analysis (p = 0.017). ConclusionsRelationship between compression sites and immediate outcomes could provide useful information to surgeons for predicting if symptoms will resolve over long term. Moreover, changes in AMRs recorded from the mentalis muscle could predict the postoperative course of HFS. SignificanceThese findings can help surgeons evaluate the changes in AMR amplitude and duration during MVD for HFS.

Comparison of retinal parameters between rhesus and cynomolgus macaques.

Nonhuman primates are important research models for basic vision research, preclinical pathogenesis, and treatment studies due to strong similarities in retinal structure and function with humans. We compared retinal parameters between 10 healthy normal rhesus macaques (Macaca mulatta) and 10 cynomolgus macaques (Macaca fascicularis) by optical coherence tomography and electroretinography. The Heidelberg Spectralis® HRA+OCT and Roland multifocal electrophysiometer were used to analyze retinal morphology, multifocal electroretinograms (mfERGs), and full-field electroretinograms (ff-ERGs). Mean retinal thickness was lowest in the central fovea of macaques and did not differ significantly between species, but the retinal thicknesses of the nerve fiber ganglion cell layer and the inner plexiform layer were significantly different. The amplitude density of the N1 wave was lower in rhesus macaques than in cynomolgus macaques in ring and quadrant areas. Dark-adapted 3.0 oscillatory potentials (reflection of amacrine cell activity) and light-adapted 30-hz flicker ERG (a sensitive cone-pathway-driven response) waveforms of the ff-ERG were similar in both species, while the times to peaks in dark-adapted 0.01 ERG (the rod-driven response of bipolar cells) and dark-adapted 3.0 ERG (combined rod and cone system responses) as well as the implicit times of the a- and b-waves in light-adapted 3.0 ERG (the single-flash cone response) were substantially different. This study provides normative retinal parameters for nonhuman primate research on basic and clinical ophthalmology, as well as a reference for researchers in the appropriate selection of rhesus or cynomolgus macaques as models for ophthalmology studies.

Effect of Loading Frequency on the Fretting-Corrosion Degradation of the Stem-Head and Stem-Cement Interfaces in Hip Implants

Abstract Degradation by fretting-corrosion of the stem-head interface is considered the main source of the release of metal ions and debris, leading to adverse tissue reactions at the cemented total hip arthroplasty. The aim of this study was to investigate the effect of loading frequency on the fretting-corrosion degradation at the stem-head and stem-cement interfaces through electrochemical measurements during the benchtop testing. Different loading frequency conditions were investigated with the same number of cycles (1,000 cycles) to calculate the fretting current and Faraday mass loss. For both interfaces, there was a biphasic waveform response of the fretting current to the load applied, at 1 Hz, almost with phase opposition. An increase in the loading frequency (1 to 30 Hz) generated a higher fretting current for the stem-head (2.97 ± 2.04 μA to 7.66 ± 2.46 μA) and stem-cement (7.62 ± 2.26 μA to 25.48 ± 5.35 μA) interfaces. However, an increase in the loading frequency (1 to 30 Hz) reduced the Faraday mass loss of the stem-head (0.79 ± 0.54 μg to 0.07 ± 0.02 μg) and stem-cement (2.02 ± 0.62 μg to 0.32 ± 0.12 μg) interfaces because of a reduction in the overall testing time. Thus, there is a compromise relation between the fretting current and the Faraday mass loss, which must be considered when setting the loading frequencies in the fretting-corrosion benchtop testing of hip implants.