High Frequency Components Research Articles

Artificial intelligence to reduce artifact in cardiac electrophysiological signals

Abstract Background/Introduction Signals in Electrophysiology cases are often noisy despite laboratory shielding and filtering, and current noise-reduction methods are suboptimal. Template matching can identify a “nearest type” of electrogram, but libraries of signal shapes may be unavailable. Beat averaging can reduce noise but obscures beat-to-beat variations and is not optimal to analyze dynamically changing signals, such as when moving a catheter in the heart. Smoothing reduces noise yet blurs high frequency components. Purpose We set out to test if machine learned autoencoders could reduce noise in single beats without requiring massive training data or beat libraries. Specifically, we hypothesised that noisy electrograms in small datasets of atrial signals could be de-noised using an encoder-decoder neural network (NN) using transfer learning of machines trained to recognize key features in larger datasets of related signals. Methods We applied NN to monophasic action potentials (MAPs), because they have visually verifiable shapes. The NN was first trained to reconstruct 5706 left and right ventricular MAPs in 42 patients (67±13y; Fig. 1A). Transfer learning was then used to apply the NN to a much smaller dataset of 641 atrial MAPs in 21 patients (67±5y, 13 women; Fig. 1B, D, F). Results NN reconstructed atrial MAPs with a Pearson correlation of 0.87±0.11. After fine-tuning, NN reconstruction accuracy improved dramatically (Pearson 0.99±0.01; p<0.001). In Fig. 1B–G the NN learned key MAP features (upstroke, triangular descent, terminus) and thus could eliminate ventricular artifact and electrical circuit noise without specific training or manual annotation. Conclusion Machine learned autoencoders are a novel and powerful approach to de-noise electrophysiological signals in a dynamic, beat-to-beat fashion. The ability to learn fundamental signal features from models trained in large datasets, and apply them via transfer learning to small datasets in different heart chambers may have wide ranging applications for automated signal annotation, mapping and ablation. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH

Fault Line Selection with Correlation Analysis of Zero Sequence Current and Voltage under High-Low Frequency

When a single-phase ground fault occurs near the zero or maximum point, the fault line selection of resonance grounding system is not sensitive enough. After analyzing the transient current and line impedance characteristics of zero sequence network, and making full use of the decaying DC component, power frequency component and high frequency component of the transient zero sequence current of the feeder, a fault line selection method based on correlation analysis of zero sequence current and bus zero sequence voltage derivative in high and low frequency band is proposed. The effectiveness of the method is verified by the examples from the aspects of fault closing angle, transition resistance.

Exploring the Effect of High-frequency Components in GANs Training

Generative Adversarial Networks (GANs) have the ability to generate images that are visually indistinguishable from real images. However, recent studies have revealed that generated and real images share significant differences in the frequency domain. In this article, we argue that the frequency gap is caused by the high-frequency sensitivity of the discriminator. According to our observation, during the training of most GANs, severe high-frequency differences make the discriminator focus on high-frequency components excessively, which hinders the generator from fitting the low-frequency components that are important for learning images’ content. Then, we propose two simple yet effective image pre-processing operations in the frequency domain for eliminating the side effects caused by high-frequency differences in GANs training: High-frequency Confusion (HFC) and High-frequency Filter (HFF). The proposed operations are general and can be applied to most existing GANs at a fraction of the cost. The advanced performance of the proposed operations is verified on multiple loss functions, network architectures, and datasets. Specifically, the proposed HFF achieves significant improvements of 42.5% FID on CelebA (128*128) unconditional generation based on SNGAN, 30.2% FID on CelebA unconditional generation based on SSGAN, and 69.3% FID on CelebA unconditional generation based on InfoMAXGAN. Furthermore, we also adopt HFF as the first attempt at data augmentation in the frequency domain for contrastive learning, achieving state-of-the-art performance on unconditional generation. Code is available athttps://github.com/iceli1007/HFC-and-HFF.

Cardiovascular autonomic regulation in fighter pilots: Lessons from active standing tests.

Fighter pilots (FP) are exposed to flight accelerations and stressful situations that defy cardiovascular control during and after flight. FP presents a smaller adjustment in sympatho-vagal balance during tilt test after flight compared to baseline, suggesting a huge impact of flight on autonomic modulation to the heart. We undertake to test the hypothesis that FP will have a smaller vagal reentrance and lower sympathetic withdrawal during the recovery at the supine position after a prolonged active standing test that mimics flight hemodynamic demands. Twenty-one military personnel (20-34 years old), composed of 9 FP and 12 non-pilots (NP) matched by age, V̉O2max and body mass index were enroled in the experimental protocol. R-R intervals were continuously recorded in the supine position for 15 min (SUPbaseline ), during the prolonged active standing test (45 min) windowed in six 5 mintime frames (from ORT1 to ORT6), and a recovery period in the supine position for 15 min (SUPrecovery ). Heart rate variability was performed by spectral analysis to obtain the normalized low (LFn) and high (HFn) frequency components. The variation (Δ) from baseline (Δ = ORT6 - SUPbaseline ) and from recovery (Δ = SUPrecovery -ORT6) periods were calculated. FP had a smaller ΔLFn (sympathetic mediated) and ΔHFn (vagal meditated) during recovery after active standing as compared to NP. Both groups showed similar changes in ΔLFn and ΔHFn during orthostatic stress compared to baseline, with no differences over time. Therefore, FP show a smaller vagal reentrance and a lower sympathetic reduction during recovery at supine following active standing compared to NP.

To the interpretation of frequency components of the heart rate variability

Assessing the sympathetic-vagal balance is an important task for predicting shifts in the functioning of the body when adapting to changing external conditions, as well as aging, various pathological processes and therapy. Today, the method of analyzing the spectrum of heart rate variability (HRV) for determining the sympathetic-vagal balance due to its simplicity and non-invasiveness has become widespread both in clinical practice and in experimental studies. The article analyzes the results of our own and literary studies of influence of various effects on hemodynamic parameters, the level of circulating catecholamines, sympathetic nervous activity, the baroreceptor reflex, and also on the components of the HRV spectrum (LF (low frequency), HF (high frequency) and LF/HF). Often different authors under the same impact register different changes. Reasons for this include different doses of drugs, nature/strength of exposure (for stress and exercise), time between drug administration and registration. In addition, the effect of some drugs can be enhanced in the case of pathology, such as hypertension. Based on the comparison of the vectors of changes in the studied parameters, we concluded that, using the frequency analysis of HRV, it is necessary to draw a conclusion about the sympatho-vagal balance with great care due to the ambiguous nature of LF component, but HF component gives information of the tone of the vagus with sufficient accuracy.

Accurately Stable Q-Compensated Reverse-Time Migration Scheme for Heterogeneous Viscoelastic Media

The development of multi-component seismic acquisition technology creates new possibilities for the high-precision imaging of complex media. Compared to the scalar acoustic wave equation, the elastic wave equation takes the information of P-waves, S-waves, and converted waves into account simultaneously, enabling accurate description of actual seismic propagation. However, inherent attenuation is one of the important factors that restricts multi-component high-precision migration imaging. Its influence is mainly reflected in the following three ways: first, the attenuation of the amplitude energy makes the deep structure display unclear; second, phase distortion introduces errors to the positioning of underground structures; and third, the loss of high frequency components reduces imaging resolution. Therefore, it is crucial to fully consider the absorption and attenuation characteristics of the real Earth during seismic modeling and imaging. This paper aims to develop an accurate attenuation compensation reverse-time migration scheme for complex heterogeneous viscoelastic media. We first utilize a novel viscoelastic wave equation with decoupled fractional Laplacians to depict the Earth’s attenuation behavior. Then, an adaptive stable attenuation compensation operator is developed to realize high-precision attenuation compensation imaging. Several synthetic and field data analyses verify the effectiveness of the proposed method.

Angular distribution of near-threshold photoelectrons from resonance-enhanced multiphoton ionization of argon

We experimentally study the angular distribution of near-threshold photoelectrons of argon in the multiphoton-ionization regime. Those near-threshold photoelectrons are released from atoms by a weak 800-nm laser field and a strong 400-nm laser field with parallel polarizations. In the laser polarization direction, the yield of the near-threshold photoelectron oscillates with twice the 800-nm laser frequency, while higher-frequency components for the yield oscillations are found for the electrons emitted perpendicular to the laser polarization direction. By decomposing the photoelectron angular distribution into different partial waves, we found that those near-threshold photoelectrons are generated by the interference of different photon channels via resonant states. Moreover, we observed a large time delay between the near-threshold photoelectrons emitted along the laser polarization direction via the $5p$ and $4f$ resonant states.

Differences in beginner and expert neurointerventionalists" heart rate variability during simulated neuroangiographies.

Likewise work experience, heart rate variability (HRV) has repeatedly been correlated with improved performance under real life and simulator conditions. Using HRV as a correlate of workload, it is meaningful to assess the impact of work experience. To understand the impact of work experience on HRV metrics, we examined differences in HRV among experts and beginners during simulated endovascular neuroradiological procedures. Six inexperienced radiologists (beginners) and five experts in neurological endovascular intervention each performed 10 diagnostic angiographies on a Vascular Interventional System Trainer (VIST) simulator (Mentice AB, SW). Beyond total time, fluoroscopy time, and amount of contrast medium used, heart rate variability and the NASA-task load index were gathered as correlates of workload. The t-Test for independent samples as well as Mann-Whitney-U tests were applied for group-wise comparison between beginners and experts. Multivariate regression was used to assess the influence of age and expert status. Ten participants completed all scenarios; one participant only completed the first five scenarios. Accordingly, 105 simulations were analyzed (beginners N = 60; experts N = 45, respectively). The heart rate variability of experts and beginners significantly differed in three time domain HRV metrics (decreased RMSSD, NN50, pNN50 in experts; all p < 0.05) as well as with respect to its distribution in the frequency spectrum (LF/HF ratio; p < 0.001, increased high frequency components in experts). The HRV of beginners and expert neurointerventionalists significantly differed during simulated endovascular neuroradiological procedures. Experts presented decreased HRV, this could be a cardiovascular surrogate to the effort the subjects expend on their performance. It is in line with previous studies on vagal influences on the heart and cognitive-executive performance.

Day-ahead and intraday multi-time scale microgrid scheduling based on light robustness and MPC

An optimized microgrid scheduling model is established considering demand responses, forecast errors, and the effects of uncertainties in different scheduling stages. A day-ahead, intraday, multi-time scale economic scheduling method based on light robust optimization and model predictive control (MPC) is also proposed. In the day-ahead, long-time-scale scheduling stage, light robustness optimization is used to cope with low-frequency components in prediction errors and uncertainties, and mitigates the deviations between the day-ahead scheduling plan and the actual economic scheduling outcomes under source and load forecast errors and uncertainties. At the intraday, short-time-scale stages, the MPC tracks the day-ahead light robustness economic scheduling plan, considering the high frequency components of prediction errors and uncertainties, so as to achieve robust open-loop control, better tracking results, and better economy. Analytical results demonstrate the effectiveness of the method.

Backward-propagating source as a component of rising tone whistler-mode chorus generation

Whistler-mode chorus waves in the magnetosphere play a crucial role in space weather via wave–particle interactions. The past two decades have observed tremendous advances in theory and simulations of chorus generation; however, several details of the generation mechanism are still actively contended. To simulate chorus generation, a new envelope particle-in-cell code is introduced. The model produces a rising tone chorus element in a parabolic geomagnetic field. The initial chorus element “embryo” frequency is shown to initialize near the equator at the frequency of maximum linear growth. A backward resonant current is then observed to propagate upstream of the equator. The trajectory of the backward current follows that of a freely falling electron that has been de-trapped at the equator superimposed with forward motion at the group velocity. The backward current iteratively radiates a rising tone element where the highest frequency components are generated furthest upstream. The work provides new advancements in modeling chorus and corroborates other recent work that has also demonstrated a backward-moving source during the generation of coherent whistler-mode waves.

Frequency aware face hallucination generative adversarial network with semantic structural constraint

In this paper, we address the issue of face hallucination. Most current face hallucination methods rely on two-dimensional facial priors to generate high resolution face images from low resolution face images. These methods are only capable of assimilating global information into the generated image. Still there exist some inherent problems in these methods, such as, local features, subtle structural details and depth information are missing in final output image. This work proposes a generative adversarial network (GAN) based novel progressive face hallucination (FH) network to address these issues present among current methods. The generator of the proposed model comprises of FH network and two sub-networks, assisting FH network to generate high resolution images. The first sub-network leverages on explicitly adding high frequency components into the model. To explicitly encode the high frequency components, an auto encoder is proposed to generate high resolution coefficients of discrete cosine transform (DCT). To add three dimensional parametric information into the network, second sub-network is proposed. This network uses a shape model of 3D morphable models (3DMM) to add structural constraint to the FH network. Extensive experimentation evaluation show the usefulness of proposed architecture in the form of state-of-the-art quantitative results.

The study of ground motion amplification based on an SMASS array site

There is a consensus in earthquake engineering that different soil structures have significant effects on ground motion. Three main methods can be used to study this issue: theoretical analysis, numerical simulation, and earthquake records analysis. Notably, the combined analysis of strong-motion records and station site information appears to be the most effective and reliable approach. In this study, we used site information from the Seismic Monitoring Array of Site and Structure (SMASS) of the Institute of Disaster Prevention and a record of the earthquake that occurred in Guye, China: a point on the bedrock was considered as reference for studying the amplification effect of the soil structure on ground motion through traditional spectral ratio analysis. Our main conclusions are the following. (1) The SMASS array data indicated that the shallow soil had an amplifying effect on ground motion: the frequency bands amplifying the horizontal ground motion were mainly those between 3.4 and 6.8, 11.3–13, and 15–20.8 Hz, while the frequency bands amplifying the vertical ground vibration were mainly those between 5 and 9.9, 13.5–15.9, and 18.5–21.9 Hz (2) The SMASS array data indicated that the influence of the deep soil on the horizontal ground motion frequency component was more significant than that of vertical ground motion. (3) Overall, the SMASS array site amplified the effects of both the high (3.4–20.8 Hz) and low (0.4–2.2 Hz) frequency components of ground motion with the depth decreases of buried depth; notably, the amplification effect of the high-frequency components grew much faster than that of the low-frequency components. (4) The groundwater table had an amplification effect on the vertical ground motion; therefore, its influence is also important to consider.

Fully automated machine learning-based selection of optimal bSSFP frequency offset for artifact reduction in cardiac MRI

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Research Support from Siemens Healthineers GmbH. Background In bSSFP sequences commonly used for cardiac MRI, signal modulation (e.g. banding artifacts) due to B0 inhomogeneity is often observed, especially at higher field strengths. The spatial position of these artifacts can be shifted by a frequency offset to reduce artifacts in a region of interest (ROI), e.g. the heart. To this end, frequency scout (FS) scans are acquired to visually select the optimal frequency offset [1,2]. In this work, we propose a fully automated image-based system for selecting the optimal frequency offset on FS images based on machine learning. Methods The proposed prototype system consists of four main steps (Fig.1). First, a pre-trained deep-learning-based whole heart segmentation network is applied on a four chamber-view FS image to localize the ROI where artifacts should be reduced. Second, high frequency components within the ROI (for each frequency offset in the FS series) are extracted by successive processing of Fourier transformation, high-pass filtering, inverse Fourier transformation and subtraction over series. and N images with the lowest high-frequency content are selected. Third, an adaptive weighting map for each FS image is generated which penalizes signal deviations from a pixel-wise median that is calculated based on the selected images [3]. By averaging the maps and selecting the frame with maximum percentage, the optimal frequency offset is selected. A total of 38 datasets, acquired on multiple clinical 3T MRI scanners (MAGNETOM Skyra, Vida, Prisma, Lumina; Siemens Healthcare, Erlangen, Germany), were used to evaluate the proposed system. All FS series were annotated manually and used to compare with the system output. The experts were allowed to select multiple possible optimal FS images within a FS series. In case of multiple annotations, the system output was labelled as correct when it selected one of the offsets chosen by the expert. Further, the generated weighting maps were visually evaluated. Results The proposed system achieved an accuracy of 92.1% compared to experts’ ground truth annotations. From the failed cases (n=3), the maximum difference was off by 2 frames. Based on the generated weighting maps, a reasonable decision on the selection of the optimal frequency offset is made. The algorithm successfully selects an FS image with minimized banding and flow artifacts within the ROI (Fig. 2a). Further, it reveals that the generated weighting map correctly suppress areas containing artifacts (Fig. 2b). Conclusions Initial results demonstrate the feasibility of the proposed system to automatically select the optimal frequency offset on FS scans. Therefore, it can improve the automation of a cardiac MRI workflow. An example of the result of each step

Ultra-High Frequency Signal Transmission Based on Temporal Ghost Imaging

We propose a method based on temporal ghost imaging (TGI) that enables signal transmission at an ultra-high frequency much higher than the bandwidth of the emitter in a visible light communication (VLC) system. The signal is transmitted by a micro-LED at a frequency of up to 32 GHz, almost 600 times the 54 MHz micro-LED bandwidth. At such a high frequency, the directly transmitted signal is severely distorted and attenuated by bandwidth limit. However, based on the principle of TGI, we can recover the signal by correlating the power of the distorted signals and Hadamard modulation sequences. The linear transmission property of Hadamard spectrum density guarantees that, although bandwidth limit causes density loss, high frequency component of the signal can be reconstructed with high quality and reasonable recognizability. This work opens up a new path to pushing the signal transmission frequency beyond the bandwidth limit of the hardware, which could find meaningful applications in optical communication areas such as ultra-high frequency free-space optical communication, underwater wireless optical communication, and ultraviolet-C optical wireless communication.

Numerical study on green water and slamming loads of ship advancing in freaking wave

The simulation of shipping of green water and prediction of slamming loads are fundamental issues in the field of naval architecture. However, severe green water and slamming are rarely researched in freaking waves due to the complexity associated with strong nonlinear interactions between waves and ships. In this study, the numerical method was applied for the simulation of green water and slamming on ships advancing in freaking waves. Firstly, the numerical simulation of freaking waves, green water and ship motions were compared with theoretical and experimental data respectively to verify the reliability and accuracy of the numerical method in this study. Secondly, a three-dimensional numerical tank was built to investigate the phenomenon of multiple green water and slamming caused by freaking waves in the time domain, which indicated the second green water had a longer duration and more violent intensity compared to the first. Finally, the FFT method was adopted to analyze the features of motions and slamming loads in spectral. The results indicated that the frequencies of peak pressure in the bow and deck area were affected significantly by high frequency components of the freaking waves, while peak pressure in the superstructure area was mainly affected by low frequency.

Modified multiscale transfer entropy analysis of intra- and inter-couplings of cardio-respiratory systems during meditation

The complex nonstationary interconnection between cardiovascular and respiratory systems ensures the normal metabolism of the human body. However, the dynamic and multiscale characteristics of the interconnection are not fully understood, especially the alterations of cardiac sympathetic-parasympathetic and cardio-respiratory couplings. In this study, modified multiscale transfer entropy (MMTE) was proposed and applied to investigate the intra- and inter-couplings of cardio-respiratory systems. In the experiment, 18 healthy young subjects were recruited to finish the meditation task, and electrocardiogram (ECG) and respiratory signals were recorded in the pre-, during-, and post-task phases. A simulation study has been conducted to test the reliability of MMTE by cardio-respiratory and sympathetic-parasympathetic dynamics models. For the two physiological simulation models, MMTE on scales 10–20 possessed the ability in noise abatement and superior reliability of quantifying coupling strength to that on other scales. Experimental results showed significantly increased MMTE values from the respiratory series to the RR interval series (the time intervals between consecutive R waves of the ECG) over multiple time scales compared with the opposite direction. The same is true for the MMTE values from low frequency (LF) to high frequency (HF) components of the RR interval series. Moreover, the MMTE values between respiratory and RR interval series and between LF and HF series in the during-task phase were significantly smaller than those in other phases. These findings expand the existing mechanism of intra- and inter-couplings of cardio-respiratory systems induced by meditation. Further research is needed to validate and promote the clinical applications.

Predicting China's carbon price based on a multi-scale integrated model

Carbon price is one of the core indicators of the carbon market. Making accurate predictions of carbon prices based on insight into the volatility characteristics of the carbon market will not only provide a scientific basis for investors and regulators to make decisions and greatly promote emission reduction, but also effectively promote the healthy development of the carbon financial market. Based on the historical carbon price data of the national carbon market, this paper constructs the EEMD-BP-ELM model to predict the high and low frequency components of China's future carbon price. On this basis, according to China's goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, the CHINAGEM-E model is used to predict the trend component of the carbon price under the baseline and the 2060 carbon neutral scenarios. Finally, the trend component is combined with the high and low frequency components to obtain the price range from 2022 to 2060. We find that under the baseline scenario, China's carbon price range in 2060 is [343, 785] CNY/tCO2, while under the 2060 carbon neutral scenario, this price range is [1543, 3531] CNY/tCO2.

Abstract P1097: Heart Rate Variability Analysis In Rodents Reveals Altered Cardiac Autonomic Regulation Following Myocardial Infarction

Heart rate variability (HRV) is an index of cardiovascular health and reflects the ability of the heart to modify beating rate in response to neurohumoral factors. Alterations of HRV have been reported in patients with myocardial infarction (MI), but whether experimental models of ischemic disease recapitulate features of HRV observed in human remains to be clarified. HRV was evaluated in female mice using electrocardiograms collected in the conscious, restrained state using a tunnel device. Naïve mice and animals after permanent coronary artery ligation (MI) were studied. HRV was assessed using time-domain, frequency-domain, and non-linear parameters. By echocardiography, all infarcted animals presented akinetic anterior LV free wall and, respect to naïve mice, a 1.8-fold increase in LV diameter and a 64% reduction in ejection fraction. At 3 to ~30 days after MI, RR interval duration was preserved with respect to non-infarcted mice, whereas standard deviation of RR interval duration (SDRR), was reduced by 61-70% at 3 days to ~30 days after MI. Standard deviation of instantaneous (SD1) and long-term (SD2) RR interval variability, indicative of parasympathetic and sympathetic influence, respectively, were derived from Poincaré plots of RR i and RR i+1 intervals. SD1 and SD2 were reduced after MI (64-73% and 61-70%, respectively). Moreover, by frequency-domain analysis, the contribution of high- (1.5-5 Hz), low- (0.15-1.5 Hz), and very low-frequency (0-0.15 Hz) components of RR interval oscillations were assessed. High-frequency components, which are under the influence of the sympathetic nervous system, were not affected after MI; low-frequency oscillations, attributed to baroreflex-mediated modulation of heart rate, were reduced at 3 and 7 days after MI, whereas very low-frequency components, attributed to the modulatory action of the renin-angiotensin system, thermoregulation, and, partly, parasympathetic activity, were reduced from 3-30 days after MI. Moreover, the ratio between low and high frequency components, indicative of sympathovagal balance, was reduced after MI. In conclusion, myocardial infarction in rodent results in altered heart rate variability, which appears to be secondary, in part, to altered autonomic regulation.

DOES GREEN TEA ENHANCE THE EFFECTS OF PHYSICAL TRAINING ON HEART RATE VARIABILITY?

ABSTRACT Introduction: Green tea is associated with a series of health benefits, as is physical training. However, in combination they present little known chronotropic and autonomic cardiac effects. Objective: To evaluate the effect of the association of chronic administration of green tea and physical training on basal heart rate (HR) and heart rate variability (HRV) in Wistar rats. Methods: Forty-three Wistar rats (Rattus norvegicus, var. albinus), paired by weight and age, were distributed among four experimental groups, titled sedentary control (CONsed, n = 10), trained control (CONtre, n = 08), sedentary tea (CHÁsed, n = 16) and trained tea (CHÁtre, n = 09). Ingestion of the tea was ad libitum. The physical training protocol lasted for eight weeks and consisted of sessions of swimming with incremental loads. At the end of the training, basal heart rate and heart rate variability (HRV) in both time and frequency domains were determined. The level of significance adopted was 5% (p &lt; 0.05). Results: Both physical training and consumption of green tea caused higher resting bradycardia than that of the CONsed group animals. Regarding HRV, the CONtre, CHÁsed, and CHÁtre groups presented significantly higher values than the CONsed group. Supplementation with green tea caused an increase in the variance, high frequency (HF) component, and sympathovagal balance as compared to the CONsed group. Physical training (PT) did not enhance any of the parameters evaluated. Conclusions: There was no significant optimization of the hemodynamic or autonomic cardiovascular parameters resulting from the association between the administration of green tea and physical training in Wistar rats. Level of Evidence IV; Case Series.

Series Arc Fault Detection Sensor Based on an ABS Rogowski Coil in Medium Voltage

Three types of Rogowski coils were designed and constructed by utilising 3D printing technology: RC1, RC2, and RC3, with the sensors having the same geometrical dimensions but varying number of turns: 20, 50, and 100 respectively. To verify the feasibility and effectiveness of the proposed sensors, a series arc fault generator was set up and high voltage of up to 3-kV was injected. The analysis of the Fast Fourier Transform (FFT) was done by using MATLAB to determine frequency and voltage amplitude. From the analysis, it was determined that the higher the number of turns, the greater the sensitivity. The resonance frequency (MHz) dropped significantly as the number of turns increased. Furthermore, sensor features such as frequency bandwidth and sensitivity are affected by the number of turns, which was thoroughly investigated in this study. The Rogowski coil was created to detect the high frequency component of the series arc fault signal. All the designed sensors reliably detect the series arc fault signal, according to the experimental results.