Elimination Of Artifacts Research Articles

Isolation of multiple electrocardiogram artifacts using independent vector analysis.

Electrocardiogram (ECG) signals are normally contaminated by various physiological and nonphysiological artifacts. Among these artifacts baseline wandering, electrode movement and muscle artifacts are particularly difficult to remove. Independent component analysis (ICA) is a well-known technique of blind source separation (BSS) and is extensively used in literature for ECG artifact elimination. In this article, the independent vector analysis (IVA) is used for artifact removal in the ECG data. This technique takes advantage of both the canonical correlation analysis (CCA) and the ICA due to the utilization of second-order and high order statistics for un-mixing of the recorded mixed data. The utilization of recorded signals along with their delayed versions makes the IVA-based technique more practical. The proposed technique is evaluated on real and simulated ECG signals and it shows that the proposed technique outperforms the CCA and ICA because it removes the artifacts while altering the ECG signals minimally.

EEG Artifact Removal Using Canonical Correlation Analysis and EMD-DFA based Hybrid Denoising Approach

EEG signal recordings are often contaminated by Ocular and Muscular artifacts. Such artifacts obscure the signal's important information and complicate the diagnosis. Thus, before utilising the recorded EEG signal for any kind of diagnosis, the signal must be pre-processed to eliminate the artifacts that have contaminated the signal. In this paper a hybrid approach for denoising the EEG signal is proposed. The approach is based on Canonical Correlation Analysis (CCA) as a Blind Source Separation (BSS) technique for elimination of muscular artifacts. In the next step, DFA thresholded EMD process is utilized for the rejection of ocular artifacts. The performance of the proposed hybrid approach is compared with existing techniques on the basis of different performance metrics.

Deetect: A Deep Learning-Based Image Analysis Tool for Quantification of Adherent Cell Populations on Oxygenator Membranes after Extracorporeal Membrane Oxygenation Therapy.

The strong interaction of blood with the foreign surface of membrane oxygenators during ECMO therapy leads to adhesion of immune cells on the oxygenator membranes, which can be visualized in the form of image sequences using confocal laser scanning microscopy. The segmentation and quantification of these image sequences is a demanding task, but it is essential to understanding the significance of adhering cells during extracorporeal circulation. The aim of this work was to develop and test a deep learning-supported image processing tool (Deetect), suitable for the analysis of confocal image sequences of cell deposits on oxygenator membranes at certain predilection sites. Deetect was tested using confocal image sequences of stained (DAPI) blood cells that adhered to specific predilection sites (junctional warps and hollow fibers) of a phosphorylcholine-coated polymethylpentene membrane oxygenator after patient support (>24 h). Deetect comprises various functions to overcome difficulties that occur during quantification (segmentation, elimination of artifacts). To evaluate Deetects performance, images were counted and segmented manually as a reference and compared with the analysis by a traditional segmentation approach in Fiji and the newly developed tool. Deetect outperformed conventional segmentation in clustered areas. In sections where cell boundaries were difficult to distinguish visually, previously defined post-processing steps of Deetect were applied, resulting in a more objective approach for the resolution of these areas.

A "Steady-State" Relaxation Dispersion Nuclear Magnetic Resonance Experiment for Studies of Chemical Exchange in Degenerate 1H Transitions of Methyl Groups.

Degenerate spin-systems consisting of magnetically equivalent nuclear spins, such as a 1H3 spin-system in selectively 13CH3-labeled proteins, present considerable challenges for the design of Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion NMR experiments to characterize chemical exchange on the micro-to-millisecond time-scale. Several approaches have been previously proposed for the elimination of deleterious artifacts observed in methyl 1H CPMG relaxation dispersion profiles obtained for (13C)1H3 groups. We describe an alternative, experimentally simple solution and design a "steady-state" methyl 1H CPMG scheme, where 90° or acute-angle (<90°) 1H radiofrequency pulses are applied after each CPMG echo in-phase with methyl 1H magnetization, resulting in the establishment of a "steady-state" for effective rates of magnetization decay. A simple computational procedure for quantitative analysis of the "steady-state" CPMG relaxation dispersion profiles is developed. The "steady-state" CPMG methodology is applied to two protein systems where exchange between major and minor species occurs in different regimes on the chemical shift time-scale.

A Novel Adaptive Noise Elimination Algorithm in Long RR Interval Sequences for Heart Rate Variability Analysis.

As heart rate variability (HRV) studies become more and more prevalent in clinical practice, one of the most common and significant causes of errors is associated with distorted RR interval (RRI) data acquisition. The nature of such artifacts can be both mechanical as well as software based. Various currently used noise elimination in RRI sequences methods use filtering algorithms that eliminate artifacts without taking into account the fact that the whole RRI sequence time cannot be shortened or lengthened. Keeping that in mind, we aimed to develop an artifacts elimination algorithm suited to long-term (hours or days) sequences that does not affect the overall structure of the RRI sequence and does not alter the duration of data registration. An original adaptive smart time series step-by-step analysis and statistical verification methods were used. The adaptive algorithm was designed to maximize the reconstruction of the heart-rate structure and is suitable for use, especially in polygraphy. The authors submit the scheme and program for use.

The Second-Order Shannon Total Generalized Variation for Image Restoration

In this paper, a Shannon-based second-order TGV (Shannon TGV) is proposed. Shannon first and second-order operators are defined and their corresponding adjoints are investigated leading to design a variational model as an optimization problem. We obtain the dual form of the proposed model and utilize it to formulate imaging problems, i.e., denoising and deconvolution. Moreover, we examine numerically the effectiveness of the proposed scheme in imaging problems and compare the results to the classic total variation (TV), the second-order total generalized variation (TGV) and Shannon total variation. The outcomes confirm that the proposed model retains the advantages of both TGV and Shannon TV in elimination of artifacts simultaneously and admit the greater capability of the new model to remove artifacts.

EEGANet: Removal of Ocular Artifacts From the EEG Signal Using Generative Adversarial Networks.

The elimination of ocular artifacts is critical in analyzing electroencephalography (EEG) data for various brain-computer interface (BCI) applications. Despite numerous promising solutions, electrooculography (EOG) recording or an eye-blink detection algorithm is required for the majority of artifact removal algorithms. This reliance can hinder the model's implementation in real-world applications. This paper proposes EEGANet, a framework based on generative adversarial networks (GANs), to address this issue as a data-driven assistive tool for ocular artifacts removal (source code is available at https://github.com/IoBT-VISTEC/EEGANet). After the model was trained, the removal of ocular artifacts could be applied calibration-free without relying on the EOG channels or the eye blink detection algorithms. First, we tested EEGANet's ability to generate multi-channel EEG signals, artifacts removal performance, and robustness using the EEG eye artifact dataset, which contains a significant degree of data fluctuation. According to the results, EEGANet is comparable to state-of-the-art approaches that utilize EOG channels for artifact removal. Moreover, we demonstrated the effectiveness of EEGANet in BCI applications utilizing two distinct datasets under inter-day and subject-independent schemes. Despite the absence of EOG signals, the classification performance of the signals processed by EEGANet is equivalent to that of traditional baseline methods. This study demonstrates the potential for further use of GANs as a data-driven artifact removal technique for any multivariate time-series bio-signal, which might be a valuable step towards building next-generation healthcare technology.

Elimination of Motion Artifacts in Signal of Photoplethysmography Sensor

Elimination of Motion Artifacts in Signal of Photoplethysmography Sensor

Motion resilience of the balanced steady-state free precession geometric solution.

Many MRI sequences are sensitive to motion and its associated artifacts. The linearized geometric solution (LGS), a balanced steady-state free precession (bSSFP) off-resonance signal demodulation technique, is evaluated with respect to motion artifact resilience. The mechanism and extent of LGS motion artifact resilience is examined in simulated, flow phantom, and in vivo clinical imaging. Motion artifact correction capabilities are decoupled from susceptibility artifact correction when feasible to permit controlled analysis of motion artifact correction when comparing the LGS with standard and phase-cycle-averaged (complex sum) bSSFP imaging. Simulations reveal that the LGS demonstrates motion artifact reduction capabilities similar to standard clinical bSSFP imaging techniques, with slightly greater resilience in high SNR regions and for shorter-duration motion. Flow phantom experiments assert that the LGS reduces shorter-duration motion artifact error by ∼24%-65% relative to the complex sum, whereas reconstructions exhibit similar error reduction for constant motion. In vivo analysis demonstrates that in the internal auditory canal/orbits, the LGS was deemed to have less artifact in 24%/49% and similar artifact in 76%/51% of radiological assessments relative to the complex sum, and the LGS had less artifact in 97%/81% and similar artifact in 3%/16% of assessments relative to standard bSSFP. Only 2 of 63 assessments deemed the LGS inferior to either complex sum or standard bSSFP in terms of artifact reduction. The LGS provides sufficient bSSFP motion artifact resilience to permit robust elimination of susceptibility artifacts, inspiring its use in a wide variety of applications.

EEG artifact removal using sub-space decomposition, nonlinear dynamics, stationary wavelet transform and machine learning algorithms.

Blind source separation (BSS) methods have received a great deal of attention in electroencephalogram (EEG) artifact elimination as they are routine and standard signal processing tools to remove artifacts and reserve desired neural information. On the other hand, a classifier should follow BSS methods to automatically identify artifactual sources and remove them in the following steps. In addition, removing all detected artifactual components leads to loss of information since some desired information related to neural activity leaks to these sources. So, an approach should be employed to detect and suppress the artifacts and reserve neural activity. This study introduces a novel method based on EEG and Poincare planes in the phase space to detect artifactual components estimated by second-order blind identification (SOBI). Artifacts are detected using a mixture of well-known conventional classifiers and were removed employing stationary wavelet transform (SWT) to reserve neural information. The proposed method is a combination of signal processing techniques and machine learning algorithms, including multi-layer perceptron (MLP), K-nearest neighbor (KNN), naïve Bayes, and support vector machine (SVM) which have significant results while applying our proposed method to different scenarios. Simulated, semi-simulated, and real EEG signals are employed to evaluate the proposed method, and several evaluation criteria are calculated. We achieved acceptable results, for example, 98% average accuracy and 97% average sensitivity in artifactual EEG component detection or about 2% as mean square error in EEG reconstruction after artifact removal. Results showed that the proposed method is effective and can be used in future studies as we have considered different real-world scenarios to evaluate it.

Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review.

Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time-frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.

Defect data image enhancement method based on all-focus imaging algorithm

When the total focus imaging (TFM) algorithm is oriented to multi-layer media, the energy attenuation of the sound beam in the wedge coupling model results in a decrease in detection energy and imaging reliability. In this paper, a new defect data image enhancement method is proposed to calibrate the full-focus imaging model: the attenuation coefficients of different emission-received acoustic beams in the coupled model were measured by the plexiglass reflection method, and the whole matrix captured data were reconstructed. To enhance the image of defect data, multi-threshold fusion is proposed to eliminate the artifacts in the imaging region, especially the noise artifacts in the near field region. Experimental results show that compared with the traditional full-focus imaging algorithm, the self-built algorithm model proposed in this paper greatly improves the energy attenuation problem at the defect with large deflection angle. Especially in the range of excellent detection results, the energy uniformity of the defect is significantly improved, which has a certain inhibitory effect on the artifacts near the defect. The subsequent artifact elimination work improved the defect characterization ability and image signal-to-noise ratio of fully focused images while the effect of near-field artifact elimination was significant.

A NOVEL METHOD TO FILTER OUT PACING ARTIFACTS FROM ELECTROCARDIOGRAMS – FACILITATING AUTOMATED VOLTAGE-TIME-INTEGRAL MEASUREMENT

BackgroundQRS 3D-voltage-time-integral (VTI) or 3D QRS area is a recently proposed marker for assessing response to cardiac resynchronization therapy (CRT). Automated VTI measurement in patients receiving CRT is affected by pacing artifacts that occur within the QRS.ObjectiveTo demonstrate that automated signal processing can effectively filter out pacing artifacts without appreciably affecting the true ECG signal.MethodsWe devised a two-step filter. First, the true QRS is identified as the peak within the QRS complex that is lower than the 98th percentile of the modified Z-score of detrended data plus 40. The pacing outlier filter deletes all data above this value and fills the gap with a B-spline interpolation function. Second, a median filter is applied to eliminate residual noise. 12 leads each from 30 ECGs with pacing artifacts and 30 non-paced control ECGs were evaluated. We compared the unfiltered ECG with 3 filters (a) Whittaker & Hayes, (b) Laplacian of Gaussian, and (c) our novel method.ResultsAs shown in the Table, among paced ECGs our novel method had the greatest diminution of the pacing artifact (peak-to-peak amplitude from 1.59±1.36 to 0.02±0.04 mV and artifact VTI from 5.28±4.71 to 0.05±0.43 μVs). The novel method had the least impact on the QRS VTI measurement in ECGs with no pacing (from 35.5±20.1 to 35.1±19.9 μVs). The Figure demonstrates complete elimination of pacing artifacts with the novel filter in most cases, without any overt effect on the true ECG signal.ConclusionView Large Image Figure ViewerDownload Hi-res image Download (PPT) BackgroundQRS 3D-voltage-time-integral (VTI) or 3D QRS area is a recently proposed marker for assessing response to cardiac resynchronization therapy (CRT). Automated VTI measurement in patients receiving CRT is affected by pacing artifacts that occur within the QRS. QRS 3D-voltage-time-integral (VTI) or 3D QRS area is a recently proposed marker for assessing response to cardiac resynchronization therapy (CRT). Automated VTI measurement in patients receiving CRT is affected by pacing artifacts that occur within the QRS. ObjectiveTo demonstrate that automated signal processing can effectively filter out pacing artifacts without appreciably affecting the true ECG signal. To demonstrate that automated signal processing can effectively filter out pacing artifacts without appreciably affecting the true ECG signal. MethodsWe devised a two-step filter. First, the true QRS is identified as the peak within the QRS complex that is lower than the 98th percentile of the modified Z-score of detrended data plus 40. The pacing outlier filter deletes all data above this value and fills the gap with a B-spline interpolation function. Second, a median filter is applied to eliminate residual noise. 12 leads each from 30 ECGs with pacing artifacts and 30 non-paced control ECGs were evaluated. We compared the unfiltered ECG with 3 filters (a) Whittaker & Hayes, (b) Laplacian of Gaussian, and (c) our novel method. We devised a two-step filter. First, the true QRS is identified as the peak within the QRS complex that is lower than the 98th percentile of the modified Z-score of detrended data plus 40. The pacing outlier filter deletes all data above this value and fills the gap with a B-spline interpolation function. Second, a median filter is applied to eliminate residual noise. 12 leads each from 30 ECGs with pacing artifacts and 30 non-paced control ECGs were evaluated. We compared the unfiltered ECG with 3 filters (a) Whittaker & Hayes, (b) Laplacian of Gaussian, and (c) our novel method. ResultsAs shown in the Table, among paced ECGs our novel method had the greatest diminution of the pacing artifact (peak-to-peak amplitude from 1.59±1.36 to 0.02±0.04 mV and artifact VTI from 5.28±4.71 to 0.05±0.43 μVs). The novel method had the least impact on the QRS VTI measurement in ECGs with no pacing (from 35.5±20.1 to 35.1±19.9 μVs). The Figure demonstrates complete elimination of pacing artifacts with the novel filter in most cases, without any overt effect on the true ECG signal. As shown in the Table, among paced ECGs our novel method had the greatest diminution of the pacing artifact (peak-to-peak amplitude from 1.59±1.36 to 0.02±0.04 mV and artifact VTI from 5.28±4.71 to 0.05±0.43 μVs). The novel method had the least impact on the QRS VTI measurement in ECGs with no pacing (from 35.5±20.1 to 35.1±19.9 μVs). The Figure demonstrates complete elimination of pacing artifacts with the novel filter in most cases, without any overt effect on the true ECG signal. Conclusion

A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization

Objective. High-frequency oscillations (HFOs) are considered a biomarker of the epileptogenic zone in intracranial EEG recordings. However, automated HFO detectors confound true oscillations with spurious events caused by the presence of artifacts. Approach. We hypothesized that, unlike pseudo-HFOs with sharp transients or arbitrary shapes, real HFOs have a signal characteristic that can be represented using a small number of oscillatory bases. Based on this hypothesis using a sparse representation framework, this study introduces a new classification approach to distinguish true HFOs from the pseudo-events that mislead seizure onset zone (SOZ) localization. Moreover, we further classified the HFOs into ripples and fast ripples by introducing an adaptive reconstruction scheme using sparse representation. By visualizing the raw waveforms and time-frequency representation of events recorded from 16 patients, three experts labeled 6400 candidate events that passed an initial amplitude-threshold-based HFO detector. We formed a redundant analytical multiscale dictionary built from smooth oscillatory Gabor atoms and represented each event with orthogonal matching pursuit by using a small number of dictionary elements. We used the approximation error and residual signal at each iteration to extract features that can distinguish the HFOs from any type of artifact regardless of their corresponding source. We validated our model on sixteen subjects with thirty minutes of continuous interictal intracranial EEG recording from each. Main results. We showed that the accuracy of SOZ detection after applying our method was significantly improved. In particular, we achieved a 96.65% classification accuracy in labeled events and a 17.57% improvement in SOZ detection on continuous data. Our sparse representation framework can also distinguish between ripples and fast ripples. Significance. We show that by using a sparse representation approach we can remove the pseudo-HFOs from the pool of events and improve the reliability of detected HFOs in large data sets and minimize manual artifact elimination.

A quick survey of EEG signal noise removal methods

An Electroencephalogram (EEG) is produced as a consequence of the electrical voltage of neurons in the brain. The EEG signal is crucial for detecting brain activity and attitude. Because this signal has very low amplitude, it is easily corrupted by different artefacts. The study and analysis of brain signals in the presence of these artifacts is a challenging task. ECG, EOG, EMG, and motion are the popular artifacts that induce disturbance to the EEG signal. This survey paper emphasizes the artifact elimination methods with their substantial parameters that must be considered during the study of published research on this trend.

Sparsity-based method for ring artifact elimination in computed tomography.

Ring artifact elimination is one of the popular problems in computed tomography (CT). It appears in the reconstructed image in the form of bright or dark patterns of concentric circles. In this paper, based on the compressed sensing theory, we propose a method for eliminating the ring artifact during the image reconstruction. The proposed method is based on representing the projection data by a sum of two components. The first component contains ideal correct values, while the latter contains imperfect error values causing the ring artifact. We propose to minimize some sparsity-induced norms corresponding to the imperfect error components to effectively eliminate the ring artifact. In particular, we investigate the effect of using different sparse models, i.e. different sparsity-induced norms, on the accuracy of the ring artifact correction. The proposed cost function is optimized using an iterative algorithm derived from the alternative direction method of multipliers. Moreover, we propose improved versions of the proposed algorithms by incorporating a smoothing penalty function into the cost function. We also introduce angular constrained forms of the proposed algorithms by considering a special case as follows. The imperfect error values are constant over all the projection angles, as in the case where the source of ring artifact is the non-uniform sensitivity of the detector. Real data and simulation studies were performed to evaluate the proposed algorithms. Results demonstrate that the proposed algorithms with incorporating smoothing penalty and their angular constrained forms are effective in ring artifact elimination.

Universal orbit design for metal artifact elimination

Objective. Metal artifacts are a persistent problem in CT and cone-beam CT. In this work, we propose to reduce or even eliminate metal artifacts by providing better sampling of data using non-circular orbits. Approach. We treat any measurements intersecting metal as missing data, and aim to design a universal orbit that can generally accommodate arbitrary metal shapes and locations. We adapted a local sampling completeness metric based on Tuy’s condition to quantify the extent of sampling in the presence of metal. A maxi-min objective over all possible metal locations was used for orbit design. A simple class of sinusoidal orbits was evaluated as a function of frequencies, maximum tilt angles, and orbital extents. Experimental implementation of these orbits were performed on an imaging bench and evaluated on two phantoms, one containing metal balls and the other containing a pedicle screw assembly for spine fixation. Metal artifact reduction (MAR) performance was compared amongst three approaches: non-circular orbits only, algorithmic correction only, and a combined approach. Main results. Theoretical evaluations of the objective favor sinusoidal orbits with large tilt angles and large orbital extents. Furthermore, orbits that leverage redundant azimuthal angles to sample non-redundant data have better performance, e.g. even or non-integer frequency sinusoids for a 360° acquisition. Experimental data support the trends observed in theoretical evaluations. Reconstructions using even or non-integer frequency orbits present less streaking artifacts and background details with finer resolution, even when multiple metal objects are present and even in the absence of MAR algorithms. The combined approach of non-circular orbits and MAR algorithm yields the best performance. The observed trend in image quality is supported by quantitative measures of sampling and severity of streaking artifact. Significance. This work demonstrates that sinusoidal orbits are generally robust against metal artifacts and can provide an avenue for improved image quality in interventional imaging.

Printed label defect detection using twice gradient matching based on improved cosine similarity measure

Many vision-based methods for printed label defect detection have been proposed to replace inefficient manual inspection. However, due to the existence of artifacts and noise regions, it usually leads to a large number of misjudgments. Also, since most of the printed labels are non-rigid, they are prone to local deformation, which will cause lots of artifacts after image subtraction. This paper proposes a novel printed label defect detection framework (PLDD), which performs twice gradient matching based on improved cosine similarity measures. The overall idea is based on comparing a golden master (GM) image with test images, thus the GM image is demanded. Specifically, latent defect candidates will be extracted firstly from RGB sub-images for artifact elimination. Mask mechanism is also introduced to eliminate the influence of background gradient features around these defect candidates. Experiments compared with existing methods are conducted with three industrial datasets. The results exhibit that PLDD achieves a high mean F1 score (0.9702), and only 103 false positives (FP) occurred in 44,628 ground truths. Defects are being detected in real-time with an average time consuming of 0.26362 s.

Effect of audiovisual stimulation on adult memory performance based electroencephalography wavelet analysis

Human memory stores various information and events that can be retrieved when needed. Many factors can influence memory performance which either provide positive or negative feedback. This research investigates the effect of audiovisual stimulation on adult memory based on electroencephalography (EEG) analysis. Sixty college students are participating in this experimental study. They must memorize visual assessment at two different levels in Mozart’s Sonata music and white noise stimulation. During memorizing duration, the EEG machine records brain electrical activity based on 10–20 electrode placement. The collected raw brain signals are processed using the wavelet-based method. The stationary wavelet transform (SWT) is used for artifact elimination, whereas discrete wavelet transform (DWT) is applied to obtain alpha, beta, theta, and gamma rhythms. The time–frequency domain features are collected from the EEG signals to discover the influence of audiovisual stimulation. The findings showed a different increasing and decreasing trend of mean, standard deviation, and peak-to-peak EEG signal amplitude before and after audiovisual stimulation exposure. The theta and alpha rhythms showed the most influence with the highest relative power. Suppression of relative gamma and beta power is vital for improving visual information processing and attention level. Memorizing in audio stimulation has suppressed the relative alpha, beta, theta, and gamma power, leading to better visual memorizing ability. The white noise stimulation provides more influence on adult visual memory.

Accidental monstrosities: Taxonomic chimeras in Ostracoda (Crustacea).

Taxonomic chimeras, artificial taxa created unintentionally by amalgamation of morphological traits belonging to different taxonomic units, can be found in 19th century to present day scientific literature. We recognise two types of such artefacts in Ostracoda. Chimera Type 1 is represented by species defined by morphological traits belonging to two (or more) different valid taxa at the rank of species. A thorough comparative analysis of carapace and limb characteristics of Fabaeformiscandona balatonica (Daday) sensu Bronshtein (1947) allows us to conclude that it is a chimera comprising F. balatonica and F. levanderi (Hirschmann), for which we provide new, expanded diagnoses. Chimera Type 2 refers to a genus defined by juvenile morphological traits that also occur in other genera. Analysis of Candoniella Schneider, 1956 shows it to be an artefact based on morphological traits belonging to juveniles of at least three genera: Pseudocandona Kaufmann, 1900, Fabaeformiscandona Krsti, 1972 and Neglecandona Krsti, 2006. Elimination of taxonomic artefacts is necessary to improve not only taxonomy but also adjacent domains of investigation like the ecology and geographical distribution of confused taxa. Considered in historical contexts, the creation and perpetuation of such accidental monstrosities may be attributed to social motivations as well as limitations of material, literature and communication.