Journal articles: 'Health IT artifact'

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Bateson, Mary Catherine. "Health as artifact." Journal of Professional Nursing 5, no. 6 (November 1989): 322–25. http://dx.doi.org/10.1016/s8755-7223(89)80071-7.

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Ernstberger, Thorsten. "Implant-related MRI artifacts of determined interbody test spacers: artifact calculations due to implant parameters in a porcine spine model." Health 01, no. 03 (2009): 207–10. http://dx.doi.org/10.4236/health.2009.13035.

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Warnecke, Joana M., Ju Wang, Tolga Cakir, Nicolai Spicher, Nagarajan Ganapathy, and Thomas M. Deserno. "Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair." PLOS ONE 16, no. 7 (July 28, 2021): e0254780. http://dx.doi.org/10.1371/journal.pone.0254780.

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Continuous monitoring of an electrocardiogram (ECG) in private diagnostic spaces such as vehicles or apartments allows early detection of cardiovascular diseases. We will use an armchair with integrated capacitive electrodes to record the capacitive electrocardiogram (cECG) during everyday activities. However, movements and other artifacts affect the signal quality. Therefore, an artifact index is needed to detect artifacts and classify the cECG. The unavailability of cECG data and reliable ground truth information requires new recordings to develop an artifact index. This study is designed to test the hypothesis: an artifact index can be devised, which intends to estimate the signal quality of segments and classify signals. In a single-arm study with 44 subjects, we will record two activities of 11-minute duration: reading and watching television. During recording, we will capture cECG, ECG, and oxygen saturation (SpO2) with time synchronization as well as keypoint-based movement indicators obtained from a video camera. SpO2 provides additional information on the subject’s health status. The keypoint-based movements indicate artifacts in the cECG. We will combine all ground truth data to evaluate the index. In the future, we aim at using the artifact index to exclude cECG segments with artifacts from further analysis. This will improve cECG technology for the measurement of cardiovascular parameters.

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Pressly, Kalynn B. "Alcohol gel artifact." American Journal of Infection Control 28, no. 4 (August 2000): 322. http://dx.doi.org/10.1067/mic.2000.107200.

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Papathanasiou, E. S. "Knitting artifact." Journal of Neurology, Neurosurgery & Psychiatry 74, no. 11 (November 1, 2003): 1501. http://dx.doi.org/10.1136/jnnp.74.11.1501.

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Lin, Chin-Teng, Chih-Sheng Huang, Wen-Yu Yang, Avinash Kumar Singh, Chun-Hsiang Chuang, and Yu-Kai Wang. "Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering." Journal of Healthcare Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/5081258.

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Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

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Oehler, M., and T. M. Buzug. "Statistical Image Reconstruction for Inconsistent CT Projection Data." Methods of Information in Medicine 46, no. 03 (2007): 261–69. http://dx.doi.org/10.1160/me9041.

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Summary Objectives: The filtered backprojection is not able to cope with metal-induced inconsistencies in the Radon space which leads to artifacts in reconstructed CT images. A new algorithm is presented that reduces the drawbacks of existing artifact reduction strategies. Methods: Inconsistent projection data are bridged by directed interpolation. These projections are reconstructed using a weighted maximum likelihood algorithm (λ-MLEM). The correlation coefficient between images of a torso phantom marked with steel markers reconstructed with λ-MLEM and images of the same torso slice without markers quantifies the quality achieved. For clinical data, entropy maximization is presented to obtain appropriate weightings. Results: Different interpolation strategies have been applied. The quality of reconstruction sensitively depends on the complexity of interpolation. A directional interpolation gives best results. However, the quality of the images can be further improved byan appropriate weighing within λ-MLEM. This has been demonstrated with data from a torso phantom, a jaw with amalgam fillings and a hip prosthesis. Conclusions: λ-MLEM image reconstruction using data from directional Radon space interpolation is a new approach for metal artifact reduction. The weighting in this statistical approach is used to reduce the influence of residual inconsistencies in a way that optimal artifact suppression is obtained by optimizing a compromise between residual inconsistencies and void data. The image quality is superior compared with other artifact reduction strategies.

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Mendes, Lúcia Helena da Silva, Luiz Cláudio Sampaio Mendes, Lilian Lucy dos Santos, Carlos Otávio Senff, Claudimar Pereira da Veiga, and Luiz Carlos Duclós. "An Artifact for Evaluating the Quality of Health Service Providers: Evidence From Brazil." INQUIRY: The Journal of Health Care Organization, Provision, and Financing 55 (January 2018): 004695801879016. http://dx.doi.org/10.1177/0046958018790168.

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The aim of this article is to present an artifact for evaluating the quality and performance of service providers in the field of health care: the UNIPLUS Program. To verify the scientific nature of the artifact and ensure that it meets the criteria set by the community and the environment, the premises of Design Science Research (DSR) were used. As this research field lacks empirical evidence, the artifact was tested from 2013 to 2015 with 25 health care service providers from different categories, with an emphasis on hospitals and clinics located in 7 cities in the south of Brazil. This article makes 3 main contributions to the field: (1) the artifact can be applied to any health insurance operator in Brazil and other countries, as it meets the legal norms and requirements established by current legislation; (2) it helps health service providers by generating information that identifies shortfalls and possibilities for improvement for every aspect analyzed in the evaluation process; and (3) it uses the DSR methodology in an evaluation artifact that evaluates the quality and performance of services in the field of health care. The artifact proved to be adequate for the purpose in question, helping to improve the quality of care and institutional performance.

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Cohen, D. A., and L. Mascola. "Congenital syphilis not an artifact." American Journal of Public Health 81, no. 4 (April 1991): 513–14. http://dx.doi.org/10.2105/ajph.81.4.513-a.

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Markowitz, Richard I. "Wet diaper artifact." Pediatric Radiology 37, no. 12 (October 10, 2007): 1303. http://dx.doi.org/10.1007/s00247-007-0641-8.

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Hu, Qihan, Xintao Deng, Xin Liu, Aiguo Wang, and Cuiwei Yang. "A Robust Beat-to-Beat Artifact Detection Algorithm for Pulse Wave." Mathematical Problems in Engineering 2020 (November 11, 2020): 1–8. http://dx.doi.org/10.1155/2020/5691805.

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With the rise of the concept of smart cities and healthcare, artificial intelligence helps people pay increasing attention to the health of themselves. People can wear a variety of wearable devices to monitor their physiological conditions. The pulse wave is a kind of physiological signal which is widely applied in the physiological monitoring system. However, the pulse wave is susceptible to artifacts, which prevents its popularization. In this work, we propose a novel beat-to-beat artifact detection algorithm, which performs pulse wave segmentation based on wavelet transform and then detects artifacts beat by beat based on the decision list. We verified our method on data acquired from different databases and compared with experts’ annotations. The segmentation algorithm achieved an accuracy of 96.13%. When it is applied to detect main peaks, the performance achieved an accuracy of 99.11%. After the previous segmentation algorithm, the artifact detection algorithm can detect beat-to-beat pulse waves and artifacts with an accuracy of 98.11%. The result indicated that the proposed method is robust for pulse waves of different patterns and could effectively detect the artifact without the complex algorithm. In summary, our proposed algorithm is capable of annotating pulse waves of various patterns and determining pulse wave quality. Since our method is developed and evaluated on the transmission-mode PPG data, it is more suitable for the devices and applications inside the hospitals instead of reflectance-mode PPG.

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Rey-López, Juan Pablo, Leandro Fornias de Rezende, Thiago Hérick de Sá, and Emmanuel Stamatakis. "Is the Metabolically Healthy Obesity Phenotype an Irrelevant Artifact for Public Health?" American Journal of Epidemiology 182, no. 9 (September 12, 2015): 737–41. http://dx.doi.org/10.1093/aje/kwv177.

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Lee, Sandra Soo-Jin. "The Biobank as Political Artifact." ANNALS of the American Academy of Political and Social Science 661, no. 1 (August 10, 2015): 143–59. http://dx.doi.org/10.1177/0002716215591141.

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This article discusses the institutional practices of classifying and creating taxonomies of difference within biobanks (repositories that store a broad range of biological materials, including DNA) and the technical and sociopolitical priorities that ultimately create biobanks. I argue that biobanks operate as political artifacts and that the social circumstances surrounding the development and use of biobanks determine what counts as meaningful difference within human genetic research. The massive collection of human DNA, blood, and tissues is critical to genomic medicine and the development and governance of biobanks structure knowledge that will ultimately bear on how population differences are interpreted and health disparities are framed. Careful consideration of how to avoid the conflation of concepts of race, ethnicity, and nationality with biological differences is necessary to identify effective interventions that will bear positively on health.

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Braswell, Gregory S. "Preschool children’s participation in representational and non-representational activities." Journal of Early Childhood Research 15, no. 2 (January 13, 2016): 195–211. http://dx.doi.org/10.1177/1476718x15614043.

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The present study examined representational and non-representational activities in which children in a Head Start classroom participated. This was an investigation from the perspective of cultural-historical activity theory of how components (e.g. artifacts and division of labour) of classroom activities vary across and within types of activities. Participants included a class of 21 ethnically diverse 4- and 5-year-olds and two teachers. Data collection involved naturalistic observations of classroom members participating in indoor play, outdoor play, and notational activities (e.g. reading and drawing) over 8 days. Who was involved, artifact use, and artifact-related actions varied by activity. Furthermore, who was involved, actions, and division of labour were strongly linked in second-by-second analyses. The present study contributes research which situates children’s development within daily activities.

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Xu, Xiaowen, Ying Liang, Pei He, and Junliang Yang. "Adaptive Motion Artifact Reduction Based on Empirical Wavelet Transform and Wavelet Thresholding for the Non-Contact ECG Monitoring Systems." Sensors 19, no. 13 (July 1, 2019): 2916. http://dx.doi.org/10.3390/s19132916.

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Electrocardiogram (ECG) signals are crucial for determining the health status of the human heart. A clean ECG signal is critical in analysis and diagnosis of heart diseases. However, ECG signals are often contaminated by motion artifact noise in the non-contact ECG monitoring systems. In this paper, an ECG motion artifact removal approach based on empirical wavelet transform (EWT) and wavelet thresholding (WT) is proposed. This method consists of five steps, namely, spectrum preprocessing, spectrum segmentation, EWT decomposition, wavelet threshold denoising, and EWT reconstruction. The proposed approach was used to process real ECG signals collected by the non-contact ECG monitoring equipment. The results of quantitative study and analysis indicate that this approach produces a better performance in terms of restorage of QRS complexes of the original ECG with reduced distortion, retaining useful information in ECG signals, and improvement of the signal to noise ratio (SNR) value of the signal. The output results of the practical ECG signal test show that motion artifact in the real recorded ECG is effectively filtered out. The proposed method is feasible for reducing motion artifacts from ECG signals, whether from simulation ECG signals or practical non-contact ECG monitoring systems.

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Mount-Campbell, Austin F., Kevin D. Evans, David D. Woods, Esther M. Chipps, Susan D. Moffatt-Bruce, and Emily S. Patterson. "Value and Usage of a Workaround Artifact: A Cognitive Work Analysis of “Brains” Use by Hospital Nurses." Journal of Cognitive Engineering and Decision Making 13, no. 2 (February 4, 2019): 67–80. http://dx.doi.org/10.1177/1555343418825429.

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We identify the value and usage of a cognitive artifact used by hospital nurses. By analyzing the value and usage of workaround artifacts, unmet needs using intended systems can be uncovered. A descriptive study employed direct observations of registered nurses at two hospitals using a paper workaround (“brains”) and the Electronic Health Record. Field notes and photographs were taken; the format, size, layout, permanence, and content of the artifact were analyzed. Thirty-nine observations, spanning 156 hr, were conducted with 20 nurses across four clinical units. A total of 322 photographs of paper-based artifacts for 161 patients were collected. All participants used and updated “brains” during report, and throughout the shift, most were self-generated. These artifacts contained patient identifiers in a header with room number, last name, age, code status, and physician; clinical data were recorded in the body with historical chronic issues, detailed assessment information, and planned activities for the shift. Updates continuously made during the shift highlighted important information, updated values, and tracked the completion of activities. The primary functional uses of “brains” are to support nurses’ needs for clinical immediacy through personally generated snapshot overviews for clinical summaries and updates to the status of planned activities.

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Shmarlouski, A., Y. Shulhevich, S. Geraci, J. Friedemann, N. Gretz, S. Neudecker, J. Hesser, and D. Stsepankou. "Automatic artifact removal from GFR measurements." Biomedical Signal Processing and Control 14 (November 2014): 30–41. http://dx.doi.org/10.1016/j.bspc.2014.06.010.

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Hoog Antink, Christoph, Florian Schulz, Steffen Leonhardt, and Marian Walter. "Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring." Sensors 18, no. 2 (December 25, 2017): 38. http://dx.doi.org/10.3390/s18010038.

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Królak, Aleksandra, Tomasz Wiktorski, Magnus Friestad Bjørkavoll-Bergseth, and Stein Ørn. "Artifact Correction in Short-Term HRV during Strenuous Physical Exercise." Sensors 20, no. 21 (November 8, 2020): 6372. http://dx.doi.org/10.3390/s20216372.

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Heart rate variability (HRV) analysis can be a useful tool to detect underlying heart or even general health problems. Currently, such analysis is usually performed in controlled or semi-controlled conditions. Since many of the typical HRV measures are sensitive to data quality, manual artifact correction is common in literature, both as an exclusive method or in addition to various filters. With proliferation of Personal Monitoring Devices with continuous HRV analysis an opportunity opens for HRV analysis in a new setting. However, current artifact correction approaches have several limitations that hamper the analysis of real-life HRV data. To address this issue we propose an algorithm for automated artifact correction that has a minimal impact on HRV measures, but can handle more artifacts than existing solutions. We verify this algorithm based on two datasets. One collected during a recreational bicycle race and another one in a laboratory, both using a PMD in form of a GPS watch. Data include direct measurement of electrical myocardial signals using chest straps and direct measurements of power using a crank sensor (in case of race dataset), both paired with the watch. Early results suggest that the algorithm can correct more artifacts than existing solutions without a need for manual support or parameter tuning. At the same time, the error introduced to HRV measures for peak correction and shorter gaps is similar to the best existing solution (Kubios-inspired threshold-based cubic interpolation) and better than commonly used median filter. For longer gaps, cubic interpolation can in some cases result in lower error in HRV measures, but the shape of the curve it generates matches ground truth worse than our algorithm. It might suggest that further development of the proposed algorithm may also improve these results.

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Ardizzone, E., R. Pirrone, and O. Gambino. "Bias artifact suppression on MR volumes." Computer Methods and Programs in Biomedicine 92, no. 1 (October 2008): 35–53. http://dx.doi.org/10.1016/j.cmpb.2008.06.005.

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Vila-Rodriguez, Fidel, and Joseph Tham. "The Relevance of EEG Artifact Identification." Journal of ECT 29, no. 1 (March 2013): 13–14. http://dx.doi.org/10.1097/yct.0b013e3182621c4a.

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Repka, Michael X., Kyle A. Arnoldi, and M. Edward Wilson. "Lateral Incomitance in Exotropia: Fact or Artifact?/Lateral Incomitance in Exotropia: Fact or Artifact? Discussion." Journal of Pediatric Ophthalmology & Strabismus 28, no. 3 (May 1991): 125–30. http://dx.doi.org/10.3928/0191-3913-19910501-03.

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Bhagyalakshmi, A., and V. Vijaya Chamundeeswari. "Hybrid Approach Towards Removal of Streak Artifacts and Region Identification in Brain Radiology." Journal of Medical Imaging and Health Informatics 10, no. 3 (March 1, 2020): 604–9. http://dx.doi.org/10.1166/jmihi.2020.2977.

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A streak artifact is an anomaly seen in the CT image of the brain. It is like a dark bands or streaks appearing in an image that is not present in the original region. The appearance of these artifacts leads to misinterpretation of medical diagnosis. Under sampling, photon starvation, motion and beam hardening are some of the reasons for occurrence of streak artifacts. These type of artifacts commonly occur in the posterior fossa of the brain. This paper mainly focuses on region shape and texture identification and removal of streak artifacts exists in the brain image. Initially morphological operators and fuzzy k-means algorithms are used to identify the different regions in the CT brain image. Each region is treated as a single image and features are extracted for each image. The feature descriptors are constructed using region properties and statistical features. The extracted features are compared with threshold brain image to identify the dark bands of streak artifacts.

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Lee, Hye-Nam. "Usefulness of twinkling Artifact for urinary Stone Measurement." Indian Journal of Public Health Research & Development 9, no. 3 (2018): 690. http://dx.doi.org/10.5958/0976-5506.2018.00368.6.

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Adler, Nancy E. "Late Age at Childbirth: Survival Advantage or Artifact?" American Journal of Public Health 107, no. 9 (September 2017): 1381–84. http://dx.doi.org/10.2105/ajph.2017.303965.

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AMOIRIDIS, G. "All tibial foot: an electrophysiological artifact." Journal of Neurology, Neurosurgery & Psychiatry 67, no. 5 (November 1, 1999): 698–99. http://dx.doi.org/10.1136/jnnp.67.5.698.

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Deepika, J., T. Senthil, C. Rajan, and A. Surendar. "Machine learning algorithms: a background artifact." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 143. http://dx.doi.org/10.14419/ijet.v7i1.1.9214.

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With the greater development of technology and automation human history is predominantly updated. The technology movement shifted from large mainframes to PCs to cloud when computing the available data for a larger period. This has happened only due to the advent of many tools and practices, that elevated the next generation in computing. A large number of techniques has been developed so far to automate such computing. Research dragged towards training the computers to behave similar to human intelligence. Here the diversity of machine learning came into play for knowledge discovery. Machine Learning (ML) is applied in many areas such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents reviews about machine learning algorithm foundations, its types and flavors together with R code and Python scripts possibly for each machine learning techniques.

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Beckett, Megan. "Converging Health Inequalities in Later Life-An Artifact of Mortality Selection?" Journal of Health and Social Behavior 41, no. 1 (March 2000): 106. http://dx.doi.org/10.2307/2676363.

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Martin, Stephen A., and Lisa Simon. "Oral Health and Medicine Integration: Overcoming Historical Artifact to Relieve Suffering." American Journal of Public Health 107, S1 (May 2017): S30—S31. http://dx.doi.org/10.2105/ajph.2017.303683.

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B.M., Manjula, and Chirag Sharma. "BCG Artifact Removal Using Improved Independent Component Analysis Approach." Indonesian Journal of Electrical Engineering and Computer Science 5, no. 1 (January 1, 2017): 130. http://dx.doi.org/10.11591/ijeecs.v5.i1.pp130-138.

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<p>Recent advancement in bio-medical field has attracted researchers toward BCG signal processing for monitoring the health activities. There have been various techniques for monitoring physical activities such as (SCG) Seismocardiography, Electrocardiography (ECG) etc. BCG signal is a measurement of reaction force applied for cardiac ejection of blood. Various measurement schemes and systems have been developed for BCG detection and measurement such as tables, beds, weighing scale and chairs. Weighing scales have been promising method for measurement of BCG signal because of less cost of implementation, smaller size etc. but these devices still suffer from the artifact which are induced due to subject movement or motion during signal acquisition or it can be caused due to floor vibrations. Artifact removal is necessary for efficient analysis and health monitoring. In this work we address the issue of artifact removal in BCG signal by proposing a novel method of signal processing. According to proposed approach raw signal is pre-processed and parsed to independent component analysis which provides the decomposed components and later k-means is applied to detect the components which are responsible for artifact and removed. Proposed approach is compared with existing method and shows better performance in terms of artifact removal.</p>

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Cuevas-González, Daniel, Miguel Bravo-Zanoguera, Eladio Altamira-Colado, Roberto López-Avitia, Juan Pablo García-Vázquez, and Marco A. Reyna. "Coping with Motion Artifacts by Analog Front-End ECG Microchips under Variable Digital Resolution and Gain." Engineering Proceedings 2, no. 1 (November 14, 2020): 12. http://dx.doi.org/10.3390/ecsa-7-08247.

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The development of portable ECG technology has found growing markets, from wearable ECG sensors to ambulatory ECG recorders, encountering challenges of moderately complex to tightly regulated devices. This study investigated how a typical 0.5–40 Hz bandwidth ECG is affected by motion artifact when using analog front-end (AFE) integrated circuits such as the AD823X family. It is known that the typical amplitude resolution of current mobile health ECG devices is 10–12 bits, and sometimes 16-bits, which is enough for monitoring but might be insufficient to identify the small potential amplitudes useful in diagnoses. The interest now is on the interplay of how a digital resolution choice and variable gain can cope with motion artifacts inherent in mobile health devices. With our methodology for a rapid prototyping of an ECG device, and using the AFE AD8232 and Bluetooth communication, a specific cardiac monitor ECG configuration was evaluated under two microcontroller systems of different resolution: a generic Arduino Nano board which featured a 10-bit analog-to-digital converter (ADC) and the 24-bit ADC of Silicon Labs C8051F350 board. The ECG cardiac monitor setup, recommended by Analog Devices, featuring two gain values under these two different microcontroller systems, was explored as to its ability to solve motion artifact problems.

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WAZANA, ASHLEY, MICHAELINE BRESNAHAN, and JENNIE KLINE. "The Autism Epidemic: Fact or Artifact?" Journal of the American Academy of Child & Adolescent Psychiatry 46, no. 6 (June 2007): 721–30. http://dx.doi.org/10.1097/chi.0b013e31804a7f3b.

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Lukas, Lou A. "The TEAM Approach: Mistaking Artifact for Art?" Journal of Oncology Practice 13, no. 9 (September 2017): 567–68. http://dx.doi.org/10.1200/jop.2017.026542.

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Priem, Michael, Niroop Ravula, and Lee Nguyen. "Unexpected MRI artifact encountered under anesthesia." Pediatric Anesthesia 30, no. 10 (September 6, 2020): 1153–54. http://dx.doi.org/10.1111/pan.13998.

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Liu, Qingze, Aiping Liu, Xu Zhang, Xiang Chen, Ruobing Qian, and Xun Chen. "Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis." Journal of Healthcare Engineering 2019 (December 31, 2019): 1–13. http://dx.doi.org/10.1155/2019/4159676.

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Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts. Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity. At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination. In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA. Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics. Our proposed approach can take advantage of SSA as well as cross-channel information. The performance of SSA-CCA is evaluated on semisimulated and real data. The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.

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Mottron, Laurent, and Danilo Bzdok. "Autism spectrum heterogeneity: fact or artifact?" Molecular Psychiatry 25, no. 12 (April 30, 2020): 3178–85. http://dx.doi.org/10.1038/s41380-020-0748-y.

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AbstractThe current diagnostic practices are linked to a 20-fold increase in the reported prevalence of ASD over the last 30 years. Fragmenting the autism phenotype into dimensional “autistic traits” results in the alleged recognition of autism-like symptoms in any psychiatric or neurodevelopemental condition and in individuals decreasingly distant from the typical population, and prematurely dismisses the relevance of a diagnostic threshold. Non-specific socio-communicative and repetitive DSM 5 criteria, combined with four quantitative specifiers as well as all their possible combinations, render limitless variety of presentations consistent with the categorical diagnosis of ASD. We propose several remedies to this problem: maintain a line of research on prototypical autism; limit the heterogeneity compatible with a categorical diagnosis to situations with a phenotypic overlap and a validated etiological link with prototypical autism; reintroduce the qualitative properties of autism presentations and of current dimensional specifiers, language, intelligence, comorbidity, and severity in the criteria used to diagnose autism in replacement of quantitative “social” and “repetitive” criteria; use these qualitative features combined with the clinical intuition of experts and machine-learning algorithms to differentiate coherent subgroups in today’s autism spectrum; study these subgroups separately, and then compare them; and question the autistic nature of “autistic traits”

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Dunbar-Jacob, Jacqueline, and Jeffrey M. Rohay. "Predictors of medication adherence: fact or artifact." Journal of Behavioral Medicine 39, no. 6 (June 15, 2016): 957–68. http://dx.doi.org/10.1007/s10865-016-9752-8.

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Pontes, Helena, Márcia Carvalho, Paula Guedes de Pinho, Helena Carmo, Fernando Remião, Félix Carvalho, and Maria Lourdes Bastos. "Ethanol, the forgotten artifact in cell culture." Archives of Toxicology 82, no. 3 (February 6, 2008): 197–98. http://dx.doi.org/10.1007/s00204-008-0285-y.

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Phillips, Susan P., and Katarina Hamberg. "Women's relative immunity to the socio-economic health gradient: artifact or real?" Global Health Action 8, no. 1 (May 5, 2015): 27259. http://dx.doi.org/10.3402/gha.v8.27259.

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Li, Na, Shoujun Zhou, Gang Zhao, Zhicheng Zhang, Yaoqin Xie, and Xiaokun Liang. "Iterative stripe artifact correction framework for TOF-MRA." Computers in Biology and Medicine 134 (July 2021): 104456. http://dx.doi.org/10.1016/j.compbiomed.2021.104456.

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Sheridan, David C., Ryan Dehart, Amber Lin, Michael Sabbaj, and Steven D. Baker. "Heart Rate Variability Analysis: How Much Artifact Can We Remove?" Psychiatry Investigation 17, no. 9 (September 25, 2020): 960–65. http://dx.doi.org/10.30773/pi.2020.0168.

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Objective Heart rate variability (HRV) evaluates small beat-to-beat time interval (BBI) differences produced by the heart and suggested as a marker of the autonomic nervous system. Artifact produced by movement with wrist worn devices can significantly impact the validity of HRV analysis. The objective of this study was to determine the impact of small errors in BBI selection on HRV analysis and produce a foundation for future research in mental health wearable technology.Methods This was a sub-analysis from a prospective observational clinical trial registered with clinicaltrials.gov (NCT03030924). A cohort of 10 subject’s HRV tracings from a wearable wrist monitor without any artifact were manipulated by the study team to represent the most common forms of artifact encountered.Results Root mean square of successive differences stayed below a clinically significant change when up to 5 beats were selected at the wrong time interval and up to 36% of BBIs was removed. Standard deviation of next normal intervals stayed below a clinically significant change when up to 3 beats were selected at the wrong time interval and up to 36% of BBIs were removed. High frequency HRV shows significant changes when more than 2 beats were selected at the wrong time interval and any BBIs were removed.Conclusion Time domain HRV metrics appear to be more robust to artifact compared to frequency domains. Investigators examining wearable technology for mental health should be aware of these values for future analysis of HRV studies to improve data quality.

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Grueso-Hinestroza, Merlin Patricia, Mónica López-Santamaría, Javier L. González, Wilmer Salcedo, and Marysella Amaya. "Organizational Culture Artifacts and Compassionate Human Resources Practices in a Healthcare Organization." Asian Social Science 14, no. 4 (March 19, 2018): 90. http://dx.doi.org/10.5539/ass.v14n4p90.

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Compassion in organizations and their determinants is a research topic that is still underdeveloped, in consequence, a study was carried out with a sample of 112 employees in a health services organization in Bogotá (Colombia). To achieve this goal, the Organizational Culture Artifact Questionnaire -OCSA- and the Compassion Organizational Practices Questionnaire were administered. The results show that the organizational culture through its artifacts predicts in a significant way the adoption of compassion organizational practices. In analyzing the type of culture that has the greatest predictive power over compassionate organizational practices, it has been found that progressive culture has the greatest effect, in contrast to traditional culture. The conclusions discuss the practical implications of the study and its limitations.

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Appleton, K. M., J. V. Woodside, D. Arveiler, B. Haas, P. Amouyel, M. Montaye, J. Ferrieres, et al. "Depression and mortality: Artifact of measurement and analysis?" Journal of Affective Disorders 151, no. 2 (November 2013): 632–38. http://dx.doi.org/10.1016/j.jad.2013.07.010.

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Niemeyer, Max, Tobias G. J. Bergmann, and Ralph Beneke. "Oxygen uptake plateau: calculation artifact or physiological reality?" European Journal of Applied Physiology 120, no. 1 (November 20, 2019): 231–42. http://dx.doi.org/10.1007/s00421-019-04267-7.

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Damodaran, Saji S. "Severity of family distress of schizophrenia?fact or artifact?" Acta Psychiatrica Scandinavica 88, no. 3 (September 1993): 221. http://dx.doi.org/10.1111/j.1600-0447.1993.tb03443.x.

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Machado, Juliano, Amauri Machado, and Alexandre Balbinot. "Deep learning for surface electromyography artifact contamination type detection." Biomedical Signal Processing and Control 68 (July 2021): 102752. http://dx.doi.org/10.1016/j.bspc.2021.102752.

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Smith, Timothy Charles, Allan Green, and Peter Hutton. "Recognition of cardiogenic artifact in pediatric capnograms." Journal of Clinical Monitoring 10, no. 4 (July 1994): 270–75. http://dx.doi.org/10.1007/bf02899514.

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Klcinman, Bruce, Kamlcsh Shah, Ronald Belusko, and Bradford Blakeman. "Electrocardiographic artifact caused by extracorporeal roller pump." Journal of Clinical Monitoring 6, no. 3 (July 1990): 258–59. http://dx.doi.org/10.1007/bf02832157.

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Ishimitsul, Yoshiyuki, Kazuyuki Arai, Ricky K. Taira, and H. K. Huang. "Radiological laser film scanner sampling artifact." Computerized Medical Imaging and Graphics 14, no. 1 (January 1990): 25–33. http://dx.doi.org/10.1016/0895-6111(90)90137-z.

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Poets, C. F., and V. A. Stebbens. "Detection of movement artifact in recorded pulse oximeter saturation." European Journal of Pediatrics 156, no. 10 (September 25, 1997): 808–11. http://dx.doi.org/10.1007/s004310050719.

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Journal articles on the topic 'Health IT artifact'

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