Journal articles: 'Signal scoring'

1

Truong, Khuong, Jack Boenders, Zofia Maciorowski, Philippe Vielh, Bernard Dutrillaux, Bernard Malfoy, and Claire A. Bourgeois. "Signal Amplification of FISH for Automated Detection Using Image Cytometry." Analytical Cellular Pathology 13, no. 3 (1997): 137–46. http://dx.doi.org/10.1155/1997/297216.

Full text

Abstract:

The purpose of this study was to improve the detection of FISH signals, in order that spot counting by a fully automated image cytometer be comparable to that obtained visually under the microscope. Two systems of spot scoring, visual and automated counting, were investigated in parallel on stimulated human lymphocytes with FISH using a biotinylated centromeric probe for chromosome 3. Signal characteristics were first analyzed on images recorded with a coupled charge device (CCD) camera. Number of spots per nucleus were scored visually on these recorded images versus automatically with a DISCOVERY image analyzer. Several fluochromes, amplification systems and pretreatments were tested. Our results for both visual and automated scoring show that the tyramide amplification system (TSA) gives the best amplification of signal if pepsin treatment is applied prior to FISH. Accuracy of the automated scoring, however, remained low (58% of nuclei containing two spots) compared to the visual scoring because of the high intranuclear variation between FISH spots.

APA, Harvard, Vancouver, ISO, and other styles

2

Young, K. L., and E. R. Delay. "Comparison of standard scoring with signal detection theory scoring of the seashore rhythmus test." Archives of Clinical Neuropsychology 6, no. 3 (January 1, 1991): 237. http://dx.doi.org/10.1093/arclin/6.3.237.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Senny, Frederic, Gisele Maury, Laurent Cambron, Amandine Leroux, Jacques Destiné, and Robert Poirrier. "The sleep/wake state scoring from mandible movement signal." Sleep and Breathing 16, no. 2 (June 11, 2011): 535–42. http://dx.doi.org/10.1007/s11325-011-0539-4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Bharath Siva Varma, P., Adimulam Yesubabu, and K. Subrahmanyam. "Identify virtual ligand hits using consensus scoring approach for drug target S. Aureus." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 84. http://dx.doi.org/10.14419/ijet.v7i2.7.10265.

Full text

Abstract:

The associations amongst protein and ligand can be assessed for scoring functions based on the binding modes of ligand obtained from search algorithms. Several scoring functions have been proposed and every method has their own strengths and weaknesses, hence a multiple scoring analysis referred as consensus scoring increases the overall signal noise ratio. Therefore, top ten ligands obtained and were subjected to re-scoring using mcule, AutoDock and X-score functions. A computational consensus scoring analysis was taken up for a dataset of top ten PubChem compounds which are identified to exhibit better inhibitory properties against phosphotransacetylase, a putative drug target for S. aureus.

APA, Harvard, Vancouver, ISO, and other styles

5

Macaš, Martin, Nela Grimová, Václav Gerla, and Lenka Lhotská. "Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances." Proceedings 31, no. 1 (November 20, 2019): 46. http://dx.doi.org/10.3390/proceedings2019031046.

Full text

Abstract:

Sleep scoring is an important tool for physicians. Assigning of segments of long biomedical signal into sleep stages is, however, a very time consuming, tedious and expensive task which is performed by an expert. Automatic sleep scoring is not well accepted in clinical practice because of low interactivity and unacceptable error, which is often caused by inter-patient variability. This is solved by proposing a semi-automatic approach, where parts of the signal are selected for manual labeling by active learning and the resulting classifier is used for automatic labeling of the remaining signal. The active learning is disturbed by noisy ambiguous data instances caused by continuous character of the sleep stage transitions and a removal of such transitional instances from the training set prior to active learning can improve the efficiency of the method. This paper proposes to use the hidden Markov model for the detection of the transitional instances. It shows experimentally on 35 sleep EEG recordings that such a method significantly improves the semi-automatic method. A complete methodology for semi-automatic sleep scoring is proposed and evaluated, which can be better accepted as a decision support tool for sleep scoring experts.

APA, Harvard, Vancouver, ISO, and other styles

6

Zhang, Wei-Xun, Xiaoyong Pan, and Hong-Bin Shen. "Signal-3L 3.0: Improving Signal Peptide Prediction through Combining Attention Deep Learning with Window-Based Scoring." Journal of Chemical Information and Modeling 60, no. 7 (June 5, 2020): 3679–86. http://dx.doi.org/10.1021/acs.jcim.0c00401.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Procházka, Aleš, Jiří Kuchyňka, Oldřich Vyšata, Martin Schätz, Mohammadreza Yadollahi, Saeid Sanei, and Martin Vališ. "Sleep scoring using polysomnography data features." Signal, Image and Video Processing 12, no. 6 (February 10, 2018): 1043–51. http://dx.doi.org/10.1007/s11760-018-1252-6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Damjanov, Nemanja, Vera Milic, Juan Carlos Nieto-González, Iustina Janta, Lina Martínez-Estupiñan, Belén Serrano, Carmen Mata, et al. "Multiobserver Reliability of Ultrasound Assessment of Salivary Glands in Patients with Established Primary Sjögren Syndrome." Journal of Rheumatology 43, no. 10 (August 15, 2016): 1858–63. http://dx.doi.org/10.3899/jrheum.151220.

Full text

Abstract:

Objective.To evaluate the multiobserver reliability of salivary gland ultrasonography (SGUS) for scoring greyscale (GS) parenchymal inhomogeneity and parenchymal color Doppler (CD) signal in patients with established primary Sjögren syndrome (pSS).Methods.The study comprised 2 multiobserver reliability assessments in patients with pSS in 2 European centers. The first reliability exercise was performed on 24 patients with pSS and 8 controls who were independently evaluated with GS and CD US by 5 observers at the Institute of Rheumatology, Belgrade, Serbia. The second reliability exercise was carried out on 10 patients with pSS who were independently assessed with GS and CD US by 8 observers at the Hospital G.U. Gregorio Marañón, Madrid, Spain. SGUS parenchymal inhomogeneity and parenchymal CD signal were semiquantitatively scored using a 4-grade scoring system. The multiobserver agreement was calculated by the overall agreement and Light’s κ statistics.Results.A total of 640 SGUS examinations were performed in the first reliability exercise and a total of 320 examinations in the second reliability exercise. Multiobserver reliability was good (κ = 0.71–0.79) to excellent (κ = 0.81–0.82) for GS parenchymal inhomogeneity in both exercises. There was a moderate (κ = 0.53–0.58) to good (κ = 0.70) multiobserver reliability for parenchymal CD signal in the first exercise. However, there was no agreement or only a fair agreement (κ = 0.03–0.29) for parenchymal CD signal in the second exercise.Conclusion.US may be a reliable technique in the multiobserver scoring of GS parenchymal inhomogeneity of major SG in patients with established pSS. CD scoring of SG needs further standardization to be used in multicenter studies.

APA, Harvard, Vancouver, ISO, and other styles

9

Farooq, Hemu, Anuj Jain, and V. K. Sharma. "A Proposal for Sleep Scoring Analysis Designed by Computer Assisted using Physiological Signals." International Journal of Engineering and Advanced Technology 10, no. 5 (June 30, 2021): 230–35. http://dx.doi.org/10.35940/ijeat.e2609.0610521.

Full text

Abstract:

Sleep is utterly regarded as compulsory component for a person’s prosperity and is an exceedingly important element for wellbeing of a healthy person. It is a condition in which an individual is physically and mentally at rest. The conception of sleep is considered extremely peculiar and is a topic of discussion and researchers all over the world has been attracted by this concept. Sleep analysis and its stages is analyzed to be useful in sleep research and sleep medicine area. By properly analyzing the sleep scoring system and its different stages has proven helpful for diagnosing sleep disorders. As it’s seen,sleep stage classification by manual process is a hectic procedure as it takes sufficient time for sleep experts to perform data analysis. Besides, mistakes and irregularities in between classification of same data can be recurrent. Therefore, theuse of automatic scoring system in order to support reliable classification is highly in greater use. The scheduled work provides an insight to use the automatic scheme which is based on real time EMG signals and Artificial neural network. EMG is an electro neurological diagnostic tool which evaluates and records the electrical activity generated by muscle cells. The sleep scoring analysis can be applied by recording Electroencephalogram (EEG), Electromyogram (EMG), and Electrooculogram (EOG) based on epoch and this method is termed as PSG test or polysomnography test. The epoch measured has length segments for a period of 30 seconds. The standard database of EMG records was gathered from various hospitals in sleep laboratory which gives the different stages of sleep. These are Waking, Non-REM1 (stage-1), NonREM2 (stage-2), Non-REM3 (stage-3), REM. The collection of datawas done for the period of 30 second known as epoch, for seven hours. The dataset obtained from the biological signal was managedso that necessary data is to be extracted from degenerated signal utilized for the purpose of study. As a matter of fact, it is known electrical signals are distributed throughout the body and is needed to be removed. These unwanted signals are termed as artifacts and they are removed with the help of filters. In this proposed work, the signal is filtered by making use of low-pass filter called Butterworth. The withdrawn characteristics were instructed and categorized by utilizing Artificial Neural Network (ANN). ANN, on the other hand is highly complicated network and utilizing same in the field of biomedical when contracted with electrical signals,acquired from human body is itself a novel. The precision obtainedby the help of the procedure was discovered to be satisfactory and hencethe processis very useful in clinics of sleep, especiallyhelpful for neuro-scientists for discovering the disturbance in sleep.

APA, Harvard, Vancouver, ISO, and other styles

10

Lweesy, K., N. Khasawneh, M. Fraiwan, H. Wenz, H. Dickhaus, and L. Fraiwan. "Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA." Methods of Information in Medicine 49, no. 03 (2010): 230–37. http://dx.doi.org/10.3414/me09-01-0054.

Full text

Abstract:

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

APA, Harvard, Vancouver, ISO, and other styles

11

Svetnik, V., T. Wang, Y. Xu, B. J. Hansen, and S. V. Fox. "0432 A Deep Learning Approach for Automated Sleep-Wake Scoring in Pre-Clinical Animal Models." Sleep 43, Supplement_1 (April 2020): A166. http://dx.doi.org/10.1093/sleep/zsaa056.429.

Full text

Abstract:

Abstract Introduction Experimental investigation of sleep-wake dynamics in animals is an important part of pharmaceutical development. It typically involves recording of electroencephalogram, electromyogram, locomotor activity, and electrooculogram. Visual identification, or scoring, of the sleep-wake states from these recordings is time-consuming. We sought to develop software for automated sleep-wake scoring capable of processing large databases of multi-channel signal recordings in a range of animal species. Methods We used a large historical database of signal recordings and scores in non-human primates, dogs, mice, and rats, to develop a deep Convolutional Neural Network (CNN) classification algorithm for automatically scoring sleep-wake states. We compared the performance of the CNN algorithm with that of a widely used Machine Learning algorithm, Random Forest (RF). Results In non-human primates and dogs, CNN accuracy in sleep-wake scoring of data was significantly higher than RF accuracy: 0.75 versus 0.66 for non-human primates and 0.73 versus 0.64 for dogs. In rodents, the difference between CNN and RF was smaller: 0.83 versus 0.81 for mice and 0.78 versus 0.77 for rats. The variability of CNN accuracy was lower than that of RF for non-human primates, dogs and mice, but similar for rats. Conclusion We recommend use of CNN for sleep-wake scoring in non-human primates and dogs, and RF for sleep-wake scoring in rodents. Support Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA

APA, Harvard, Vancouver, ISO, and other styles

12

Young, K. L., and E. R. Delay. "Seashore rhythm test: Comparison of signal detection theory and standard scoring procedures." Archives of Clinical Neuropsychology 8, no. 2 (January 1, 1993): 111–21. http://dx.doi.org/10.1093/arclin/8.2.111.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

Ruehland, Warren R., Thomas J. Churchward, Linda M. Schachter, Tristia Lakey, Natalie Tarquinio, Fergal J. O'Donoghue, Maree Barnes, and Peter D. Rochford. "Polysomnography using abbreviated signal montages: impact on sleep and cortical arousal scoring." Sleep Medicine 16, no. 1 (January 2015): 173–80. http://dx.doi.org/10.1016/j.sleep.2014.11.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

DeCarlo, Lawrence T. "STUDIES OF A LATENT-CLASS SIGNAL-DETECTION MODEL FOR CONSTRUCTED-RESPONSE SCORING." ETS Research Report Series 2008, no. 2 (December 2008): i—55. http://dx.doi.org/10.1002/j.2333-8504.2008.tb02149.x.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Young, K. "Seashore rhythm test: Comparison of signal detection theory and standard scoring procedures." Archives of Clinical Neuropsychology 8, no. 2 (April 1993): 111–21. http://dx.doi.org/10.1016/0887-6177(93)90029-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

16

Kuo, C., and G. Chen. "0441 An Automatic Sleep Scoring System Based on Ensemble Convolutional Neural Network and Spectrogram of Sleep Physiological Signal." Sleep 43, Supplement_1 (April 2020): A169. http://dx.doi.org/10.1093/sleep/zsaa056.438.

Full text

Abstract:

Abstract Introduction Manual sleep stage scoring is time consuming and subjective. Therefore, several studies focused on developing automated sleep scoring algorithms. The previously reported the automatic sleep scoring have been develop usually using small dataset, which less than 100 subjects. In this study, an automatic sleep scoring system based on ensemble convolutional neural network (ensemble-CNN) and spectrogram of sleep physiological signal was proposed and evaluated using a large dataset with sleep disorder. Methods The spectrograms were computed from each 30-s EEG and EOG of 994 subjects from PhysioNet 2018 challenge dataset, using the continuous wavelet transform, which were fed into an ensemble-CNN classification for training. The ensemble-CNN contained five pretrained models, ResNet-101, Inception-v4, DenseNet-201, Xception, and NASNet models, because these models’ architectures are different which can learn different features from the spectrograms to obtain high accuracy. The probabilities of five models were averaged to decide the sleep stage for each spectrogram. After classifying sleep stage, a smoothing process was used for sleep continuity. Moreover, the total 80% data from PhysioNet dataset were randomly assigned to the training set, and the remaining data were assigned to the testing set. Results To validate the robustness of the proposed system, the validation procedure was repeated five times. The performance measures were averaged over the five runs. The overall agreement and kappa coefficient of the proposed method are 82% and 0.73, respectively. The sensitivity of the sleep stages of Wake, N1, N2, N3, and REM are 90.0%, 48.6%, 84.9%, 84.2%, and 81.9%, respectively. Conclusion The performance of the proposed method was achieved expert level, and it was noted that the ensemble-CNN is a promising solution for automatic sleep stage scoring. This method can assist clinical staff in reducing the time required for sleep stage scoring in the future. Support This work was supported by the Ministry of Science and Technology, Taiwan. (MOST 106-2218-E-035-013-MY2, 108-2221-E-035-064, and 108-2634-F-006-012).

APA, Harvard, Vancouver, ISO, and other styles

17

Zhao, Yongdong, T. Shawn Sato, Sabrina M. Nielsen, Meinrad Beer, Mingqian Huang, Ramesh S. Iyer, Michael McGuire, et al. "Development of a Scoring Tool for Chronic Nonbacterial Osteomyelitis Magnetic Resonance Imaging and Evaluation of its Interrater Reliability." Journal of Rheumatology 47, no. 5 (October 1, 2019): 739–47. http://dx.doi.org/10.3899/jrheum.190186.

Full text

Abstract:

Objective.Serial magnetic resonance imaging (MRI) examinations are often needed in chronic nonbacterial osteomyelitis (CNO) to determine the objective response to treatment. Our objectives in this study were (1) to develop a consensus-based MRI scoring tool for clinical and research use in CNO; and (2) to evaluate interrater reliability and agreement using whole-body (WB)-MRI from children with CNO.Methods.Eleven pediatric radiologists discussed definitions and grading of signal intensity, size of signal abnormality within bone marrow, and associated features on MRI through monthly conference calls and a consensus meeting, using a nominal group technique in July 2017. WB-MRI scans from children with CNO were deidentified for training reading and an interrater reliability study. The reading by each radiologist was conducted in a randomized order. Interrater reliability for abnormal signal and severity were assessed using free-marginal κ statistics.Results.Radiologists reached a consensus on grading CNO-specific MRI findings and on describing bone units based on anatomy. A total of 45 sets of WB-MRI scans, including 4 sets of non-CNO MRI examinations, were selected for the final reading. The mean κ of each category of bones was > 0.7 with majority > 0.9 demonstrating substantial/almost perfect interrater reliability of readings among radiologists. The agreement on signal intensity and the size of signal abnormality within the most commonly affected bones (femur and tibia) were lower than those of other bones.Conclusion.The chronic nonbacterial osteomyelitis magnetic resonance imaging scoring (CROMRIS) tool is a comprehensive standardized scoring tool for MRI in children with CNO. Our interrater study demonstrated good interrater reliability and agreement of readings.

APA, Harvard, Vancouver, ISO, and other styles

18

Duun-Henriksen, Jonas, Troels Wesenberg Kjaer, David Looney, Mary Doreen Atkins, Jens Ahm Sørensen, Martin Rose, Danilo P. Mandic, Rasmus Elsborg Madsen, and Claus Bogh Juhl. "EEG Signal Quality of a Subcutaneous Recording System Compared to Standard Surface Electrodes." Journal of Sensors 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/341208.

Full text

Abstract:

Purpose. We provide a comprehensive verification of a new subcutaneous EEG recording device which promises robust and unobtrusive measurements over ultra-long time periods. The approach is evaluated against a state-of-the-art surface EEG electrode technology.Materials and Methods. An electrode powered by an inductive link was subcutaneously implanted on five subjects. Surface electrodes were placed at sites corresponding to the subcutaneous electrodes, and the EEG signals were evaluated with both quantitative (power spectral density and coherence analysis) and qualitative (blinded subjective scoring by neurophysiologists) analysis.Results. The power spectral density and coherence analysis were very similar during measurements of resting EEG. The scoring by neurophysiologists showed a higher EEG quality for the implanted system for different subject states (eyes open and eyes closed). This was most likely due to higher amplitude of the subcutaneous signals. During periods with artifacts, such as chewing, blinking, and eye movement, the two systems performed equally well.Conclusions. Subcutaneous measurements of EEG with the test device showed high quality as measured by both quantitative and more subjective qualitative methods. The signal might be superior to surface EEG in some aspects and provides a method of ultra-long term EEG recording in situations where this is required and where a small number of EEG electrodes are sufficient.

APA, Harvard, Vancouver, ISO, and other styles

19

Mascarenhas, Sheryl, and Nina Couette. "A Systematic Review of the Inclusion of Non-Inflammatory Ultrasonographic Enthesopathy Findings in Enthesitis Scoring Indices." Diagnostics 11, no. 4 (April 8, 2021): 669. http://dx.doi.org/10.3390/diagnostics11040669.

Full text

Abstract:

Ultrasound has advanced the diagnosis and management of patients with inflammatory rheumatic conditions. It can be used to identify and monitor enthesitis, a cardinal feature of spondyloarthropthies. Several enthesitis scoring systems utilizing ultrasound to determine entheseal involvement have been developed. These scoring systems generally rely on determining the presence or absence of erosions, tendon enlargement, power Doppler signal, or enthesophytes. This systematic review identified ultrasound scoring systems that have been utilized for evaluating enthesitis and what key components derive the score. Review of these scoring systems, however, demonstrated confounding as some of the score components including enthesophytes may be seen in non-inflammatory conditions and some components including erosions can be seen from chronic damage, but not necessarily indicate active inflammatory disease. What is furthermore limiting is that currently there is not an agreed upon term to describe non-inflammatory enthesopathies, further complicating these scoring systems. This review highlights the need for a more comprehensive ultrasound enthesopathy scoring index.

APA, Harvard, Vancouver, ISO, and other styles

20

Yan, Rui, Chi Zhang, Karen Spruyt, Lai Wei, Zhiqiang Wang, Lili Tian, Xueqiao Li, Tapani Ristaniemi, Jihui Zhang, and Fengyu Cong. "Multi-modality of polysomnography signals’ fusion for automatic sleep scoring." Biomedical Signal Processing and Control 49 (March 2019): 14–23. http://dx.doi.org/10.1016/j.bspc.2018.10.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Tazenkova, Olga Andreevna. "Application of Credit Risk Scoring Methods in Corporate Borrower Monitoring." Russian Digital Libraries Journal 24, no. 4 (September 12, 2021): 689–709. http://dx.doi.org/10.26907/1562-5419-2021-24-4-689-709.

Full text

Abstract:

A method for assessing the risk of default of a corporate borrower at the monitoring stage based on a scoring assessment is proposed. This paper provides proof of the hypothesis that scoring methods for assessing credit risks can be used not only at the stage of initial assessment of a potential borrower when making a decision on lending, but also at the stage of its monitoring when accompanying a transaction. Monitoring is a periodic review of the credit quality of the corporate borrower with whom the loan agreement is concluded. This is done for the purpose of timely detection of negative signals, as well as timely response to threatening trends in the borrower's activities. Some credit institutions save on monitoring by relying on the decision-making system, considering it flawless. However, this saving can be a fatal mistake, since many things change over time during the "life" of the enterprise. This is facilitated by both external factors (political, economic) and internal (incorrect development strategy of the organization, inability to assess its own credit capabilities, unscrupulous counterparties). The proposed method is a system of automatic risk signals that have been tested for predictive ability, excluding manual procedures. The proposed solution includes markers (risk signals) that have a predictive ability above average, which can lead to a default of the corporate borrower. In addition, color marking is applied – red, yellow, green, which allows you to visualize the criticality of the identified risk signal depending on the predictive ability - a visual representation of the borrower's risks in order to facilitate interpretation. The analysis of the developed method showed how much it is possible to speed up the monitoring process, which will allow for a prompt response to the identified risk signals, as well as to predict the likely deterioration of the borrower's credit quality in the loan or guarantee portfolio without compromising the quality of risk assessment.

APA, Harvard, Vancouver, ISO, and other styles

22

Billings, Curtis J., Tina M. Penman, Emily M. Ellis, Lucas S. Baltzell, and Garnett P. McMillan. "Phoneme and Word Scoring in Speech-in-Noise Audiometry." American Journal of Audiology 25, no. 1 (March 2016): 75–83. http://dx.doi.org/10.1044/2016_aja-15-0068.

Full text

Abstract:

Purpose Understanding speech in background noise is difficult for many individuals; however, time constraints have limited its inclusion in the clinical audiology assessment battery. Phoneme scoring of words has been suggested as a method of reducing test time and variability. The purposes of this study were to establish a phoneme scoring rubric and use it in testing phoneme and word perception in noise in older individuals and individuals with hearing impairment. Method Words were presented to 3 participant groups at 80 dB in speech-shaped noise at 7 signal-to-noise ratios (−10 to 35 dB). Responses were scored for words and phonemes correct. Results It was not surprising to find that phoneme scores were up to about 30% better than word scores. Word scoring resulted in larger hearing loss effect sizes than phoneme scoring, whereas scoring method did not significantly modify age effect sizes. There were significant effects of hearing loss and some limited effects of age; age effect sizes of about 3 dB and hearing loss effect sizes of more than 10 dB were found. Conclusion Hearing loss is the major factor affecting word and phoneme recognition with a subtle contribution of age. Phoneme scoring may provide several advantages over word scoring. A set of recommended phoneme scoring guidelines is provided.

APA, Harvard, Vancouver, ISO, and other styles

23

Seifert, Markus H. J. "Optimizing the Signal-to-Noise Ratio of Scoring Functions for Protein−Ligand Docking." Journal of Chemical Information and Modeling 48, no. 3 (February 23, 2008): 602–12. http://dx.doi.org/10.1021/ci700345n.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Fernandez-Blanco, Enrique, Daniel Rivero, and Alejandro Pazos. "Convolutional neural networks for sleep stage scoring on a two-channel EEG signal." Soft Computing 24, no. 6 (June 26, 2019): 4067–79. http://dx.doi.org/10.1007/s00500-019-04174-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Leclercq, Yves, Jessica Schrouff, Quentin Noirhomme, Pierre Maquet, and Christophe Phillips. "fMRI Artefact Rejection and Sleep Scoring Toolbox." Computational Intelligence and Neuroscience 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/598206.

Full text

Abstract:

We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FAST”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FAST tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FAST” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FAST” is available for free, under the GNU-GPL.

APA, Harvard, Vancouver, ISO, and other styles

26

Heravi, Mohamad Amin Younessi, Morteza Pishghadam, Hosnieh Raoufian, and Akram Gazerani. "RECURRENCE QUANTIFICATION ANALYSIS OF ELECTROOCULOGRAPHY SIGNAL TO A CONTROL QUESTION TEST: A NEW APPROACH FOR THE DETECTION OF DECEPTION." Biomedical Engineering: Applications, Basis and Communications 32, no. 04 (August 2020): 2050029. http://dx.doi.org/10.4015/s1016237220500295.

Full text

Abstract:

This study aimed to evaluate a lie-detection system by nonlinear analysis of electrooculography (EOG) signals in the polygraph test. The physiological signals such as photoplethysmography signal, electrodermal response, respiratory changes as well as EOG signal were recorded based on a Control Question Test (CQT). Three psychophysiological signals were evaluated based on the extracted features in the seven-position numerical scoring. The dynamics of EOG signals in subjects that had a total negative score were analyzed by recurrence quantification analysis (RQA). The six values of RQA were calculated to analyze the EOG signals in relevant questions compared to other questions. A one-way ANOVA with multiple comparisons was performed to evaluate the extracted variables in different questions. Eleven subjects had a total score of [Formula: see text]2 and less, so the EOG signals of these subjects were evaluated. Recurrence plots (RPs) of EOG signals showed clear differences in the two types of questions. The recurrence quantification analysis of vertical EOG signal indicated that [Formula: see text] and determinism (DET) values decreased significantly for relevant questions compared to other questions. Moreover, a significant decrease was observed in all RQA parameters except RR for the horizontal EOG signal. The differences of EOG signals in relevant questions observed using RPs and RQA were possibly related to the underlying changes in rapid eye movement due to the stress. The results of this study illustrate that the RQA technique is well suited to analyze the EOG signals in the detection of deception.

APA, Harvard, Vancouver, ISO, and other styles

27

Guinsburg, Ruth, Maria Fernanda Branco de Almeida, Clóvis de Araújo Peres, Alexandre R. Shinzato, and Benjamin Israel Kopelman. "Reliability of two behavioral tools to assess pain in preterm neonates." Sao Paulo Medical Journal 121, no. 2 (2003): 72–76. http://dx.doi.org/10.1590/s1516-31802003000200008.

Full text

Abstract:

CONTEXT: One of the main difficulties in adequately treating the pain of neonatal patients is the scarcity of validated pain evaluation methods for this population. OBJECTIVE: To analyze the reliability of two behavioral pain scales in neonates. TYPE OF STUDY: Cross-sectional. SETTING: University hospital neonatal intensive care unit. PARTICIPANTS: 22 preterm neonates were studied, with gestational age of 34 ± 2 weeks, birth weight of 1804 ± 584 g, 68% female, 30 ± 12 hours of life, and 30% intubated. PROCEDURES: Two neonatologists (A and B) observed the patients at the bedside and on video films for 10 minutes. The Neonatal Facial Coding System and the Clinical Scoring System were scored at 1, 5, and 10 minutes. The final score was the median of the three values for each observer and scale. A and B were blinded to each other. Video assessments were made three months after bedside evaluations. MAIN MEASUREMENTS: End scores were compared between the observers using the intraclass correlation coefficient and bias analysis (paired t test and signal test). RESULTS: For the Neonatal Facial Coding System, at the bedside and on video, A and B showed a significant correlation of scores (intraclass correlation score: 0.62), without bias between them (t test and signal test: p > 0.05). For the Clinical Scoring System bedside assessment, A and B showed correlation of scores (intraclass correlation score: 0.55), but bias was also detected between them: A scored on average two points higher than B (paired t test and signal test: p < 0.05). For the Clinical Scoring System video assessment, A and B did not show correlation of scores (intraclass correlation score: 0.25), and bias was also detected between them (paired t-test and signal test: p < 0.05). CONCLUSION: The results strengthen the reliability of the Neonatal Facial Coding System for bedside pain assessment in preterm neonates.

APA, Harvard, Vancouver, ISO, and other styles

28

Fernandez-Blanco, Enrique, Daniel Rivero, and Alejandro Pazos. "EEG signal processing with separable convolutional neural network for automatic scoring of sleeping stage." Neurocomputing 410 (October 2020): 220–28. http://dx.doi.org/10.1016/j.neucom.2020.05.085.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Lin, Jhih-rong, and Jianjun Hu. "SeqNLS: Nuclear Localization Signal Prediction Based on Frequent Pattern Mining and Linear Motif Scoring." PLoS ONE 8, no. 10 (October 29, 2013): e76864. http://dx.doi.org/10.1371/journal.pone.0076864.

Full text

APA, Harvard, Vancouver, ISO, and other styles

30

Rolon, R. E., I. E. Gareis, L. D. Larrateguy, L. E. Di Persia, R. D. Spies, and H. L. Rufiner. "Automatic scoring of apnea and hypopnea events using blood oxygen saturation signals." Biomedical Signal Processing and Control 62 (September 2020): 102062. http://dx.doi.org/10.1016/j.bspc.2020.102062.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Gimenez, Javier, Jorge Martinez, and Ana Georgina Flesia. "Unsupervised edge map scoring: A statistical complexity approach." Computer Vision and Image Understanding 122 (May 2014): 131–42. http://dx.doi.org/10.1016/j.cviu.2014.02.005.

Full text

APA, Harvard, Vancouver, ISO, and other styles

32

Wei, E. "Intonation Characteristics of Singing Based on Artificial Intelligence Technology and Its Application in Song-on-Demand Scoring System." Mathematical Problems in Engineering 2021 (March 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/5510401.

Full text

Abstract:

With the continuous progress of my country’s cultural industry, how to apply artificial intelligence technology to song on demand has become an issue of concern. This research mainly discusses the research of singing intonation characteristics based on artificial intelligence technology and its application in song-on-demand scoring system. This paper uses the combination of ant colony algorithm and DTW algorithm to measure the similarity between speech signals with the average distortion distance, so as to expect accurate recognition results. The design of the song-on-demand scoring function module uses a combination of MVC mode and command mode based on artificial intelligence technology. The view component in the MVC mode is mainly used to display the content that the user needs to sing and realize the interaction with the user. The singer selects a song to start playing, and the scoring terminal device queries the music library server for song information according to the song number, then starts playing the song through the FTP file sharing service according to the audio file path in the song information, and at the same time displays the song on the display according to the timeline Show song and pitch information. The singer sings according to the screen prompts. The microphone collects the voice signal and transmits it to the scoring terminal. After the scoring algorithm is calculated, the result is fed back to the screen in real time. The singer can view his singing status in real time and make corresponding adjustments to obtain a higher score. After the singing, the scoring terminal will display the final result on the screen to inform the user and upload the singing record to the server for recording. In the tested on-demand retrieval engine, the average hit rate of the top 3 has reached more than 90% under various humming methods, basically maintaining the high hit rate characteristics of the original retrieval engine. The system designed in this research helps to effectively improve the singing level.

APA, Harvard, Vancouver, ISO, and other styles

33

Zhu, Tianqi, Wei Luo, and Feng Yu. "Convolution- and Attention-Based Neural Network for Automated Sleep Stage Classification." International Journal of Environmental Research and Public Health 17, no. 11 (June 10, 2020): 4152. http://dx.doi.org/10.3390/ijerph17114152.

Full text

Abstract:

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health; however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an experienced medical expert. When scoring sleep epochs, experts pay attention to find specific signal characteristics (e.g., K-complexes and spindles), and sometimes need to integrate information from preceding and subsequent epochs in order to make a decision. To imitate this process and to build a more interpretable deep learning model, we propose a neural network based on a convolutional network (CNN) and attention mechanism to perform automatic sleep staging. The CNN learns local signal characteristics, and the attention mechanism excels in learning inter- and intra-epoch features. In experiments on the public sleep-edf and sleep-edfx databases with different training and testing set partitioning methods, our model achieved overall accuracies of 93.7% and 82.8%, and macro-average F1-scores of 84.5 and 77.8, respectively, outperforming recently reported machine learning-based methods.

APA, Harvard, Vancouver, ISO, and other styles

34

Thorey, V., A. Guillot, K. El Kanbi, M. Harris, and P. J. Arnal. "1211 Assessing the Accuracy of a Dry-EEG Headband for Measuring Brain Activity, Heart Rate, Breathing and Automatic Sleep Staging." Sleep 43, Supplement_1 (April 2020): A463. http://dx.doi.org/10.1093/sleep/zsaa056.1205.

Full text

Abstract:

Abstract Introduction The development of new sleep study devices, adapted for daily use, is necessary for diagnosis of sleep disorders. However, this requires to be both suitable for daily use and capable of recording accurate electrophysiological data. This study assesses the signal acquisition of a comfortable sleep headband, using dry electrodes, and the performance of its automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 4 sleep experts. Methods 42 participants slept at a sleep center wearing both the Dreem headband (DH) and a PSG simultaneously. We measured 1) the EEG signal similarity between both devices, 2) heart rate, breathing frequency and respiration rate variability (RRV) agreement, and 3) the performance of the headband automatic sleep scoring compared to PSG sleep experts manual scoring. Results Results demonstrate a strong correlation between the EEG signals acquired by the headband and those from the PSG, and the signals acquired by the headband enable monitoring of alpha (r= 0.75 ± 0.11), beta (r= 0.74 ± 0.14), delta (r = 0.78 ± 0.16), and theta (r = 0.63 ± 0.15) frequencies during sleep. The mean absolute error for heart rate, breathing frequency, and RRV was 2.2 ± 0.8 bpm, 0.3 ± 0.2 cpm and 3.1 ± 0.4 %, respectively. Automatic Sleep Staging reached an overall accuracy of 84.1 ± 7.5% (F1 score: 83.0 ± 8.4) for the headband to be compared with an average of 86.4 ± 5.5% (F1 score: 86.5 ± 5.5) for the 4 sleep experts. Conclusion These results demonstrate the capacity of the headband to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies. Support This Study has been supported by Dreem sas.

APA, Harvard, Vancouver, ISO, and other styles

35

M Tasbolatov, N. Mekebayev, O. Mamyrbayev, M. Turdalyuly, D. Oralbekova,. "Algorithms and architectures of speech recognition systems." Psychology and Education Journal 58, no. 2 (February 20, 2021): 6497–501. http://dx.doi.org/10.17762/pae.v58i2.3182.

Full text

Abstract:

Digital processing of speech signal and the voice recognition algorithm is very important for fast and accurate automatic scoring of the recognition technology. A voice is a signal of infinite information. The direct analysis and synthesis of a complex speech signal is due to the fact that the information is contained in the signal. Speech is the most natural way of communicating people. The task of speech recognition is to convert speech into a sequence of words using a computer program. This article presents an algorithm of extracting MFCC for speech recognition. The MFCC algorithm reduces the processing power by 53% compared to the conventional algorithm. Automatic speech recognition using Matlab.

APA, Harvard, Vancouver, ISO, and other styles

36

Jean-Louis, Girardin, Hans von Gizycki, Ferdinand Zizi, Peter Hauri, Arthur Spielman, and Harvey Taub. "The Actigraph Data Analysis Software: I. A Novel Approach to Scoring and Interpreting Sleep-Wake Activity." Perceptual and Motor Skills 85, no. 1 (August 1997): 207–16. http://dx.doi.org/10.2466/pms.1997.85.1.207.

Full text

Abstract:

Decades of empirical observations have established the validity of actigraphy primarily in individuals without sleep disorders. Methodological problems encountered thus far coupled with the widespread use of actigraphy signal the need for concentrated efforts to establish a consensus regarding scoring procedures. Currently available scoring methods show less reliability in clinical populations. To address these issues two validation studies were conducted: one for individuals without sleep disorders and the other for patients diagnosed with insomnia. The results of these two studies using the Actigraph Data Analysis Software as the scoring method have shown that the described system is fairly precise. It can be used for actigraphs with different features and mode of operation and is applicable to individuals with insomnia. These findings corroborate previous research showing that actigraphy is a valid instrument for assessment of sleep and wakefulness.

APA, Harvard, Vancouver, ISO, and other styles

37

Sönmez, Kemal, Larry Heck, and Mitchel Weintraub. "Multiple Speaker Tracking and Detection: Handset Normalization and Duration Scoring." Digital Signal Processing 10, no. 1-3 (January 2000): 133–42. http://dx.doi.org/10.1006/dspr.1999.0368.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Rostoker, Guy, Mireille Laroudie, Raphaël Blanc, Mireille Griuncelli, Christelle Loridon, Fanny Lepeytre, Clémentine Rabaté, and Yves Cohen. "Histological Scores Validate the Accuracy of Hepatic Iron Load Measured by Signal Intensity Ratio and R2* Relaxometry MRI in Dialysis Patients." Journal of Clinical Medicine 9, no. 1 (December 19, 2019): 17. http://dx.doi.org/10.3390/jcm9010017.

Full text

Abstract:

Almost all haemodialysis patients are treated with parenteral iron to compensate for blood loss and to allow the full therapeutic effect of erythropoiesis-stimulating agents. Iron overload is an increasingly recognised clinical situation diagnosed by quantitative magnetic resonance imaging (MRI). MRI methods have not been fully validated in dialysis patients. We compared Deugnier’s and Turlin’s histological scoring of iron overload and Scheuer’s classification (with Perls’ stain) with three quantitative MRI methods for measuring liver iron concentration (LIC)—signal intensity ratio (SIR), R2* relaxometry, and R2* multi-peak spectral modelling (Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL-IQ®)) relaxometry—in 16 haemodialysis patients in whom a liver biopsy was formally indicated for medical follow-up. LIC MRI with these three different methods was highly correlated with Deugnier’s and Turlin’s histological scoring (SIR: r = 0.8329, p = 0.0002; R2* relaxometry: r = −0.9099, p < 0.0001; R2* relaxometry (IDEAL-IQ®): r = −0.872, p = 0.0018). Scheuer’s classification was also significantly correlated with these three MRI techniques. The positive likelihood ratio for the diagnosis of abnormal LIC by Deugnier’s histological scoring was > 62 for the three MRI methods. This study supports the accuracy of quantitative MRI methods for the non-invasive diagnosis and follow-up of iron overload in haemodialysis patients.

APA, Harvard, Vancouver, ISO, and other styles

39

Peker, Musa. "An efficient sleep scoring system based on EEG signal using complex-valued machine learning algorithms." Neurocomputing 207 (September 2016): 165–77. http://dx.doi.org/10.1016/j.neucom.2016.04.049.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Jin, Jiayi, and Edgar Sanchez-Sinencio. "A Home Sleep Apnea Screening Device With Time-Domain Signal Processing and Autonomous Scoring Capability." IEEE Transactions on Biomedical Circuits and Systems 9, no. 1 (February 2015): 96–104. http://dx.doi.org/10.1109/tbcas.2014.2314301.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Sors, Arnaud, Stéphane Bonnet, Sébastien Mirek, Laurent Vercueil, and Jean-François Payen. "A convolutional neural network for sleep stage scoring from raw single-channel EEG." Biomedical Signal Processing and Control 42 (April 2018): 107–14. http://dx.doi.org/10.1016/j.bspc.2017.12.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Al-Shawwa, B., J. Cruz, Z. Ehsan, and D. G. Ingram. "0889 Utility of Pulse Wave Amplitude in Detecting Arousals in Children." Sleep 43, Supplement_1 (April 2020): A338—A339. http://dx.doi.org/10.1093/sleep/zsaa056.885.

Full text

Abstract:

Abstract Introduction Identifying and scoring arousals are crucial to score hypopneas and respiratory efforts related arousals (RERAs). At the same time, children’s sleep architecture appears to be more stable compared to adults which makes it more difficult to score arousals. Pulse wave amplitude (PWA) is a signal obtained from finger photoplethysmography which is directly and positively correlated to finger blood flow. It is also used as a marker for finger vasoconstriction reflected by decreased signal amplitude and as a surrogate for autonomic and cortical arousals. Our aim was to study the effect of scoring arousals based on PWA on scoring of respiratory events in pediatric patients. Methods Ten polysomnograms for patients between the ages of 5-15 years who had apnea-hypopnea indices between 1 and 3 events/hour were identified. Patients with syndromes were excluded. A drop in PWA signal of at least 30% that lasted for 3 seconds was needed to identify subcortical/autonomic arousals. Arousals were rescored based on this criteria and subsequently respiratory events were rescored. Paired t-tests were employed to compare PSG indices scored with or without PWA incorporation. Results The sample of 10 children included 2 females, and the average age was 9.8 +/-3.1 years. Overall, polysomnography revealed an average total sleep time of 464.1 +/-25 mins, sleep efficiency of 92% +/-4.2, sleep latency of 19.6 +/-17.0 mins, rapid eye movement (REM) latency 143 +/-66 mins, N1 3.9% +/-2.0, N2 50.3% +/-12.0, N3 28.2% +/-9.1, REM 16.7% +/-4.0, and wakefulness after sleep onset (WASO) 18.1 +/-7.5 mins. Including arousals from PWA changes, respiratory indices significantly increased including total AHI (2.3 +/-0.7 vs 5.7 +/-2.1, p<0.001), obstructive AHI (1.45 +/-0.7 vs 4.8 +/-1.8, p<0.001), and RDI (2.36 +/-0.7 vs 7.6 +/-2.0, p<0.001). Likewise, total arousal index was significantly higher (8.7 +/-2.3 vs 29.4 +/-6.5, p<0.001). Conclusion Pulse wave amplitude is a useful marker for arousals that are not otherwise easily identified in pediatric polysomnography. This in turn would lead to a more accurate scoring of respiratory events and severity assessment of sleep disordered breathing. This likely will have clinical and therapeutic implications. Support None

APA, Harvard, Vancouver, ISO, and other styles

43

Otobo, T. M., N. Herregods, J. L. Jaremko, R. G. Lambert, I. Sudoł-Szopińska, A. B. Meyers, E. Kirkhus, et al. "POS1323 SACROILIAC JOINT MRI ABNORMALITIES IN JUVENILE SPONDYLOARTHRITIS: AN UPDATE OF DEFINITIONS AND SCORING OF THE OMERACT JUVENILE IDIOPATHIC ARTHRITIS MRI SCORE." Annals of the Rheumatic Diseases 80, Suppl 1 (May 19, 2021): 943.3–944. http://dx.doi.org/10.1136/annrheumdis-2021-eular.3321.

Full text

Abstract:

Background:Preliminary definitions for SIJ lesions in the OMERACT Juvenile Idiopathic Arthritis Magnetic Resonance Imaging score has been reported1. Investigators identified the need to revise the JAMRIS-SIJ item definitions.Objectives:To update the JAMRIS-SIJ definitions and scoring method.Methods:The OMERACT JAMRI working group was convened to discuss the performance of the score in a reliability exercise using 30 patients. Twenty investigators (12 radiologists, 8 rheumatologists) decided which definitions and scoring methods to be revised, retained or added.Results:The revised JAMRI-SIJ is in the Table 1.Table 1.Revised OMERACT JAMRIS-SIJ.ComponentDefinitionSegmentation/sliceScore range/sliceBone Marrow Edema (BME)An ill-defined area of high bone marrow signal intensity within the subchondral bone in the ilium or sacrum on fluid sensitive images4 quadrants/SIJ0-8BME IntensityPresence of hyperintensity of the marrow on fluid sensitive images using the signal of the presacral veins or cerebrospinal fluid as reference1 score/SIJ0-2BME DepthContinuing increased signal on fluid sensitive images of depth ≥ 5mm/ ≥ 1cm from the articular surface using the signal of the presacral veins or cerebrospinal fluid as reference1 score/SIJ0-2OsteitisAn ill-defined area of high bone marrow signal intensity within the subchondral bone in the ilium or sacrum on contrast enhanced T1 weighted sequences4 quadrants/SIJ0-8CapsulitisIncreased signal on fluid sensitive or contrast enhanced T1 weighted sequences involving the superior portion of the SIJ capsulesuperior halves/SIJ0-2Joint space fluidHigh signal intensity equivalent to the CSF on fluid sensitive sequences within the joint space of the cartilaginous portion of the SIJhalves/SIJ0-4Joint space enhancementIncreased signal intensity on contrast enhanced T1 weighted sequences within the joint space of the cartilaginous portion of the SIJhalves/SIJ0-4Inflammation in erosion cavityIncreased signal intensity on fluid sensitive or contrast enhanced T1 weighted sequences in an erosion cavity of the cartilaginous portion of the SIJhalves/SIJ0-4EnthesitisIncreased signal intensity in bone marrow and/or adjacent soft tissue on fluid sensitive or contrast enhanced T1 weighted sequences at sites where ligaments and tendons attach to a bone excluding retroarticular enthesitisScore per case0-1Damage DomainSclerosisA substantially wider than normal area of very low bone marrow signal intensity within the subchondral bone in the ilium or sacrum on a non-fat suppressed sequence, preferably a non-fat suppressed T1 weighted sequence. This feature must also be present on all other sequences, as available4 quadrants/SIJ0-8ErosionA focal loss of the low signal of cortical bone at the osteochondral interface and adjacent marrow matrix on T1 weighted images4 quadrants/SIJ0-8Fat metaplasia lesionHomogeneous increased signal intensity within the subchondral bone marrow on T1weighted images4 quadrants/SIJ0-8BackfillA high signal on non-contrast enhanced T1 weighted sequences in a typical location for an erosion, with signal intensity greater than normal bone marrow, clearly demarcated from adjacent bone marrow by an irregular band of low signal reflecting sclerosis at the border of the original erosionhalves/SIJ0-4AnkylosisPresence of signal equivalent to regional bone marrow continuously bridging a portion of the joint space between the iliac and sacral boneshalves/SIJ0-4Statement of overarching consideration for all definitions: “[…] in comparison to physiological changes normally seen on MRI examinations of age- and sex-matched children, and visible in 2 planes wherever available.”Conclusion:Revised JAMRIS-SIJ has been developed. Validation steps are underway.References:[1]Otobo TM, et al. Preliminary Definitions for Sacroiliac Joint Pathologies in the OMERACT Juvenile Idiopathic Arthritis Magnetic Resonance Imaging Score (OMERACT JAMRIS-SIJ). The Journal of rheumatology. 2019;46(9):1192-7.Acknowledgements:The authors acknowledge The Hospital for SickKids Research Trainee Competition (RESTRACOMP) and Queen Elizabeth II/Edward Dunlop Foundation Scholarship In Science and Technology (QEII-GSST) at the University of Toronto for funding provided to Dr. Tarimobo M. Otobo. The authors also acknowledge Prof. Dr. Desiree van der Heijde for providing expert commentary.Disclosure of Interests:None declared

APA, Harvard, Vancouver, ISO, and other styles

44

Barouni, Amna, Jörg Ottenbacher, Johannes Schneider, Bernd Feige, Dieter Riemann, Anne Herlan, Driss El Hardouz, and Darren McLennan. "Ambulatory sleep scoring using accelerometers—distinguishing between nonwear and sleep/wake states." PeerJ 8 (January 2, 2020): e8284. http://dx.doi.org/10.7717/peerj.8284.

Full text

Abstract:

Background Differentiating nonwear time from sleep and wake times is essential for the estimation of sleep duration based on actigraphy data. To efficiently analyze large-scale data sets, an automatic method of identifying these three different states is required. Therefore, we developed a classification algorithm to determine nonwear, sleep and wake periods from accelerometer data. Our work aimed to (I) develop a new pattern recognition algorithm for identifying nonwear periods from actigraphy data based on the influence of respiration rate on the power spectrum of the acceleration signal and implement it in an automatic classification algorithm for nonwear/sleep/wake states; (II) address motion artifacts that occur during nonwear periods and are known to cause misclassification of these periods; (III) adjust the algorithm depending on the sensor position (wrist, chest); and (IV) validate the algorithm on both healthy individuals and patients with sleep disorders. Methods The study involved 98 participants who wore wrist and chest acceleration sensors for one day of measurements. They spent one night in the sleep laboratory and continued to wear the sensors outside of the laboratory for the remainder of the day. The results of the classification algorithm were compared to those of the reference source: polysomnography for wake/sleep and manual annotations for nonwear/wear classification. Results The median kappa values for the two locations were 0.83 (wrist) and 0.84 (chest). The level of agreement did not vary significantly by sleep health (good sleepers vs. subjects with sleep disorders) (p = 0.348, p = 0.118) or by sex (p = 0.442, p = 0.456). The intraclass correlation coefficients of nonwear total time between the reference and the algorithm were 0.92 and 0.97 with the outliers and 0.95 and 0.98 after the outliers were removed for the wrist and chest, respectively. There was no evidence of an association between the mean difference (and 95% limits of agreement) and the mean of the two methods for either sensor position (wrist p = 0.110, chest p = 0.164), and the mean differences (algorithm minus reference) were 5.11 [95% LoA −15.4–25.7] and 1.32 [95% LoA −9.59–12.24] min/day, respectively, after the outliers were removed. Discussion We studied the influence of the respiration wave on the power spectrum of the acceleration signal for the differentiation of nonwear periods from sleep and wake periods. The algorithm combined both spectral analysis of the acceleration signal and rescoring. Based on the Bland-Altman analysis, the chest-worn accelerometer showed better results than the wrist-worn accelerometer.

APA, Harvard, Vancouver, ISO, and other styles

45

Al Nazi, Zabir, A. B. M. Aowlad Hossain, and Md Monirul Islam. "Motor Imagery EEG Classification Using Random Subspace Ensemble Network with Variable Length Features." International Journal Bioautomation 25, no. 1 (March 2021): 13–24. http://dx.doi.org/10.7546/ijba.2021.25.1.000611.

Full text

Abstract:

Classification of electroencephalography (EEG) signals for brain-computer interface has great impact on people having various kinds of physical disabilities. Motor imagery EEG signals of hand and leg movement classification can help people whose limbs are replaced by prosthetics. In this paper, random subspace ensemble network with variable length feature sampling has been proposed for improving the prediction accuracy of motor imagery EEG signal classification. The method has been tested on eight different subjects and a hybrid dataset of two subjects data combined. Discrete wavelet transform based de-noising scheme has been adopted to remove artifacts from the EEG signal. For sub-band selection, dual-tree complex wavelet Transform has been employed. Mutual information scoring has been used for univariate feature selection from the feature space. A comparative analysis has been carried out where random subspace ensemble network outperformed other classification models. The maximum accuracy obtained by the model was 90.00%. Furthermore, the model showed better performance on the hybrid dataset with an average accuracy of 86.00%. The findings of this study are expected to be useful in artificial limb movements through brain-computer interfacing for rehabilitation of people with such physical disabilities.

APA, Harvard, Vancouver, ISO, and other styles

46

Drummond, Gordon B., Darius Fischer, Margaret Lees, Andrew Bates, Janek Mann, and D. K. Arvind. "Classifying signals from a wearable accelerometer device to measure respiratory rate." ERJ Open Research 7, no. 2 (March 18, 2021): 00681–2020. http://dx.doi.org/10.1183/23120541.00681-2020.

Full text

Abstract:

BackgroundAutomatic measurement of respiratory rate in general hospital patients is difficult. Patient movement degrades the signal and variation of the breathing cycle means that accurate observation for ≥60 s is needed for adequate precision.MethodsWe studied acutely ill patients recently admitted to a teaching hospital. Breath duration was measured from a triaxial accelerometer attached to the chest wall and compared with a signal from a nasal cannula. We randomly divided the patient records into a training (n=54) and a test set (n=7). We used machine learning to train a neural network to select reliable signals, automatically identifying signal features associated with accurate measurement of respiratory rate. We used the test records to assess the accuracy of the device, indicated by the median absolute difference between respiratory rates, provided by the accelerometer and by the nasal cannula.ResultsIn the test set of patients, machine classification of the respiratory signal reduced the median absolute difference (interquartile range) from 1.25 (0.56–2.18) to 0.48 (0.30–0.78) breaths per min. 50% of the recording periods were rejected as unreliable and in one patient, only 10% of the signal time was classified as reliable. However, even only 10% of observation time would allow accurate measurement for 6 min in an hour of recording, giving greater reliability than nurse charting, which is based on much less observation time.ConclusionSignals from a body-mounted accelerometer yield accurate measures of respiratory rate, which could improve automatic illness scoring in adult hospital patients.

APA, Harvard, Vancouver, ISO, and other styles

47

Miwa, Shinji, Norio Yamamoto, Katsuhiro Hayashi, Akihiko Takeuchi, Kentaro Igarashi, Kaoru Tada, Hirotaka Yonezawa, et al. "A Radiological Scoring System for Differentiation between Enchondroma and Chondrosarcoma." Cancers 13, no. 14 (July 16, 2021): 3558. http://dx.doi.org/10.3390/cancers13143558.

Full text

Abstract:

Background: It is challenging to differentiate between enchondromas and atypical cartilaginous tumors (ACTs)/chondrosarcomas. In this study, correlations between radiological findings and final diagnosis were investigated in patients with central cartilaginous tumors. Methods: To evaluate the diagnostic usefulness of radiological findings, correlations between various radiological findings and final diagnoses were investigated in a cohort of 81 patients. Furthermore, a new radiological scoring system was developed by combining radiological findings. Results: Periosteal reaction on X-ray (p = 0.025), endosteal scalloping (p = 0.010) and cortical defect (p = 0.002) on CT, extraskeletal mass (p < 0.001), multilobular lesion (p < 0.001), abnormal signal in adjacent tissue (p = 0.004) on MRI, and increased uptake in bone scan (p = 0.002) and thallium scan (p = 0.027) was significantly correlated with final diagnoses. Based on the correlations between each radiological finding and postoperative histological diagnosis, a radiological scoring system combining these findings was developed. In another cohort of 17 patients, the sensitivity, specificity, and accuracy of the radiological score rates for differentiation between enchondromas and ACTs/chondrosarcomas were 88%, 89%, and 88%, respectively (p = 0.003). Conclusion: Radiological assessment with combined radiological findings is recommended to differentiate between enchondromas and ACT/chondrosarcomas.

APA, Harvard, Vancouver, ISO, and other styles

48

Okorn, Kristen, and Michael Hannigan. "Improving Air Pollutant Metal Oxide Sensor Quantification Practices through: An Exploration of Sensor Signal Normalization, Multi-Sensor and Universal Calibration Model Generation, and Physical Factors Such as Co-Location Duration and Sensor Age." Atmosphere 12, no. 5 (May 19, 2021): 645. http://dx.doi.org/10.3390/atmos12050645.

Full text

Abstract:

As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.

APA, Harvard, Vancouver, ISO, and other styles

49

He, Zhiyang, Weidong Cheng, and Weigang Wen. "A Similarity Comparison Method of Homologous Fault Response Fragments under Variable Rotational Speed." Shock and Vibration 2020 (January 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/8973678.

Full text

Abstract:

Effective fault detection and diagnosis (FDD) for rotating machinery is always a focus issue in improving the prognostic and health management (PHM) of the equipments. The existing usage of similarity measurement has been widely spread in searching the homologous fault responses from vibration signals, but most of them are just suitable for stable speed and cannot be applied in all variable speed conditions. In order to improve measurement performance, a fast-meshed phase portrait (FMPP) frame combining the phase-space technique and box-scoring calculation is proposed. Firstly, the variable-speed signal is divided into multiple undetermined fragments according to fault characteristic orders (FCOs). Secondly, the undetermined fragments are reconstructed into corresponding phase-space trajectories to overcome the time-delay matching inconsistency of the variable speed fragments. Thirdly, the phase-space trajectories are mapped into meshed phase portraits via box-scoring calculation. Such decisive calculation can effectively transforms the diverse unequal fragments into the phase diagrams with same size, which saves time for the subsequent similarity measurement. Finally, the proposed FMPP is tested both for accuracy and timeliness on a self-built bearing bench, where the order tracking (OT) and dynamic time warping (DTW) methods are used for the comparisons. The experiments proved the effectiveness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

50

Hwang, Su, Yu Lee, Do Jeong, and Kwang Park. "Unconstrained Sleep Stage Estimation Based on Respiratory Dynamics and Body Movement." Methods of Information in Medicine 55, no. 06 (2016): 545–55. http://dx.doi.org/10.3414/me15-01-0140.

Full text

Abstract:

SummaryObjectives: The aim of this study is to establish a sleep monitoring method that can classify sleep into four stages in an unconstrained manner using a polyvinylidene fluoride (PVDF) sensor for continuous and accurate estimation of sleep stages.Methods: The study participants consisted of 12 normal subjects and 13 obstructive sleep apnea (OSA) patients. The physiological signals of the subjects were unconstrainedly measured using the PVDF sensor during polysomnography. The respiration and body movement signals were extracted from the PVDF data. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respira -tory rate was chosen as an indicator for slow-wave sleep (SWS) detection. Sleep was divided into four stages (wake, light, SWS, and REM) based on the detection results.Results: The performance of the method was assessed by comparing the results with a manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with an average accuracy of 70.9 % and kappa statistics of 0.48. No significant differences were observed in the detection performance between the normal and OSA groups.Conclusions: The developed system and methods can be applied to a home sleep monitoring system.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Signal scoring'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles