scholarly journals Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Sweeti ◽  
Deepak Joshi ◽  
B. K. Panigrahi ◽  
Sneh Anand ◽  
Jayasree Santhosh
Keyword(s):  
Classification Accuracy ◽  
Statistical Significance ◽  
Repeated Measure ◽  
Stepwise Discriminant Analysis ◽  
Attention Task ◽  
Time Frequency ◽  
Signal Features ◽  
Measure Analysis ◽  
Task Conditions ◽  
Visual Hemifields

This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.

Do longitudinal orthodontic trials use appropriate statistical analyses? A meta-epidemiological study

2021 ◽  
Author(s):  
Samer Mheissen ◽  
Haris Khan ◽  
Mohammed Almuzian ◽  
Emad Eddin Alzoubi ◽  
Nikolaos Pandis
Keyword(s):  
Statistical Analysis ◽  
Epidemiological Study ◽  
Data Extraction ◽  
Statistical Significance ◽  
Repeated Measure ◽  
Exact Test ◽  
Optimal Analysis ◽  
Study Characteristics ◽  
Measure Analysis ◽  
Repeated Measure Analysis

Summary Background In orthodontic trials, longitudinal designs with multiple outcome measurements over time are common. The aim of this epidemiological study was to examine whether optimal statistical analysis approaches have been used in longitudinal orthodontic trials. Methods Pubmed was searched in August 2021 for longitudinal orthodontic trials with at least three time points of outcome assessment published in the 2017–20 period. Study selection and data extraction were done independently and in duplicate. The analysis approaches undertaken were tabulated and associations between study characteristics and the use of optimal analysis or not were assessed using Fisher’s exact test and logistic regression. Results One hundred forty-seven out of 563 unique records were deemed eligible for inclusion. Only 26.50% of these trials used an optimal statistical analysis for longitudinal data where the data structure is accounted for. None of the study characteristics except the statistical significance of the results were associated with the appropriateness of the statistical analysis. The odds of significant results in studies with suboptimal analyses were higher than that in studies with optimal longitudinal analyses (odds ratio: 3.48, 95% confidence interval: 1.62, 7.46, P = 0.001). For the studies with optimal analysis, the most frequent test was repeated-measure analysis of variance (RM-ANOVA). The reporting of the statistical analysis section was suboptimal in the majority of the trials. Conclusion Most longitudinal orthodontic trials are not analysed using optimal statistical approaches. Inferences and interpretation of their results are likely to be compromised.

scholarly journals Comparison of Mean Platelet Counts in Preterm Infants with and without Retinopathy of Prematurity

2021 ◽  
Vol 18 (7) ◽  
pp. 3783
Author(s):  
Zi Di Lim ◽  
Edwin Pheng ◽  
Evelyn Tai Li Min ◽  
Hans Van Rostenberghe ◽  
Ismail Shatriah
Keyword(s):  
Risk Factors ◽  
Retinopathy Of Prematurity ◽  
Primary Source ◽  
Analysis Of Covariance ◽  
Repeated Measure ◽  
Platelet Counts ◽  
Related Risk ◽  
Measure Analysis ◽  
Repeated Measure Analysis ◽  
Over Time

Platelets are a primary source of pro- and anti-angiogenic cytokines. However, the evidence of their role in retinopathy of prematurity (ROP) is controversial. This retrospective study aimed to compare mean weekly platelet counts between infants with and without ROP over the first 6 weeks of life. A total of 93 infants matched by gestational age and birth weight were recruited (31 with ROP, 62 without ROP). Weekly mean platelet counts and other related risk factors were documented. The repeated measure analysis of variance (ANOVA) and the repeated measure analysis of covariance (ANCOVA) were used to compare mean platelet counts over time between the two groups, with and without adjusting for confounders. We found significant differences in the weekly mean platelet counts of infants with and without ROP over the first 6 weeks of life (p = 0.002). These differences disappeared after adjusting for covariates (p = 0.489). Lower mean platelet counts in ROP infants are not directly related to ROP, but rather to the presence of other risk factors for ROP, such as culture-proven sepsis, blood transfusion and bronchopulmonary dysplasia.

scholarly journals Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 231
Author(s):  
Weiheng Jiang ◽  
Xiaogang Wu ◽  
Yimou Wang ◽  
Bolin Chen ◽  
Wenjiang Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Classification Accuracy ◽  
Communication Systems ◽  
Mimo Systems ◽  
Decision Fusion ◽  
Frequency Characteristics ◽  
Modulation Classification ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Average Classification Accuracy

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.

scholarly journals Which Douglas-Fir ( Pseudotsuga menziesii (Mirb.) Franco) Provenances Provide the Best Productivity in the Hilly Area of Croatia?

2019 ◽  
Vol 10 (1) ◽  
pp. 9-17
Author(s):  
Martina Đodan ◽  
Tomislav Dubravac ◽  
Sanja Perić
Keyword(s):  
Growth Dynamics ◽  
Development Stage ◽  
Douglas Fir ◽  
Repeated Measure ◽  
Published Data ◽  
Stand Development ◽  
Hilly Area ◽  
First Results ◽  
Measure Analysis ◽  
Repeated Measure Analysis

Background and Purpose: Recently raised questions on adaptability of native tree species to climate changes pointed to Douglas-fir as a species suitable for rapid reforestation and increase of stand resistance. The first results on provenance research need to be confirmed in later stages of stand development, so the paper answers the following two questions: (i) are there differences in growth of 14 Douglas-fir provenances still in the fifth decade of stand development, and (ii) which provenances should be used and which omitted from further use in the hilly area of Croatia? Materials and Methods: Productivity of 14 provenances was evaluated on the basis of height, diameter at breast height and volume in the 46th year after planting. Growth dynamics was also statistically analysed using a repeated measure analysis of variance, for which purpose we partially used published data from the 2010. Results: The analysis excluded Castle Rock and Shady Cove (Oregon) provenances due to their low values of all analysed growth indicators, as well as Castle Rock, Elma and Hvidilde provenances due to their high values. Average values of tree volume ranged from 0.53 m3 (Shady Cove) to 2.05 m3 (Castle Rock), while the tallest trees belonged to Elma provenance (29.6 m). Conclusions: Different growth dynamics of provenances were confirmed for later development stage, so further monitoring is still required. Clear guidelines for the selection of provenances for practical forestry distinguish provenances from lower altitudes of the State of Washington, Denmark and Bulgaria as the most productive. Shady Cove and Salmon Arm provenances are not advised to be used in the future.

scholarly journals The Significance of Neural Inter-Frequency Correlations

2021 ◽  
Author(s):  
Oscar Wiljam Savolainen
Keyword(s):  
Multiple Testing ◽  
Statistical Significance ◽  
Significance Test ◽  
Stationary Time Series ◽  
Frequency Bands ◽  
Neural Recordings ◽  
Time Frequency ◽  
Common Information ◽  
Neural Processes ◽  
Long Term Trends

Abstract It is of great interest in neuroscience to determine what frequency bands in the brain contain common information. However, to date, a comprehensive statistical approach to this question has been lacking. As such, this work presents a novel statistical significance test for correlated power across frequency bands in non-stationary time series. The test accounts for biases that often go untreated in time-frequency analysis, i.e. intra-frequency autocorrelation, inter-frequency non-dyadicity, and multiple testing under dependency. It is used to test all of the inter-frequency correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings, using a very large, publicly available dataset. The results show that neural processes have signatures across a very broad range of frequency bands. In particular, LFP frequency bands as low as 20 Hz were found to almost always be significantly correlated to kHz frequency ranges. This test also has applications in a broad range of fields, e.g. biological signal processing, economics, finance, climatology, etc. It is useful whenever one wants to robustly determine whether short-term components in a signal are robustly related to long-term trends, or what frequencies contain common information.

scholarly journals Classification of Emotional States Inparkinson’s Disease Patients Using Time,Frequency and Time-Frequency Analysis

2021 ◽  
Author(s):  
Rejith K.N ◽  
Kamalraj Subramaniam ◽  
Ayyem Pillai Vasudevan Pillai ◽  
Roshini T V ◽  
Renjith V. Ravi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Classification Accuracy ◽  
Machine Learning Algorithms ◽  
Signal Frequency ◽  
Spectral Energy ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Frequency ◽  
Energy Entropy ◽  
Teager Energy

Abstract In this work, PD patients and healthy individuals were categorized with machine-learning algorithms. EEG signals associated with six different emotions, (Happiness(E1), Sadness(E2), Fear(E3), Anger(E4), Surprise,(E5) and disgust(E6)) were used for the study. EEG data were collected from 20 PD patients and 20 normal controls using multimodal stimuli. Different features were used to categorize emotional data. Emotional recognition in Parkinson’s disease (PD) has been investigated in three domains namely, time, frequency and time frequency using Entropy, Energy-Entropy and Teager Energy-Entropy features. Three classifiers namely, K-Nearest Neighbor Algorithm, Support Vector Machine and Probabilistic Neural Network were used to observethe classification results. Emotional EEG stimuli such as anger, surprise, happiness, sadness, fear, and disgust were used to categorize PD patients and healthy controls (HC). For each EEG signal, frequency features corresponding to alpha, beta and gamma bands were obtained for nine feature extraction methods (Entropy, Energy Entropy, Teager Energy Entropy, Spectral Entropy, Spectral Energy-Entropy, Spectral Teager Energy-Entropy, STFT Entropy, STFT Energy-Entropy and STFT Teager Energy-Entropy). From the analysis, it is observed that the entropy feature in frequency domain performs evenly well (above 80 %) for all six emotions with KNN. Classification results shows that using the selected energy entropy combination feature in frequency domain provides highest accuracy for all emotions except E1 and E2 for KNN and SVM classifier, whereas other features give accuracy values of above 60% for most emotions.It is also observed that emotion E1 gives above 90 % classification accuracy for all classifiers in time domain.In frequency domain also, emotion E1 gives above 90% classification accuracy using PNN classifier.

scholarly journals Effects of intramuscular alfaxalone-midazolam combination in pigs

2019 ◽  
Vol 88 (2) ◽  
pp. 187-192 ◽  
Author(s):  
Cecilia Vullo ◽  
Marina Meligrana ◽  
Adolfo Maria Tambella ◽  
Angela Palumbo Piccionello ◽  
Fabrizio Dini ◽  
...  
Keyword(s):  
Repeated Measure ◽  
Intramuscular Administration ◽  
Arterial Oxygen ◽  
Neck Muscle ◽  
Pain On Injection ◽  
Arterial Blood ◽  
Measure Analysis ◽  
American Society ◽  
Lateral Recumbency

The aim of this experimental study was to evaluate the sedative and cardiorespiratory effects of alfaxalone and midazolam after intramuscular administration in pigs. Fourteen pigs, weighing 18 to 22 kg, aged between 55 and 70 days, American Society of Anaesthesiologists classification 2, affected by congenital reducible umbilical hernia, were included in the study. Alfaxalone (5 mg/kg) and midazolam (0.5 mg/kg) mixed in the same syringe were administered into the neck muscle. Pain on injection, quality of sedation and time to achieve lateral recumbency were recorded. Heart rate (HR), respiratory frequency (fR), and rectal temperature (RT) were recorded at 0 (baseline: before drug administration), 10, 15, and 20 min after the injection. Oxygen saturation of haemoglobin (SpO2), arterial blood pH, arterial oxygen (PaO2) and carbon dioxide (PaCO2) tensions and bicarbonate concentration (HCO3-) were recorded at 10, 15, and 20 min after injection. Continuous data were analysed using a repeated-measure analysis of variance (ANOVA) and a P-value < 0.05 was considered significant. Ten animals out of fourteen showed no pain on injection, whereas the remaining four exhibited mild pain. The time from the end of injection to lateral recumbency was 266 ± 40 s. The quality of sedation ranged between good to very good. No significant changes in the variables monitored were observed between the time points. In conclusion, the intramuscular administration of alfaxalone and midazolam in pigs at the doses used induced reliable and fast sedation, without pain on injection and moderate respiratory effects.

Strides to Achieve a Stable Symmetry Index During Running

2021 ◽  
pp. 1-6
Author(s):  
Shane P. Murphy ◽  
Zach B. Barrons ◽  
Jeremy D. Smith
Keyword(s):  
Systematic Difference ◽  
Repeated Measure ◽  
Joint Kinetics ◽  
Lower Extremity Injuries ◽  
Symmetry Index ◽  
Spatiotemporal Parameters ◽  
Minimum Number ◽  
Stable Measures ◽  
Measure Analysis ◽  
Extremity Injuries

Context: The quality of running mechanics is often characterized by limb pattern symmetry and used to support clinical decisions throughout the rehabilitation of lower-extremity injuries. It is valuable to ensure that gait analyses provide stable measures while not asking an individual to complete an excessive number of running strides. The present study aimed to determine the minimum number of strides required to establish a stable mean symmetry index (SMSI) of discrete-level measures of spatiotemporal parameters, joint kinematics, and joint kinetics. Further, the study aimed to determine if differences occurred between random and consecutive strides for directional and absolute symmetry indices. Design: Descriptive laboratory study. Methods: A sequential average was used to determine how many strides were required to achieve a SMSI within a 60-second trial. Multiple 2-factor repeated-measure analysis of variances were used to determine if differences between bins of strides and symmetry calculations were significantly different. Results: A median SMSI was achieved in 15 strides for all biomechanical variables. There were no significant differences (P > .05) found between consecutive and random bins of 15 strides within a 60-second trial. Although there were significant differences between symmetry calculation values for most variables (P < .05), there appeared to be no systematic difference between the numbers of strides required for stable symmetry for either index. Conclusions: As 15 strides were sufficient to achieve a SMSI during running, a continued emphasis should be placed on the number of strides collected when examining interlimb symmetry.

scholarly journals EEG and Deep Learning Based Brain Cognitive Function Classification

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 104
Author(s):  
Saraswati Sridhar ◽  
Vidya Manian
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Cognitive Aging ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Principal Component ◽  
Learning System ◽  
Detection Accuracy ◽  
Band Power ◽  
Signal Features

Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power.

The Influence of School-Based Family Counseling on Elementary Students and Their Families

2020 ◽  
Vol 28 (3) ◽  
pp. 273-282
Author(s):  
Viki P. Kelchner ◽  
Laurie O. Campbell ◽  
Cassandra C. Howard ◽  
Jasmine Bensinger ◽  
Glenn W. Lambie
Keyword(s):  
Family Communication ◽  
Sixth Grade ◽  
Family Counseling ◽  
Repeated Measure ◽  
Title I School ◽  
Counseling Intervention ◽  
Percentile Score ◽  
School Based ◽  
Significant Difference ◽  
Measure Analysis

A family counseling intervention grounded in systemic family therapy was conducted in a Title I school-based setting with ( N = 48) kindergarten through sixth-grade student-clients and their primary caregivers. Families’ perception of family communication and satisfaction on the Family Adaptability and Cohesion Evaluation Scale-IV was investigated to determine changes in the percentile score at three benchmarks. A repeated measure analysis of variance indicated a statistically significant difference over time in caregivers’ perception of family communication and satisfaction after 5 and 10 weeks. There was no difference in relationship to gender. School-based family counseling programs can contribute to improved family communication and satisfaction. School-based counselors can partner with institutions of higher education to provide free and accessible counseling for students and families in the greatest need.

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