scholarly journals Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1311
Author(s):  
Aleksandra Badura ◽  
Aleksandra Masłowska ◽  
Andrzej Myśliwiec ◽  
Ewa Piętka
Keyword(s):  
Self Report ◽  
Classification Model ◽  
Painful Procedure ◽  
Data Set ◽  
Multimodal Data ◽  
Scattering Transform ◽  
Individual Perception ◽  
Pain Recognition ◽  
Scattering Transforms ◽  
Related Reaction

Fascial therapy is an effective, yet painful, procedure. Information about pain level is essential for the physiotherapist to adjust the therapy course and avoid potential tissue damage. We have developed a method for automatic pain-related reaction assessment in physiotherapy due to the subjectivity of a self-report. Based on a multimodal data set, we determine the feature vector, including wavelet scattering transforms coefficients. The AdaBoost classification model distinguishes three levels of reaction (no-pain, moderate pain, and severe pain). Because patients vary in pain reactions and pain resistance, our survey assumes a subject-dependent protocol. The results reflect an individual perception of pain in patients. They also show that multiclass evaluation outperforms the binary recognition.

Prevalence of Self-Reported Depression Symptoms and Perceived Anxiety Among Community-Dwelling U.S. Adults Reporting Tinnitus

2020 ◽  
Vol 5 (4) ◽  
pp. 959-970
Author(s):  
Kelly M. Reavis ◽  
James A. Henry ◽  
Lynn M. Marshall ◽  
Kathleen F. Carlson
Keyword(s):  
Mental Health ◽  
Fold Increase ◽  
Community Dwelling ◽  
Self Report ◽  
Depression Symptoms ◽  
Community Based Interventions ◽  
Data Set ◽  
Health Distress ◽  
Validated Questionnaire ◽  
Mental Health Distress

Purpose The aim of this study was to examine the relationship between tinnitus and self-reported mental health distress, namely, depression symptoms and perceived anxiety, in adults who participated in the National Health and Nutrition Examinations Survey between 2009 and 2012. A secondary aim was to determine if a history of serving in the military modified the associations between tinnitus and mental health distress. Method This was a cross-sectional study design of a national data set that included 5,550 U.S. community-dwelling adults ages 20 years and older, 12.7% of whom were military Veterans. Bivariable and multivariable logistic regression was used to estimate the association between tinnitus and mental health distress. All measures were based on self-report. Tinnitus and perceived anxiety were each assessed using a single question. Depression symptoms were assessed using the Patient Health Questionnaire, a validated questionnaire. Multivariable regression models were adjusted for key demographic and health factors, including self-reported hearing ability. Results Prevalence of tinnitus was 15%. Compared to adults without tinnitus, adults with tinnitus had a 1.8-fold increase in depression symptoms and a 1.5-fold increase in perceived anxiety after adjusting for potential confounders. Military Veteran status did not modify these observed associations. Conclusions Findings revealed an association between tinnitus and both depression symptoms and perceived anxiety, independent of potential confounders, among both Veterans and non-Veterans. These results suggest, on a population level, that individuals with tinnitus have a greater burden of perceived mental health distress and may benefit from interdisciplinary health care, self-help, and community-based interventions. Supplemental Material https://doi.org/10.23641/asha.12568475

scholarly journals Comparison of Spectroscopic Techniques Combined with Chemometrics for Cocaine Powder Analysis

2020 ◽  
Vol 44 (8) ◽  
pp. 851-860
Author(s):  
Joy Eliaerts ◽  
Natalie Meert ◽  
Pierre Dardenne ◽  
Vincent Baeten ◽  
Juan-Antonio Fernandez Pierna ◽  
...  
Keyword(s):  
Gas Chromatography ◽  
Near Infrared ◽  
Evaluation Criteria ◽  
Classification Model ◽  
Support Vector ◽  
Spectroscopic Techniques ◽  
Data Set ◽  
Promising Tool ◽  
Powder Analysis ◽  
Mir Spectra

Abstract Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC–MS) and gas chromatography with flame-ionization detection (GC–FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

352 Feasibility of Hypnosis as Adjunctive Treatment for Subjective Sleep Disturbance: A Pilot Study and Proof of Concept

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A140-A141
Author(s):  
Emma Zhao ◽  
Afik Faerman ◽  
David Spiegel
Keyword(s):  
Sleep Quality ◽  
Sleep Disturbances ◽  
Positive Impact ◽  
Self Report ◽  
Adjunctive Treatment ◽  
Current Evidence ◽  
Data Set ◽  
Sleep Complaints ◽  
Subjective Sleep ◽  
Before And After

Abstract Introduction Hypnosis-based interventions have been shown to have a positive impact on several dimensions of sleep health. However, current evidence is limited as only a paucity of studies included populations with sleep complaints. Here we present a pilot data set to demonstrate the feasibility of developing a hypnosis-based adjunctive treatment for subjective sleep complaints. Methods Eleven adults (42% female; mean age 45±16.87 years) who sought treatment at the Stanford Sleep Medicine Center or Center for Integrative Medicine for subjective sleep complaints received hypnosis as adjunctive treatment. Self-report questionnaires were used to assess the weekly frequency of subjective sleep disturbances experienced before and after treatment, as well as 5-point Likert scale ratings of perceived qualitative improvement in symptom severity and overall sleep quality. Results Five participants (45%) reported a reduction in symptom frequency and severity after hypnosis treatment. All five participants attributed at least some of the improvement to hypnosis treatment. Most participants (63%) observed post-treatment improvements in their overall sleep quality. No participants reported adverse effects of hypnosis. Conclusion Results suggest hypnosis-based adjunctive treatment may be effective for alleviating subjective sleep disturbances. The findings serve as preliminary support for further randomly controlled trials in larger samples. Support (if any):

scholarly journals Research on Real-Time Multiple Single Garbage Classification Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jian-ye Yuan ◽  
Xin-yuan Nan ◽  
Cheng-rong Li ◽  
Le-le Sun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Environmental Science ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Models ◽  
Data Set ◽  
Classification Tasks ◽  
Accuracy Rates

Considering that the garbage classification is urgent, a 23-layer convolutional neural network (CNN) model is designed in this paper, with the emphasis on the real-time garbage classification, to solve the low accuracy of garbage classification and recycling and difficulty in manual recycling. Firstly, the depthwise separable convolution was used to reduce the Params of the model. Then, the attention mechanism was used to improve the accuracy of the garbage classification model. Finally, the model fine-tuning method was used to further improve the performance of the garbage classification model. Besides, we compared the model with classic image classification models including AlexNet, VGG16, and ResNet18 and lightweight classification models including MobileNetV2 and SuffleNetV2 and found that the model GAF_dense has a higher accuracy rate, fewer Params, and FLOPs. To further check the performance of the model, we tested the CIFAR-10 data set and found the accuracy rates of the model (GAF_dense) are 0.018 and 0.03 higher than ResNet18 and SufflenetV2, respectively. In the ImageNet data set, the accuracy rates of the model (GAF_dense) are 0.225 and 0.146 higher than Resnet18 and SufflenetV2, respectively. Therefore, the garbage classification model proposed in this paper is suitable for garbage classification and other classification tasks to protect the ecological environment, which can be applied to classification tasks such as environmental science, children’s education, and environmental protection.

The Effects of the Opioid Epidemic on Prescribing Practices in Long-Term Care Residents

2019 ◽  
Vol 34 (4) ◽  
pp. 258-267
Author(s):  
Lisa Yamagishi ◽  
Olivia Erickson ◽  
Kelly Mazzei ◽  
Christine O'Neil ◽  
Khalid M. Kamal
Keyword(s):  
Severe Pain ◽  
Long Term Care ◽  
Care Facility ◽  
The United States ◽  
Self Report ◽  
Prescribing Practices ◽  
Term Care ◽  
Data Set ◽  
Opioid Prescribing

OBJECTIVE: Evaluate opioid prescribing practices for older adults since the opioid crisis in the United States.<br/> DESIGN: Interrupted time-series analysis on retrospective observational cohort study.<br/> SETTING: 176-bed skilled-nursing facility (SNF).<br/> PARTICIPANTS: Patients admitted to a long-term care facility with pain-related diagnoses between October 1, 2015, and March 31, 2017, were included. Residents discharged prior to 14 days were excluded. Of 392 residents, 258 met inclusion criteria with 313 admissions.<br/> MAIN OUTCOME MEASURE: Changes in opioid prescribing frequency between two periods: Q1 to Q3 (Spring 2016) and Q4 to Q6 for pre- and postgovernment countermeasure, respectively.<br/> RESULTS: Opioid prescriptions for patients with pain-related diagnoses decreased during period one at -0.10% per quarter (95% confidence interval [CI] -0.85-0.85; P = 0.99), with the rate of decline increasing at -3.8% per quarter from period 1 and 2 (95% CI -0.23-0.15; P = 0.64). Opioid prescribing from top International Classification of Diseases, Ninth Revision category, "Injury and Poisoning" decreased in prescribing frequency by -3.0% per quarter from Q1 to Q6 (95% CI -0.16-0.10; P = 0.54). Appropriateness of pain-control was obtained from the Minimum Data Set version 3.0 "Percent of Residents Who Self-Report Moderate to Severe Pain (Short Stay)" measure; these results showed a significant increase in inadequacy of pain relief by 0.28% per quarter (95% CI 0.12-0.44; P = 0.009).<br/> CONCLUSION: Residents who self-report moderate- to severe pain have significantly increased since October 2015. Opioid prescriptions may have decreased for elderly patients in SNFs since Spring 2016. Further investigation with a larger population and wider time frame is warranted to further evaluate significance.

Developing a measure to assess clinicians’ ability to reflect on key staff–patient dynamics in forensic settings

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Adam Polnay ◽  
Helen Walker ◽  
Christopher Gallacher
Keyword(s):  
Reflective Practice ◽  
Factor Structure ◽  
Internal Consistency ◽  
Self Report ◽  
Face Validity ◽  
Good Test ◽  
Data Set ◽  
Retest Reliability ◽  
Content Type ◽  
Test Retest Reliability

Purpose Relational dynamics between patients and staff in forensic settings can be complicated and demanding for both sides. Reflective practice groups (RPGs) bring clinicians together to reflect on these dynamics. To date, evaluation of RPGs has lacked quantitative focus and a suitable quantitative tool. Therefore, a self-report tool was designed. This paper aims to pilot The Relational Aspects of CarE (TRACE) scale with clinicians in a high-secure hospital and investigate its psychometric properties. Design/methodology/approach A multi-professional sample of 80 clinicians were recruited, completing TRACE and attitudes to personality disorder questionnaire (APDQ). Exploratory factor analysis (EFA) determined factor structure and internal consistency of TRACE. A subset was selected to measure test–retest reliability. TRACE was cross-validated against the APDQ. Findings EFA found five factors underlying the 20 TRACE items: “awareness of common responses,” “discussing and normalising feelings;” “utilising feelings,” “wish to care” and “awareness of complicated affects.” This factor structure is complex, but items clustered logically to key areas originally used to generate items. Internal consistency (α = 0.66, 95% confidence interval (CI) = 0.55–0.76) demonstrated borderline acceptability. TRACE demonstrated good test–retest reliability (intra-class correlation = 0.94, 95% CI = 0.78–0.98) and face validity. TRACE indicated a slight negative correlation with APDQ. A larger data set is needed to substantiate these preliminary findings. Practical implications Early indications suggested TRACE was valid and reliable, suitable to measure the effectiveness of reflective practice. Originality/value The TRACE was a distinctive measure that filled a methodological gap in the literature.

MONITORING AND CLASSIFYING THE STATE OF HARD DISKS USING RECURRENT NEURAL NETWORKS

2021 ◽  
pp. 36-43
Author(s):  
L. A. Demidova ◽  
A. V. Filatov
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Dense Layer ◽  
Hard Disks ◽  
Destructive Testing ◽  
Data Set ◽  
State Classification

The article considers an approach to solving the problem of monitoring and classifying the states of hard disks, which is solved on a regular basis, within the framework of the concept of non-destructive testing. It is proposed to solve this problem by developing a classification model using machine learning algorithms, in particular, using recurrent neural networks with Simple RNN, LSTM and GRU architectures. To develop a classification model, a data set based on the values of SMART sensors installed on hard disks it used. It represents a group of multidimensional time series. At the same time, the structure of the classification model contains two layers of a neural network with one of the recurrent architectures, as well as a Dropout layer and a Dense layer. The results of experimental studies confirming the advantages of LSTM and GRU architectures as part of hard disk state classification models are presented.

A Novel ECG Automatic Detection Using LongShort-Term Memory Network and Internet of Things Technology

2021 ◽  
Vol 11 (6) ◽  
pp. 1592-1598
Author(s):  
Xufei Liu
Keyword(s):  
Machine Learning ◽  
Cardiovascular Diseases ◽  
Internet Of Things ◽  
Network Structure ◽  
Recognition Rate ◽  
Classification Model ◽  
Data Set ◽  
Public Data ◽  
Lstm Network ◽  
Internet Of Things Technology

The early detection of cardiovascular diseases based on electrocardiogram (ECG) is very important for the timely treatment of cardiovascular patients, which increases the survival rate of patients. ECG is a visual representation that describes changes in cardiac bioelectricity and is the basis for detecting heart health. With the rise of edge machine learning and Internet of Things (IoT) technologies, small machine learning models have received attention. This study proposes an ECG automatic classification method based on Internet of Things technology and LSTM network to achieve early monitoring and early prevention of cardiovascular diseases. Specifically, this paper first proposes a single-layer bidirectional LSTM network structure. Make full use of the timing-dependent features of the sampling points before and after to automatically extract features. The network structure is more lightweight and the calculation complexity is lower. In order to verify the effectiveness of the proposed classification model, the relevant comparison algorithm is used to verify on the MIT-BIH public data set. Secondly, the model is embedded in a wearable device to automatically classify the collected ECG. Finally, when an abnormality is detected, the user is alerted by an alarm. The experimental results show that the proposed model has a simple structure and a high classification and recognition rate, which can meet the needs of wearable devices for monitoring ECG of patients.

Bayesian Classifier for Sparsity-Promoting Feature Selection

2015 ◽  
Vol 29 (06) ◽  
pp. 1550022 ◽  
Author(s):  
Danlei Xu ◽  
Lan Du ◽  
Hongwei Liu ◽  
Penghui Wang
Keyword(s):  
Feature Selection ◽  
Synthetic Data ◽  
Original Data ◽  
Radar Data ◽  
Bayesian Classifier ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Classification Boundary ◽  
Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

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