scholarly journals Neural Speech Encoding in Infancy Predicts Future Language and Communication Difficulties

2021 ◽  
pp. 1-10
Author(s):  
Patrick C. M. Wong ◽  
Ching Man Lai ◽  
Peggy H. Y. Chan ◽  
Ting Fan Leung ◽  
Hugh Simon Lam ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Characteristic Curve ◽  
Cost Effective ◽  
Support Vector ◽  
Speech Stimuli ◽  
Speech Encoding ◽  
Communicative Development ◽  
Language And Communication ◽  
Vocabulary Comprehension ◽  
First Year Of Life

Purpose This study aimed to construct an objective and cost-effective prognostic tool to forecast the future language and communication abilities of individual infants. Method Speech-evoked electroencephalography (EEG) data were collected from 118 infants during the first year of life during the exposure to speech stimuli that differed principally in fundamental frequency. Language and communication outcomes, namely four subtests of the MacArthur–Bates Communicative Development Inventories (MCDI)–Chinese version, were collected between 3 and 16 months after initial EEG testing. In the two-way classification, children were classified into those with future MCDI scores below the 25th percentile for their age group and those above the same percentile, while the three-way classification classified them into < 25th, 25th–75th, and > 75th percentile groups. Machine learning (support vector machine classification) with cross validation was used for model construction. Statistical significance was assessed. Results Across the four MCDI measures of early gestures, later gestures, vocabulary comprehension, and vocabulary production, the areas under the receiver-operating characteristic curve of the predictive models were respectively .92 ± .031, .91 ± .028, .90 ± .035, and .89 ± .039 for the two-way classification, and .88 ± .041, .89 ± .033, .85 ± .047, and .85 ± .050 for the three-way classification ( p < .01 for all models). Conclusions Future language and communication variability can be predicted by an objective EEG method that indicates the function of the auditory neural pathway foundational to spoken language development, with precision sufficient for individual predictions. Longer-term research is needed to assess predictability of categorical diagnostic status. Supplemental Material https://doi.org/10.23641/asha.15138546

scholarly journals Stress Classification by Multimodal Physiological Signals Using Variational Mode Decomposition and Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nilima Salankar ◽  
Deepika Koundal ◽  
Saeed Mian Qaisar
Keyword(s):  
Mental Arithmetic ◽  
Statistical Significance ◽  
Cost Effective ◽  
Physiological Signals ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Arithmetic Task ◽  
Central Tendency Measure ◽  
Ecg Signals ◽  
Mode Decomposition

In this pandemic situation, importance and awareness about mental health are getting more attention. Stress recognition from multimodal sensor based physiological signals such as electroencephalogram (EEG) and electrocardiography (ECG) signals is a very cost-effective way due to its noninvasive nature. A dataset, recorded during the mental arithmetic task, consisting of EEG + ECG signals of 36 participants is used. It contains two categories of performance, namely, “Good” (nonstressed) and “Bad” (stressed) (Gupta et al. 2018 and Eraldeír et al. 2018). This paper presents an effective approach for the recognition of stress marker at frontal, temporal, central, and occipital lobes. It processes the multimodality physiological signals. The variational mode decomposition (VMD) strategy is used for data preprocessing and for the decomposition of signals into various oscillatory mode functions. Poincare plots (PP) are derived from the first eight variational modes and features from these plots have been extracted such as mean, area, and central tendency measure of the elliptical region. The statistical significance of the extracted features with p   <   0.5 has been performed using the Wilcoxson test. The multilayer perceptron (MPLN) and Support Vector Machine (SVM) algorithms are used for the classification of stress and nonstress categories. MLPN has achieved the maximum accuracies of 100% for frontal and temporal lobes. The suggested method can be incorporated in noninvasive EEG signal processing based automated stress identification systems.

scholarly journals Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Marinus Huber ◽  
Kosmas V Kepesidis ◽  
Liudmila Voronina ◽  
Frank Fleischmann ◽  
Ernst Fill ◽  
...  
Keyword(s):  
Binary Classification ◽  
Characteristic Curve ◽  
Cost Effective ◽  
Classification Performance ◽  
Support Vector ◽  
Clear Correlation ◽  
Molecular Fingerprinting ◽  
Serum Samples ◽  
Liquid Biopsies ◽  
Cancer Types

Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78–0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition.

Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

2019 ◽  
Vol 20 (5) ◽  
pp. 488-500 ◽  
Author(s):  
Yan Hu ◽  
Yi Lu ◽  
Shuo Wang ◽  
Mengying Zhang ◽  
Xiaosheng Qu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Design ◽  
Anticancer Drugs ◽  
Nearest Neighbor ◽  
Cost Effective ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Activity Prediction ◽  
Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world&#039;s highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. </P><P> Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. </P><P> Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. </P><P> Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

scholarly journals Haemoconcentration as a predictor of severity in acute pancreatitis

2016 ◽  
Vol 4 (1) ◽  
pp. 3-7
Author(s):  
Tanka Prasad Bohara ◽  
Dimindra Karki ◽  
Anuj Parajuli ◽  
Shail Rupakheti ◽  
Mukund Raj Joshi
Keyword(s):  
Acute Pancreatitis ◽  
Pearson Correlation ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Cost Effective ◽  
Receiver Operator Characteristic Curve ◽  
First Episode ◽  
P Value ◽  
Receiver Operator Characteristic ◽  
Chi Square

Background: Acute pancreatitis is usually a mild and self-limiting disease. About 25 % of patients have severe episode with mortality up to 30%. Early identification of these patients has potential advantages of aggressive treatment at intensive care unit or transfer to higher centre. Several scoring systems are available to predict severity of acute pancreatitis but are cumbersome, take 24 to 48 hours and are dependent on tests that are not universally available. Haematocrit has been used as a predictor of severity of acute pancreatitis but some have doubted its role.Objectives: To study the significance of haematocrit in prediction of severity of acute pancreatitis.Methods: Patients admitted with first episode of acute pancreatitis from February 2014 to July 2014 were included. Haematocrit at admission and 24 hours of admission were compared with severity of acute pancreatitis. Mean, analysis of variance, chi square, pearson correlation and receiver operator characteristic curve were used for statistical analysis.Results: Thirty one patients were included in the study with 16 (51.61%) male and 15 (48.4%) female. Haematocrit at 24 hours of admission was higher in severe acute pancreatitis (P value 0.003). Both haematocrit at admission and at 24 hours had positive correlation with severity of acute pancreatitis (r: 0.387; P value 0.031 and r: 0.584; P value 0.001) respectively.Area under receiver operator characteristic curve for haematocrit at admission and 24 hours were 0.713 (P value 0.175, 95% CI 0.536 - 0.889) and 0.917 (P value 0.008, 95% CI 0.813 – 1.00) respectively.Conclusion: Haematocrit is a simple, cost effective and widely available test and can predict severity of acute pancreatitis.Journal of Kathmandu Medical College, Vol. 4(1) 2015, 3-7

scholarly journals Does daily wear time of Twin Block reliably predict its efficiency of class II treatment?

2021 ◽  
Author(s):  
Michal Sarul ◽  
Marek Nahajowski ◽  
Grzegorz Gawin ◽  
Joanna Antoszewska-Smith
Keyword(s):  
Statistical Significance ◽  
Characteristic Curve ◽  
Class Ii ◽  
Class I ◽  
Rank Test ◽  
Treatment Efficiency ◽  
Significance Level ◽  
Twin Block ◽  
Wear Time ◽  
Class Ii Treatment

Abstract Purpose The objective of this study was to investigate how daily wear time (DWT) influences class II malocclusion treatment efficiency. Materials and methods The study group consisted of 55 patients (mean age 10.4 years) diagnosed with a class II/1 malocclusion. Twin block appliances, with built-in Theramon® microsensors (MC Technology, Hargelsberg, Austria) to monitor patients’ cooperation (daily wear time assessment), were used for treatment. Cephalograms were taken and the following initial and final measurements were compared: Co-Gn, Co-Go, Co-Olp, Pg-Olp, WITS, SNA, SNB, ANB, Co-Go-Me, overjet, molar and canine relationships. The Shapiro–Wilk test, Wilcoxon signed-rank test, Student’s t-test, Levene’s test, Mann–Whitney U test, Kruskal–Wallis test, χ2 test, and Spearman’s rank correlation coefficient with p < 0.05 set as the statistical significance level were used to determine the correlation of the outcomes with DWT; a ROC (receiver operating characteristic) curve was calculated to illustrate diagnostic ability of the binary classifier system. Results DWT was very highly positively correlated with change of the Pg-Olp parameter and highly with an improvement in the ANB, SNA, and SNB angles, an increase in the WITS parameter and an increase in Co-Gn distance. DWTs < 7.5 h correlated with significantly less improvement of the investigated variables. However, DWT > 7.5 h did not significantly correlate with the improvement of the overjet and most of the linear parameters in the mandible. The ROC curve and its AUC (area under curve) allowed the determination of a DWT of 7 h and 48 min to be capable of establishing a class I relationship with 83% probability. Conclusions Class II treatment efficiency was influenced by DWT; an 8 h threshold value had an 83% probability of establishing a class I relationship.

scholarly journals PSVII-3 Correlations between L-lactate concentrations obtained using a handheld lactate analyser and a lactate assay colorimetric kit in beef cattle transported by road

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 296-297
Author(s):  
Daniela M Meléndez ◽  
Sonia Marti ◽  
Luigi Faucitano ◽  
Derek B Haley ◽  
Timothy D Schwinghamer ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Cost Effective ◽  
Road Transportation ◽  
Long Distance ◽  
Suitable Alternative ◽  
Blood Samples ◽  
Handheld Device ◽  
Time Points ◽  
Relationship Of ◽  
The Relationship

Abstract Blood metabolites are used to assess a variety of animal conditions for veterinary diagnosis and research. Concentration of metabolites in blood can be measured using a commercially-available lab-based assay or in real-time using a handheld device developed to be more time- and cost-effective than the lab-based method. Lactate is a product of anaerobic glycolysis, used in animal research as an indicator of muscle fatigue. Therefore, it has been used as an indicator of cattle response to long distance transportation. The aim of this study was to assess the relationship of L-lactate concentrations measured using a Lactate Scout+ analyzer (Lactate Scout, EFK Diagnostics, Barleben, Germany) and a lactate assay colorimetric kit (Lactate Assay Kit, Cell Biolabs Inc., San Diego, CA). Blood samples were collected by venipuncture from 96 steers (245 ± 35.7 kg BW) prior to (L1) and after 36 h, and prior to and after an additional 4 h of road transportation, and on d 1, 2, 3, 5, 14, and 28 after transport. The Lactate Scout+ analyzer strip was dipped in blood at the time of sampling, while blood samples were collected into sodium fluoride tubes for use in colorimetric analysis. Pearson correlations were calculated to determine the relationship between the experimental methods for the quantification of L-lactate concentrations. The strengths and levels of statistical significance of the correlation varied over the observed time points, r = -0.03, P = 0.75 (L1) to r = 0.75, P = &lt; 0.0001 (d 3). The correlation for the pooled data was weak but statistically significant (r = 0.33, P &lt; 0.001). Based on the experimental results, the Lactate Scout+ analyzer is not a suitable alternative to a lab-based assay for measuring L-lactate in transported cattle, due to variability across sampling time points and weak correlation with the traditional enzymatic method.

scholarly journals Predicting Risk of Antenatal Depression and Anxiety Using Multi-Layer Perceptrons and Support Vector Machines

2021 ◽  
Vol 11 (3) ◽  
pp. 199
Author(s):  
Fajar Javed ◽  
Syed Omer Gilani ◽  
Seemab Latif ◽  
Asim Waris ◽  
Mohsin Jamil ◽  
...  
Keyword(s):  
Low Income ◽  
Operating Characteristic ◽  
Mental Health Problems ◽  
Characteristic Curve ◽  
Antenatal Depression ◽  
Low Income Countries ◽  
Support Vector ◽  
Depression And Anxiety ◽  
Gynecology And Obstetrics ◽  
Operating Characteristic Curve

Perinatal depression and anxiety are defined to be the mental health problems a woman faces during pregnancy, around childbirth, and after child delivery. While this often occurs in women and affects all family members including the infant, it can easily go undetected and underdiagnosed. The prevalence rates of antenatal depression and anxiety worldwide, especially in low-income countries, are extremely high. The wide majority suffers from mild to moderate depression with the risk of leading to impaired child–mother relationship and infant health, few women end up taking their own lives. Owing to high costs and non-availability of resources, it is almost impossible to diagnose every pregnant woman for depression/anxiety whereas under-detection can have a lasting impact on mother and child’s health. This work proposes a multi-layer perceptron based neural network (MLP-NN) classifier to predict the risk of depression and anxiety in pregnant women. We trained and evaluated our proposed system on a Pakistani dataset of 500 women in their antenatal period. ReliefF was used for feature selection before classifier training. Evaluation metrics such as accuracy, sensitivity, specificity, precision, F1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the trained model. Multilayer perceptron and support vector classifier achieved an area under the receiving operating characteristic curve of 88% and 80% for antenatal depression and 85% and 77% for antenatal anxiety, respectively. The system can be used as a facilitator for screening women during their routine visits in the hospital’s gynecology and obstetrics departments.

Estimating the area under a receiver operating characteristic curve using partially ordered sets

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ehsan Zamanzade ◽  
Xinlei Wang
Keyword(s):  
Roc Curve ◽  
Characteristic Curve ◽  
Partially Ordered Sets ◽  
Sampling Technique ◽  
Real Data ◽  
Cost Effective ◽  
Ordered Sets ◽  
Operating Characteristics ◽  
Partially Ordered ◽  
Receiver Operating

AbstractRanked set sampling (RSS), known as a cost-effective sampling technique, requires that the ranker gives a complete ranking of the units in each set. Frey (2012) proposed a modification of RSS based on partially ordered sets, referred to as RSS-t in this paper, to allow the ranker to declare ties as much as he/she wishes. We consider the problem of estimating the area under a receiver operating characteristics (ROC) curve using RSS-t samples. The area under the ROC curve (AUC) is commonly used as a measure for the effectiveness of diagnostic markers. We develop six nonparametric estimators of the AUC with/without utilizing tie information based on different approaches. We then compare the estimators using a Monte Carlo simulation and an empirical study with real data from the National Health and Nutrition Examination Survey. The results show that utilizing tie information increases the efficiency of estimating the AUC. Suggestions about when to choose which estimator are also made available to practitioners.

scholarly journals Design of an SVM Classifier Assisted Intelligent Receiver for Reliable Optical Camera Communication

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4283
Author(s):  
Md.-Habibur Rahman ◽  
Md. Shahjalal ◽  
Moh. Khalid Hasan ◽  
Md.-Osman Ali ◽  
Yeong-Min Jang
Keyword(s):  
Light Emitting Diode ◽  
Characteristic Curve ◽  
Analysis Data ◽  
Image Sensor ◽  
Data Rate ◽  
Support Vector ◽  
Svm Classifier ◽  
High Data ◽  
Receiver System ◽  
Optical Camera Communication

Embedding optical camera communication (OCC) commercially as a favorable complement of radio-frequency technology has led to the desire for an intelligent receiver system that is eligible to communicate with an accurate light-emitting diode (LED) transmitter. To shed light on this issue, a novel scheme for detecting and recognizing data transmitting LEDs has been elucidated in this paper. Since the optically modulated signal is captured wirelessly by a camera that plays the role of the receiver for the OCC technology, the process to detect LED region and retrieval of exact information from the image sensor is required to be intelligent enough to achieve a low bit error rate (BER) and high data rate to ensure reliable optical communication within limited computational abilities of the most used commercial cameras such as those in smartphones, vehicles, and mobile robots. In the proposed scheme, we have designed an intelligent camera receiver system that is capable of separating accurate data transmitting LED regions removing other unwanted LED regions employing a support vector machine (SVM) classifier along with a convolutional neural network (CNN) in the camera receiver. CNN is used to detect every LED region from the image frame and then essential features are extracted to feed into an SVM classifier for further accurate classification. The receiver operating characteristic curve and other key performance parameters of the classifier have been analyzed broadly to evaluate the performance, justify the assistance of the SVM classifier in recognizing the accurate LED region, and decode data with low BER. To investigate communication performances, BER analysis, data rate, and inter-symbol interference have been elaborately demonstrated for the proposed intelligent receiver. In addition, BER against distance and BER against data rate have also been exhibited to validate the effectiveness of our proposed scheme comparing with only CNN and only SVM classifier based receivers individually. Experimental results have ensured the robustness and applicability of the proposed scheme both in the static and mobile scenarios.

scholarly journals An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Electronic Component ◽  
Support Vector ◽  
Learning Networks ◽  
Component Recognition ◽  
And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6   level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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