scholarly journals EyeBallGUI: A Tool for Visual Inspection and Binary Marking of Multi-channel Bio-signals

2017 ◽  
Author(s):  
Kieran S. Mohr ◽  
Bahman Nasseroleslami ◽  
Parameswaran M. Iyer ◽  
Orla Hardiman ◽  
Edmund C. Lalor
Keyword(s):  
Visual Inspection ◽  
Binary Classification ◽  
Analysis Tool ◽  
Data Retention ◽  
Wide Range ◽  
Sample Data ◽  
Electroencephalogram Eeg ◽  
Free Open Source ◽  
Selection Of

AbstractA wide range of studies in human neuroscience rely on the analysis of electrophysiological bio-signals such as electroencephalogram (EEG) where customized data analysis may require supervised artefact rejection, binary marking through visual inspection, selection of noise and artefact samples for pre-processing algorithms, and selection of clinically-relevant signal segments in neurological conditions. Nevertheless, the existing preprocessing tools do not provide the needed flexibility to handle such tasks efficiently. We therefore developed a free open-source Graphical User Interface (GUI), EyeBallGUI, that allows visualization and flexible, manual marking (binary classification) of multi-channel bio-signal data. EyeBallGUI, developed for MATLAB®, allows the user to interactively and accurately inspect and mark multi-channel digitized data with no restriction on marking periods of data in subsets of channels (a restriction in place in existing tools). The new tool facilitates precise, manual marking of bio-signals by allowing any desired segment of data to be marked in any subset of channels. It is therefore of utility in circumstances where such flexibility is essential. The developed GUI is an auxiliary analysis tool that shall facilitate neural signal (pre-)processing applications where it is desirable to perform accurate supervised artefact rejection, flexible data marking for increased data retention yield, extraction of specific signal segments by expert users from sample data, or labeling of data for clinical and scientific research purposes.

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

scholarly journals Multi-Center Validation Study of Automated Classification of Pathological Slowing in Adult Scalp Electroencephalograms Via Frequency Features

2021 ◽  
pp. 2150016
Author(s):  
Wei Yan Peh ◽  
John Thomas ◽  
Elham Bagheri ◽  
Rima Chaudhari ◽  
Sagar Karia ◽  
...  
Keyword(s):  
Neurological Disorders ◽  
Visual Inspection ◽  
Detection System ◽  
Automated Classification ◽  
The Us ◽  
Electroencephalogram Eeg ◽  
Level Performance ◽  
Frequency Features ◽  
Channel Segment

Pathological slowing in the electroencephalogram (EEG) is widely investigated for the diagnosis of neurological disorders. Currently, the gold standard for slowing detection is the visual inspection of the EEG by experts, which is time-consuming and subjective. To address those issues, we propose three automated approaches to detect slowing in EEG: Threshold-based Detection System (TDS), Shallow Learning-based Detection System (SLDS), and Deep Learning-based Detection System (DLDS). These systems are evaluated on channel-, segment-, and EEG-level. The three systems perform prediction via detecting slowing at individual channels, and those detections are arranged in histograms for detection of slowing at the segment- and EEG-level. We evaluate the systems through Leave-One-Subject-Out (LOSO) cross-validation (CV) and Leave-One-Institution-Out (LOIO) CV on four datasets from the US, Singapore, and India. The DLDS achieved the best overall results: LOIO CV mean balanced accuracy (BAC) of 71.9%, 75.5%, and 82.0% at channel-, segment- and EEG-level, and LOSO CV mean BAC of 73.6%, 77.2%, and 81.8% at channel-, segment-, and EEG-level. The channel- and segment-level performance is comparable to the intra-rater agreement (IRA) of an expert of 72.4% and 82%. The DLDS can process a 30 min EEG in 4 s and can be deployed to assist clinicians in interpreting EEGs.

scholarly journals EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Bingtao Zhang ◽  
Tao Lei ◽  
Hong Liu ◽  
Hanshu Cai
Keyword(s):  
Visual Inspection ◽  
Sleep Stages ◽  
Subjective Time ◽  
Sleep Staging ◽  
Large Bulk ◽  
State Classification ◽  
Eeg Data ◽  
Electroencephalogram Eeg ◽  
Eeg Features

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot of attention from sleep researchers. Nevertheless, sleep staging based on visual inspection of tradition is subjective, time-consuming, and error-prone due to the large bulk of data which have to be processed. Therefore, automatic sleep staging is essential in order to solve these problems. In this article, an electroencephalogram- (EEG-) based scheme that is able to automatically classify sleep stages is proposed. Firstly, EEG data are preprocessed to remove artifacts, extract features, and normalization. Secondly, the normalized features and other context information are stored using an ontology-based model (OBM). Thirdly, an improved method of self-adaptive correlation analysis is designed to select the most effective EEG features. Based on these EEG features and weighting features analysis, the improved random forest (RF) is considered as the classifier to achieve the classification of sleep stages. To investigate the classification ability of the proposed method, several sets of experiments are designed and conducted to classify the sleep stages into two, three, four, and five states. The accuracy of five-state classification is 89.37%, which is improved compared to the accuracy using unimproved RF (84.37%) or previously reported classifiers. In addition, a set of controlled experiments is executed to verify the effect of the number of sleep segments (epochs) on the classification, and the results demonstrate that the proposed scheme is less affected by the sleep segments.

scholarly journals Quality of Human Resources in Europe Measured by Selected Indicators of Human Potential

2017 ◽  
Vol 20 (1) ◽  
pp. 60-76
Author(s):  
Slavomir Bucher
Keyword(s):  
Human Resources ◽  
Regional Differences ◽  
Spatial Differentiation ◽  
Human Potential ◽  
Regional Differentiation ◽  
Regression Analyses ◽  
Wide Range ◽  
Selection Of

The paper deals with regional differentiation of human resources and its determinants identified by selected indicators of human potential. The selection of correct and relevant indicators has a key role in the identification and measurement of human potential. The aim of the study is to outline causal and determinant relationship (the relation and the level of dependence) in the spatial differentiation of human resources in Europe and approaches to their interpretation. The main purpose of this paper is to analyze the link between human potential and quality or inequality of life and its effect on population from a demographic viewpoint. Methods of correlation and regression analyses were applied. A wide range of the most important and most often used human potential assessment indicators based on a basic systemic classification of human potential will also be presented. Although the first glance the quality of human resources situation in Europe might seem relatively compact, deeper analysis showed that there are quite significant regional differences. Our results show that set of specific condition a constant or moderately growing human capital may aggravate the consequences of population ageing rather than alleviate them. The important results of this study include recognition of the existence of several easily manageable methods and ways of measuring demographic and/or socio-economic solutions to the challenges posed by quality of human resources in Europe.

scholarly journals Dictionary-Based Classification of Tweets About Environment

2020 ◽  
Author(s):  
Michela Cameletti ◽  
Silvia Fabris ◽  
Stephan Schlosser ◽  
Daniele Toninelli
Keyword(s):  
Social Media ◽  
Great Britain ◽  
Unstructured Data ◽  
Digital Data ◽  
Specific Topic ◽  
Social Accounts ◽  
Wide Range ◽  
The Subject ◽  
Selection Of

Abstract In the era of social media, the huge availability of digital data (e.g. posts sent through social networks or unstructured data scraped from websites) allows to develop new types of research in a wide range of fields. These types of data are characterized by some advantages such as reduced collection costs, short retrieval times and production of almost real-time outputs. Nevertheless, their collection and analysis can be challenging. For example, particular approaches are required for the selection of posts related to specific topics; moreover, retrieving the information we are interested in inside Twitter posts can be a difficult task.The main aim of this paper is to propose an unsupervised dictionary-based method to filter tweets related to a specific topic, i.e. environment. We start from the tweets sent by a selection of Official Social Accounts clearly linked with the subject of interest. Then, a list of keywords is identified in order to set a topic-oriented dictionary. We test the performance of our method by applying the dictionary to more than 54 million geolocated tweets posted in Great Britain between January and May 2019.

scholarly journals Quantifying the dynamics of hematopoiesis by in vivo IdU pulse-chase, mass cytometry and mathematical modeling

2019 ◽  
Author(s):  
Amir Erez ◽  
Ratnadeep Mukherjee ◽  
Alexandre Day ◽  
Pankaj Mehta ◽  
Grégoire Altan-Bonnet
Keyword(s):  
Mathematical Modeling ◽  
Direct Detection ◽  
Binary Classification ◽  
Mouse Strains ◽  
Mass Cytometry ◽  
Experimental Conditions ◽  
Minimal Impact ◽  
Wide Range

AbstractWe present a new method to directly quantify the dynamics of differentiation of multiple cellular subsets in unperturbed mice. We combine a pulse-chase protocol of IdU injections with subsequent analysis by mass cytometry (CyTOF), and mathematical modeling of the IdU dynamics. Measurements by CyTOF allow for a wide range of cells to be analyzed at once, due to the availability of a large staining panel without the complication of fluorescence spill-over. These are also compatible with direct detection of integrated iodine signal, with minimal impact on immunophenotyping based on surface markers. Mathematical modeling beyond a binary classification of surface marker abundance allows for a continuum of cellular states as the cells transition from one state to another. Thus, we present a complete and robust method for directly quantifying differentiation at the systemic level, allowing for system-wide comparisons between different mouse strains and/or experimental conditions.

scholarly journals EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5092
Author(s):  
Tran-Dac-Thinh Phan ◽  
Soo-Hyung Kim ◽  
Hyung-Jeong Yang ◽  
Guee-Sang Lee
Keyword(s):  
Emotion Recognition ◽  
Binary Classification ◽  
Eeg Signals ◽  
Frequency Bands ◽  
Multi Scale ◽  
Eeg Data ◽  
Crucial Information ◽  
The Time Domain ◽  
Electroencephalogram Eeg ◽  
Selection Of

Besides facial or gesture-based emotion recognition, Electroencephalogram (EEG) data have been drawing attention thanks to their capability in countering the effect of deceptive external expressions of humans, like faces or speeches. Emotion recognition based on EEG signals heavily relies on the features and their delineation, which requires the selection of feature categories converted from the raw signals and types of expressions that could display the intrinsic properties of an individual signal or a group of them. Moreover, the correlation or interaction among channels and frequency bands also contain crucial information for emotional state prediction, and it is commonly disregarded in conventional approaches. Therefore, in our method, the correlation between 32 channels and frequency bands were put into use to enhance the emotion prediction performance. The extracted features chosen from the time domain were arranged into feature-homogeneous matrices, with their positions following the corresponding electrodes placed on the scalp. Based on this 3D representation of EEG signals, the model must have the ability to learn the local and global patterns that describe the short and long-range relations of EEG channels, along with the embedded features. To deal with this problem, we proposed the 2D CNN with different kernel-size of convolutional layers assembled into a convolution block, combining features that were distributed in small and large regions. Ten-fold cross validation was conducted on the DEAP dataset to prove the effectiveness of our approach. We achieved the average accuracies of 98.27% and 98.36% for arousal and valence binary classification, respectively.

A Travel Blog as a Space for Creation and Communication of Travel Models

2016 ◽  
Vol 40 (0) ◽  
pp. 119-0 ◽  
Author(s):  
Irina Morozova
Keyword(s):  
Content Analysis ◽  
Low Cost ◽  
Future Research ◽  
Testing Method ◽  
Wide Range ◽  
Definition Of ◽  
Travel Blog ◽  
Practical Implications ◽  
Selection Of

Purpose. The purpose of this article is to present the variety of travel models which are conveyed and promoted by amateur travel blogs. Methods. The research sample was constituted on the bases of selected Polish travel blogs which promote travel models. The basic criteria for the selection of these particular blogs was the representativeness and popularity among readers. The testing method was content analysis of selected blogs. Findings. The present study suggests a classification of travel blogs. The research hypothesis claiming that the authors of travel blogs publicize travel models was confirmed. Research and conclusions limitations. The study is focused only on amateur travel blogs which are written in Polish. During the process of research, the author focused on a range of topics of the posts as well as on the publication genres. The present study includes blogs about world travels, travelling with children as well asdogs and low-cost travels. Practical implications. The results of this study indicate a wide range of possible future research studies regarding travel blogs from different perspectives. Originality. This article attempts to establish the definition of a travel model and the main characteristics of a travel blogger which aspire to become a travelebrity. A classification of travel blogs using the 'travel model' key is also provided. Type of paper. The article presents the results of empirical research conducted by the author.

scholarly journals Dictionary-based Classification of Tweets About Environment

2020 ◽  
Author(s):  
Michela Cameletti ◽  
Silvia Fabris ◽  
Stephan Schlosser ◽  
Daniele Toninelli
Keyword(s):  
Social Media ◽  
Great Britain ◽  
Unstructured Data ◽  
Digital Data ◽  
Specific Topic ◽  
Social Accounts ◽  
Wide Range ◽  
The Subject ◽  
Selection Of

Abstract In the era of social media, the huge availability of digital data (e.g. posts sent through social networks or unstructured data scraped from websites) allows to develop new types of research in a wide range of fields. These types of data are characterized by some advantages such as reduced collection costs, short retrieval times and production of almost real-time outputs. Nevertheless, their collection and analysis can be challenging. For example, particular approaches are required for the selection of posts related to specific topics; moreover, retrieving the information we are interested in inside Twitter posts can be a difficult task.The main aim of this paper is to propose an unsupervised dictionary-based method to filter tweets related to a specific topic, i.e. environment. We start from the tweets sent by a selection of Official Social Accounts clearly linked with the subject of interest. Then, a list of keywords is identified in order to set a topic-oriented dictionary. We test the performance of our method by applying the dictionary to more than 54 million geolocated tweets posted in Great Britain between January and May 2019.

scholarly journals Selection of aggregated classifiers for the prediction of the state of technical objects

2019 ◽  
pp. 361-367 ◽  
Author(s):  
D A Zhukov ◽  
V N Klyachkin ◽  
V R Krasheninnikov ◽  
Yu E Kuvayskova
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Bayes Classifier ◽  
Object State ◽  
Machine Learning Methods ◽  
State Identification ◽  
Healthy State ◽  
Selection Of

The basic data in the problem of the prediction of technical object’s state of health based on the known indicators of its operation are the known results of the object state estimation by information about previous service. The problem may be solved using the machine learning methods, it reduces to binary classification of states of the object. The research was conducted in the Matlab environment, ten various basic methods of machine learning were used: naive Bayes classifier, neural networks, bagging of decision trees and others. In order to improve quality of healthy state identification, it has been suggested that aggregated methods combining several basic classifiers should be used. This paper addresses the issue of selection of the best aggregated classifier. The effectiveness of such approach has been confirmed by numerous tests of real-world objects.

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