scholarly journals The NMT Scalp EEG Dataset: An Open-Source Annotated Dataset of Healthy and Pathological EEG Recordings for Predictive Modeling

2022 ◽  
Vol 15 ◽  
Author(s):  
Hassan Aqeel Khan ◽  
Rahat Ul Ain ◽  
Awais Mehmood Kamboh ◽  
Hammad Tanveer Butt ◽  
Saima Shafait ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Open Source ◽  
Asian Population ◽  
Machine Learning Algorithms ◽  
University Hospital ◽  
South Asian Population ◽  
Diagnostic Screening ◽  
Scalp Eeg ◽  
Eeg Recordings

Electroencephalogram (EEG) is widely used for the diagnosis of neurological conditions like epilepsy, neurodegenerative illnesses and sleep related disorders. Proper interpretation of EEG recordings requires the expertise of trained neurologists, a resource which is scarce in the developing world. Neurologists spend a significant portion of their time sifting through EEG recordings looking for abnormalities. Most recordings turn out to be completely normal, owing to the low yield of EEG tests. To minimize such wastage of time and effort, automatic algorithms could be used to provide pre-diagnostic screening to separate normal from abnormal EEG. Data driven machine learning offers a way forward however, design and verification of modern machine learning algorithms require properly curated labeled datasets. To avoid bias, deep learning based methods must be trained on large datasets from diverse sources. This work presents a new open-source dataset, named the NMT Scalp EEG Dataset, consisting of 2,417 recordings from unique participants spanning almost 625 h. Each recording is labeled as normal or abnormal by a team of qualified neurologists. Demographic information such as gender and age of the patient are also included. Our dataset focuses on the South Asian population. Several existing state-of-the-art deep learning architectures developed for pre-diagnostic screening of EEG are implemented and evaluated on the NMT, and referenced against baseline performance on the well-known Temple University Hospital EEG Abnormal Corpus. Generalization of deep learning based architectures across the NMT and the reference datasets is also investigated. The NMT dataset is being released to increase the diversity of EEG datasets and to overcome the scarcity of accurately annotated publicly available datasets for EEG research.

Abstract P357: Deep Learning Building on Prior Ischemic Core Segmentation Improves Prediction of Infarction After Stroke

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Julian Klug ◽  
Guillaume Leclerc ◽  
Elisabeth Dirren ◽  
Preti Maria Gulia ◽  
Dimitri Van De Ville ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Infarct Volume ◽  
Machine Learning Algorithms ◽  
University Hospital ◽  
Baseline Model ◽  
Stroke Treatment ◽  
Efficient Integration ◽  
Ischemic Core

Introduction: Imaging studies are used to guide patient selection for acute stroke treatment. Perfusion CT (pCT) is widely used to identify the acute ischemic core and penumbra, but the prediction of the final infarct remains challenging. With the advent of machine learning, algorithms learning the prediction of the final lesion from imaging data collected in the acute phase have been proposed. We aimed to investigate whether machine learning methods that integrate prior ischemic core segmentation improve the prediction of the final infarct after stroke. Methodology: We retrospectively included all stroke patients admitted to the Geneva University Hospital for intravenous and/or endovascular treatment from 01.2016 to 12.2017. All patients had acute pCT and follow-up MRI. An Attention-Gated 3D Unet was used as the baseline model on which the effect of access to a threshold-based ischemic core segmentation was tested. To ensure the efficient integration of information contained in voxels from the ischemic core, we extended the baseline model with a bayesian skip connection allowing only the prior to bypass most of the network. This modifies the model’s task to predict divergence from the prior representation. All models were evaluated for the prediction of the final infarct on follow-up MRI, given acute pCT maps as input. The output of each model was compared to finals lesions manually delineated by expert neurologists. Dice score was used to assess performance. Results: A total of 144 patients were included. Median hypoperfused tissue volume (Tmax > 6s) was 60 ml [17-134], median ischemic core (relative CBF < 38%) volume was 23 ml [17-33] and median final infarct volume was 13 ml [3-38]. Dice score for the threshold based ischemic core segmentation was 0.1. The baseline model with and without prior segmentation as input achieved a Dice score of 0.19. Adding the proposed bayesian skip connection lead to a more efficient integration of the prior segmentation ensuring faster convergence and better performance with a final Dice score of 0.21. Conclusion: The evaluated deep learning model can effectively leverage the information contained in a prior segmentation of the ischemic core to enhance the learning process and improve the prediction of the final infarct after stroke.

scholarly journals Comparative analysis of machine learning methods by the example of the problem of determining muon decay

2020 ◽  
Vol 28 (2) ◽  
pp. 105-119
Author(s):  
Migran N. Gevorkyan ◽  
Anastasia V. Demidova ◽  
Dmitry S. Kulyabov
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Large Hadron Collider ◽  
Open Source ◽  
Hadron Collider ◽  
Learning Algorithms ◽  
Muon Decay ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

The history of using machine learning algorithms to analyze statistical models is quite long. The development of computer technology has given these algorithms a new breath. Nowadays deep learning is mainstream and most popular area in machine learning. However, the authors believe that many researchers are trying to use deep learning methods beyond their applicability. This happens because of the widespread availability of software systems that implement deep learning algorithms, and the apparent simplicity of research. All this motivate the authors to compare deep learning algorithms and classical machine learning algorithms. The Large Hadron Collider experiment is chosen for this task, because the authors are familiar with this scientific field, and also because the experiment data is open source. The article compares various machine learning algorithms in relation to the problem of recognizing the decay reaction + + + at the Large Hadron Collider. The authors use open source implementations of machine learning algorithms. We compare algorithms with each other based on calculated metrics. As a result of the research, we can conclude that all the considered machine learning methods are quite comparable with each other (taking into account the selected metrics), while different methods have different areas of applicability.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

scholarly journals Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kinshuk Sengupta ◽  
Praveen Ranjan Srivastava
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Machine Learning Algorithms ◽  
Classification Task ◽  
Clinical Image ◽  
Prototype Model ◽  
Learning Models ◽  
Accuracy Measure ◽  
Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Pervasive Lying Posture Tracking

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5953 ◽  
Author(s):  
Parastoo Alinia ◽  
Ali Samadani ◽  
Mladen Milosevic ◽  
Hassan Ghasemzadeh ◽  
Saman Parvaneh
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Computational Models ◽  
Learning Algorithms ◽  
Pressure Sensors ◽  
Machine Learning Algorithms ◽  
Sensor System ◽  
Accurate Detection ◽  
Research Questions ◽  
Posture Tracking

Automated lying-posture tracking is important in preventing bed-related disorders, such as pressure injuries, sleep apnea, and lower-back pain. Prior research studied in-bed lying posture tracking using sensors of different modalities (e.g., accelerometer and pressure sensors). However, there remain significant gaps in research regarding how to design efficient in-bed lying posture tracking systems. These gaps can be articulated through several research questions, as follows. First, can we design a single-sensor, pervasive, and inexpensive system that can accurately detect lying postures? Second, what computational models are most effective in the accurate detection of lying postures? Finally, what physical configuration of the sensor system is most effective for lying posture tracking? To answer these important research questions, in this article we propose a comprehensive approach for designing a sensor system that uses a single accelerometer along with machine learning algorithms for in-bed lying posture classification. We design two categories of machine learning algorithms based on deep learning and traditional classification with handcrafted features to detect lying postures. We also investigate what wearing sites are the most effective in the accurate detection of lying postures. We extensively evaluate the performance of the proposed algorithms on nine different body locations and four human lying postures using two datasets. Our results show that a system with a single accelerometer can be used with either deep learning or traditional classifiers to accurately detect lying postures. The best models in our approach achieve an F1 score that ranges from 95.2% to 97.8% with a coefficient of variation from 0.03 to 0.05. The results also identify the thighs and chest as the most salient body sites for lying posture tracking. Our findings in this article suggest that, because accelerometers are ubiquitous and inexpensive sensors, they can be a viable source of information for pervasive monitoring of in-bed postures.

scholarly journals Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building

2021 ◽  
Vol 13 (3) ◽  
pp. 67
Author(s):  
Eric Hitimana ◽  
Gaurav Bajpai ◽  
Richard Musabe ◽  
Louis Sibomana ◽  
Jayavel Kayalvizhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Time Series Data ◽  
Multivariate Time Series ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Support Vector ◽  
Human Beings ◽  
Feed Forward Network

Many countries worldwide face challenges in controlling building incidence prevention measures for fire disasters. The most critical issues are the localization, identification, detection of the room occupant. Internet of Things (IoT) along with machine learning proved the increase of the smartness of the building by providing real-time data acquisition using sensors and actuators for prediction mechanisms. This paper proposes the implementation of an IoT framework to capture indoor environmental parameters for occupancy multivariate time-series data. The application of the Long Short Term Memory (LSTM) Deep Learning algorithm is used to infer the knowledge of the presence of human beings. An experiment is conducted in an office room using multivariate time-series as predictors in the regression forecasting problem. The results obtained demonstrate that with the developed system it is possible to obtain, process, and store environmental information. The information collected was applied to the LSTM algorithm and compared with other machine learning algorithms. The compared algorithms are Support Vector Machine, Naïve Bayes Network, and Multilayer Perceptron Feed-Forward Network. The outcomes based on the parametric calibrations demonstrate that LSTM performs better in the context of the proposed application.

A Comparative Study of Alzheimer Detection Techniques

2021 ◽  
Vol 9 (8) ◽  
pp. 2877-2883
Author(s):  
Adwait Patil
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Deep Learning ◽  
Comparative Study ◽  
Fuzzy System ◽  
Early Stage ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Detection Techniques

Abstract: Alzheimer’s disease is one of the neurodegenerative disorders. It initially starts with innocuous symptoms but gradually becomes severe. This disease is so dangerous because there is no treatment, the disease is detected but typically at a later stage. So it is important to detect Alzheimer at an early stage to counter the disease and for a probable recovery for the patient. There are various approaches currently used to detect symptoms of Alzheimer’s disease (AD) at an early stage. The fuzzy system approach is not widely used as it heavily depends on expert knowledge but is quite efficient in detecting AD as it provides a mathematical foundation for interpreting the human cognitive processes. Another more accurate and widely accepted approach is the machine learning detection of AD stages which uses machine learning algorithms like Support Vector Machines (SVMs) , Decision Tree , Random Forests to detect the stage depending on the data provided. The final approach is the Deep Learning approach using multi-modal data that combines image , genetic data and patient data using deep models and then uses the concatenated data to detect the AD stage more efficiently; this method is obscure as it requires huge volumes of data. This paper elaborates on all the three approaches and provides a comparative study about them and which method is more efficient for AD detection. Keywords: Alzheimer’s Disease (AD), Fuzzy System , Machine Learning , Deep Learning , Multimodal data

Application of Machine Learning in Gas-Hydrate Formation and Trendline Prediction

2021 ◽  
Author(s):  
Celestine Udim Monday ◽  
Toyin Olabisi Odutola
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Gas ◽  
Gas Hydrate ◽  
Gas Flow ◽  
Flow Line ◽  
Learning Algorithms ◽  
Hydrate Formation ◽  
Machine Learning Algorithms ◽  
Gas Hydrate Formation

Abstract Natural Gas production and transportation are at risk of Gas hydrate plugging especially when in offshore environments where temperature is low and pressure is high. These plugs can eventually block the pipeline, increase back pressure, stop production and ultimately rupture gas pipelines. This study seeks to develops machine learning models after a kinetic inhibitor to predict the gas hydrate formation and pressure changes within the natural gas flow line. Green hydrate inhibitor A, B and C were obtained as plant extracts and applied in low dosages (0.01 wt.% to 0.1 wt.%) on a 12meter skid-mounted hydrate closed flow loop. From the data generated, the optimal dosages of inhibitor A, B and C were observed to be 0.02 wt.%, 0.06 wt.% and 0.1 wt.% respectively. The data associated with these optimal dosages were fed to a set of supervised machine learning algorithms (Extreme gradient boost, Gradient boost regressor and Linear regressor) and a deep learning algorithm (Artificial Neural Network). The output results from the set of supervised learning algorithms and Deep Learning algorithms were compared in terms of their accuracies in predicting the hydrate formation and the pressure within the natural gas flow line. All models had accuracies greater than 90%. This result show that the application Machine learning to solving flow assurance problems is viable. The results show that it is viable to apply machine learning algorithms to solve flow assurance problems, analyzing data and getting reports which can improve accuracy and speed of on-site decision making process.

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