Article
Computer Science, Artificial Intelligence
Xiaoshuang Wang, Xiulin Wang, Wenya Liu, Zheng Chang, Tommi Karkkainen, Fengyu Cong
Summary: A model combining one-dimensional convolutional neural network and random selection, data augmentation strategy was proposed for epileptic seizure detection, achieving high performance on segment-based and event-based levels when tested on two long-term EEG datasets.
Article
Computer Science, Artificial Intelligence
Xiaoshuang Wang, Guanghui Zhang, Ying Wang, Lin Yang, Zhanhua Liang, Fengyu Cong
Summary: In this study, a method combining one-dimensional convolutional neural networks and channel selection strategy was proposed for seizure prediction. The method showed high accuracy and sensitivity when evaluated on a real iEEG dataset.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Geochemistry & Geophysics
Chein-I Chang
Summary: This article explores fundamental and conceptual issues in the application of the 2D ROC curve for hyperspectral anomaly detection, providing solutions and insights. By deriving a mathematical theory and conducting comprehensive analysis, it reveals the principles of plotting the 2D ROC curve and evaluating background suppression. The article also highlights that many detectors claiming good performance in AD actually perform poorly in BS.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
Article
Agriculture, Dairy & Animal Science
M. Martin, M. D. Kleinhenz, K. S. Schwartzkopf-Genswein, D. Melendez, S. Marti, E. A. Pajor, E. D. Janzen, J. Coetzee
Summary: This study used ROC analysis to evaluate the predictive value of pain biomarkers in livestock. It found that plasma cortisol, hair cortisol, and IRT had higher AUC values, while salivary cortisol, MNT, substance P, kinematic gait analysis, and a visual analog scale for pain had lower AUC values.
JOURNAL OF DAIRY SCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Marcin Kolodziej, Andrzej Majkowski, Andrzej Rysz
Summary: This article explores the possibilities, issues, and challenges associated with utilizing artificial intelligence for seizure detection using the publicly available iEEG database. It presents standard approaches for analyzing iEEG signals and discusses modern deep learning algorithms. The study shows that CNN and LSTM networks yield significantly better results, and the gradient-weighted class activation mapping algorithm can identify important iEEG signal fragments for seizure detection.
APPLIED SCIENCES-BASEL
(2023)
Article
Psychology, Multidisciplinary
Yueran Yang
Summary: This article discusses how to improve classification performance using ROC analysis and introduces a tool to visualize a classifier's expected utility. The analysis reveals that expected utility depends not only on the accuracy of a classifier but also on its operating point. Therefore, choosing the optimal operating point can maximize expected utility. The article also explores other methods beyond ROC analysis to increase expected utility.
PSYCHOLOGICAL METHODS
(2022)
Article
Computer Science, Hardware & Architecture
Ruhul Ali Khan
Summary: A new semiparametric model of the ROC curve is proposed, which is based on the resilience family or proportional reversed hazard family. The resulting ROC curve and its summary indices have simple analytic forms. The estimation methodologies of the resilience family and a simulation study to assess the performance of the estimators are discussed.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Interdisciplinary Applications
Lisha Zhong, Jia Wu, Shuling He, Fangji Yi, Chen Zeng, Xi Li, Zhangyong Li, Zhiwei Huang
Summary: This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG signals. The spatial and temporal factors have strong synergistic effect on triggering seizures, and the proposed model can achieve high accuracy in predicting epileptic seizures.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Nutrition & Dietetics
Adam Drewnowski, Tanhia D. Gonzalez, Colin D. Rehm
Summary: This study compared the diagnostic accuracy of a new balanced hybrid nutrient density score (bHNDS) to Nutri-Score and Health Star Rating (HSR) front-of-pack systems using ROC curve analyses. The results showed high agreement between bHNDS and the two labeling systems.
FRONTIERS IN NUTRITION
(2022)
Article
Computer Science, Software Engineering
Shun Liu, Junjie Yang, Xianxian Zeng, Haiying Song, Jian Cen, Weichao Xu
Summary: Receiver Operating Characteristic (ROC) analysis is a commonly used tool in science and engineering for two-class problems. It characterizes the performance of binary statistical models using the scalar area under the ROC curve (AUC). However, tasks with more than two categories are frequently encountered, and the extension of ROC curve and AUC, namely hyper ROC surface and hyper-volume under the surface (HVUS), has attracted extensive interest. This study develops a python-based package and software tool as an alternative for multi-class ROC analysis.
Article
Mathematics
Hyunsuk Han
Summary: This study proposed the concept of using ROC curves to evaluate classifier performance and demonstrated its application in educational assessment. The results showed that ROC curves provide a comprehensive evaluation of classification quality and address issues in accuracy and consistency indices.
Article
Biochemical Research Methods
Krishnakant V. Saboo, Yurui Cao, Vaclav Kremen, Vladimir Sladky, Nicholas M. Gregg, Paul M. Arnold, Philippa J. Karoly, Dean R. Freestone, Mark J. Cook, Gregory A. Worrell, Ravishankar K. Iyer
Summary: This paper presents machine learning models that use bivariate intracranial EEG (iEEG) features to predict seizure clustering. The models accurately predicted seizure occurrence and type, providing potential benefits in addressing clinical burden and improving patients' quality of life.
IEEE TRANSACTIONS ON NANOBIOSCIENCE
(2023)
Article
Engineering, Biomedical
Xiaoshuang Wang, Chi Zhang, Tommi Karkkainen, Zheng Chang, Fengyu Cong
Summary: This article proposes a novel deep learning method based on iEEG, which combines the channel increment strategy and One-Dimensional Convolutional Neural Networks (1D-CNN) for effective seizure prediction. By sequentially increasing the number of channels, the best channel case is selected for prediction based on classification results. Experimental results on the Freiburg iEEG database show that our method achieves good performance at both segment-based and event-based levels.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Review
Neurosciences
Nykan Mirchi, Nebras M. Warsi, Frederick Zhang, Simeon M. Wong, Hrishikesh Suresh, Karim Mithani, Lauren Erdman, George M. Ibrahim
Summary: This article provides a review of the application of machine learning (ML) techniques in intracranial electroencephalography (iEEG) data analysis. The analysis of 107 articles reveals the potential clinical applications of ML in seizure analysis, motor tasks, cognitive assessment, and sleep staging.
FRONTIERS IN HUMAN NEUROSCIENCE
(2022)
Article
Computer Science, Interdisciplinary Applications
Maria Xose Rodriguez-Alvarez, Vanda Inacio
Summary: This package introduces methods for estimating various types of ROC curves and obtaining summary measures of discriminatory accuracy. It also provides tools for calculating optimal threshold values based on ROC and assessing model fit using Bayesian methods.
Article
Engineering, Biomedical
Wenwen Wu, Yanqi Huang, Xiaomei Wu
Summary: In this study, a 2D deep learning classification network SRT was proposed to improve automatic ECG analysis. The model structure was enhanced with the CNN and Transformer-encoder modules, and a novel attention module and Dilated Stem structure were introduced to improve feature extraction. Comparative experiments showed that the proposed model outperformed several advanced methods.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Chiheb Jamazi, Ghaith Manita, Amit Chhabra, Houssem Manita, Ouajdi Korbaa
Summary: In this study, a new dynamic and intelligent clustering method for brain tumor segmentation is proposed by combining the improved Aquila Optimizer (AO) and the K-Means algorithm. The proposed MAO-Kmeans approach aims to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images, and the experimental results demonstrate its effectiveness in improving the performance of conventional K-means clustering.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Alberto Hernando, Maria Dolores Pelaez-Coca, Eduardo Gil
Summary: This study applied a new algorithm to decompose the photoplethysmogram (PPG) pulse and identified changes in PPG pulse morphology due to pressure. The results showed that there was an increase in amplitude, width, and area values of the PPG pulse, and a decrease in ratios when pressure increased, indicating vasoconstriction. Furthermore, some parameters were found to be related to the pulse-to-pulse interval.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Jens Moeller, Eveline Popanda, Nuri H. Aydin, Hubert Welp, Iris Tischoff, Carsten Brenner, Kirsten Schmieder, Martin R. Hofmann, Dorothea Miller
Summary: In this study, a method based on texture features is proposed, which can classify healthy gray and white matter against glioma degrees 4 samples with reasonable classification performance using a relatively low number of samples for training. The method achieves high classification performance without the need for large datasets and complex machine learning approaches.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Amrutha Bhaskaran, Manish Arora
Summary: The study evaluates a cyclic repetition frequency-based algorithm for fetal heart rate estimation. The algorithm improves accuracy and reliability for poor-quality signals and performs well for different gestation weeks and clinical settings.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Manan Patel, Harsh Bhatt, Manushi Munshi, Shivani Pandya, Swati Jain, Priyank Thakkar, Sangwon Yoon
Summary: Electroencephalogram (EEG) signals have been effectively used to measure and analyze neurological data and brain-related ailments. Artificial Intelligence (AI) algorithms, specifically the proposed CNN-FEBAC framework, show promising results in studying the EEG signals of autistic patients and predicting their response to stimuli with 91% accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Wencheng Gu, Kexue Sun
Summary: This research proposes an improved version of YOLOv5 (AYOLOv5) based on the attention mechanism to address the issue of low recognition rate in cell detection. Experimental results demonstrate that AYOLOv5 can accurately identify cell targets and improve the quality and recognition performance of cell pictures.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Anita Gade, V. Vijaya Baskar, John Panneerselvam
Summary: Analysis of exhaled breath is an increasingly used diagnostic technique in medicine. This study introduces a new NICBGM-based model that utilizes various features and weight optimization for accurate data interpretation and result optimization.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Arsalan Asemi, Keivan Maghooli, Fereidoun Nowshiravan Rahatabad, Hamid Azadeh
Summary: Biometric authentication systems can perform identity verification with optimal accuracy in various environments and emotional changes, while the performance of signature verification systems can be affected when people are under stress. This study examines the performance of a signature verification system based on muscle synergy patterns as biometric characteristics for stressed individuals. EMG signals from hand and arm muscles were recorded and muscle synergies were extracted using Non-Negative Matrix Factorization. The extracted patterns were classified using Support Vector Machine for authentication of stressed individuals.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tianjiao Guo, Jie Yang, Qi Yu
Summary: This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
G. Akilandasowmya, G. Nirmaladevi, S. U. Suganthi, A. Aishwariya
Summary: This study proposes a technique for skin cancer detection and classification using deep hidden features and ensemble classifiers. By optimizing features to reduce data dimensionality and combining ensemble classifiers, the proposed method outperforms in skin cancer classification and improves prediction accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Tuuli Uudeberg, Juri Belikov, Laura Paeske, Hiie Hinrikus, Innar Liiv, Maie Bachmann
Summary: This article introduces a novel feature extraction method, the in-phase matrix profile (pMP), specifically adapted for electroencephalographic (EEG) signals, for detecting major depressive disorder (MDD). The results show that pMP outperforms Higuchi's fractal dimension (HFD) in detecting MDD, making it a promising method for future studies and potential clinical use for diagnosing MDD.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
P. Nancy, M. Parameswari, J. Sathya Priya
Summary: Stroke is the third leading cause of mortality worldwide, and early detection is crucial to avoid health risks. Existing research on disease detection using machine learning techniques has limitations, so a new stroke detection system is proposed. The experimental results show that the proposed method achieves a high accuracy rate in stroke detection.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Shimin Liu, Zhiwen Huang, Jianmin Zhu, Baolin Liu, Panyu Zhou
Summary: In this study, a continuous blood pressure (BP) monitoring method based on random forest feature selection (RFFS) and a gray wolf optimization-gradient boosting regression tree (GWO-GBRT) prediction model was developed. The method extracted features from electrocardiogram (ECG) and photoplethysmography (PPG) signals, and employed RFFS to select sensitive features highly correlated with BP. A hybrid prediction model of gray wolf optimization (GWO) technique and gradient boosting regression tree (GBRT) algorithm was established to learn the relationship between BP and sensitive features. Experimental results demonstrated the effectiveness and advancement of the proposed method.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
Article
Engineering, Biomedical
Weijun Gong, Yurong Qian, Weihang Zhou, Hongyong Leng
Summary: The recognition of dynamic facial expressions is challenging due to various factors, and obtaining discriminative expression features has been difficult. Traditional deep learning networks lack understanding of global and temporal expressions. This study proposes an enhanced spatial-temporal learning network to improve dynamic facial expression recognition.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2024)
