The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems.
{"title":"Integration of software-based cognitive approaches and brain-like computer machinery for efficient cognitive computing","authors":"Chitrakant Banchhor , Manoj Kumar Rawat , Rahul Joshi , Dharmesh Dhabliya , Omkaresh Kulkarni , Sandeep Dwarkanath Pande , Umesh Pawar","doi":"10.1016/j.neuri.2025.100194","DOIUrl":"10.1016/j.neuri.2025.100194","url":null,"abstract":"<div><div>The widespread adoption of the Internet has transformed various industries, driving significant systemic reforms across different sectors. This transformation has enhanced the Internet's role in information dissemination, resource sharing, and global connectivity, allowing for more efficient distribution of knowledge and services. The development of the Internet model and its research bring significant benefits from the network, enabling people to use and learn from it. However, the traditional education model provides only limited knowledge, restricting growth and progress. Moreover, there is a vast world of knowledge yet to be explored. Nowadays, with the help of network tools, people can understand the dynamics of the whole world and accept the culture and knowledge of different regions without going out. Throughout the study of English legacy problems in various countries, efficient learning methods and high levels of English skills are the goals pursued, while the traditional English model can't meet the students' learning needs in a short time. The model construction of data mining algorithm based on large open network courses is a model for solving legacy problems adopted both domestically and internationally. According to the survey data of universities in various countries, the use of data mining algorithm can fundamentally meet the student's desire and demand for English knowledge. This research, integrates the mining algorithm into English research, which will essentially improve the English legacy problems.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.neuri.2025.100195
Jie Li , Gary Green , Sarah J.A. Carr , Peng Liu , Jian Zhang
Abnormality detection in identifying a single-subject which deviates from the majority of a control group dataset is a fundamental problem. Typically, the control group is characterised using standard Normal statistics, and the detection of a single abnormal subject is in that context. However, in many situations, the control group cannot be described by Normal statistics, making standard statistical methods inappropriate. This paper presents a Bayesian Inference General Procedures for A Single-subject Test (BIGPAST) designed to mitigate the effects of skewness under the assumption that the dataset of the control group comes from the skewed Student t distribution. BIGPAST operates under the null hypothesis that the single-subject follows the same distribution as the control group. We assess BIGPAST's performance against other methods through simulation studies. The results demonstrate that BIGPAST is robust against deviations from normality and outperforms the existing approaches in accuracy, nearest to the nominal accuracy 0.95. BIGPAST can reduce model misspecification errors under the skewed Student t assumption by up to 12 times, as demonstrated in Section 3.3. We apply BIGPAST to a Magnetoencephalography (MEG) dataset consisting of an individual with mild traumatic brain injury and an age and gender-matched control group. For example, the previous method failed to detect abnormalities in 8 brain areas, whereas BIGPAST successfully identified them, demonstrating its effectiveness in detecting abnormalities in a single-subject.
{"title":"Bayesian Inference General Procedures for A Single-subject Test study","authors":"Jie Li , Gary Green , Sarah J.A. Carr , Peng Liu , Jian Zhang","doi":"10.1016/j.neuri.2025.100195","DOIUrl":"10.1016/j.neuri.2025.100195","url":null,"abstract":"<div><div>Abnormality detection in identifying a single-subject which deviates from the majority of a control group dataset is a fundamental problem. Typically, the control group is characterised using standard Normal statistics, and the detection of a single abnormal subject is in that context. However, in many situations, the control group cannot be described by Normal statistics, making standard statistical methods inappropriate. This paper presents a Bayesian Inference General Procedures for A Single-subject Test (BIGPAST) designed to mitigate the effects of skewness under the assumption that the dataset of the control group comes from the skewed Student <em>t</em> distribution. BIGPAST operates under the null hypothesis that the single-subject follows the same distribution as the control group. We assess BIGPAST's performance against other methods through simulation studies. The results demonstrate that BIGPAST is robust against deviations from normality and outperforms the existing approaches in accuracy, nearest to the nominal accuracy 0.95. BIGPAST can reduce model misspecification errors under the skewed Student <em>t</em> assumption by up to 12 times, as demonstrated in Section <span><span>3.3</span></span>. We apply BIGPAST to a Magnetoencephalography (MEG) dataset consisting of an individual with mild traumatic brain injury and an age and gender-matched control group. For example, the previous method failed to detect abnormalities in 8 brain areas, whereas BIGPAST successfully identified them, demonstrating its effectiveness in detecting abnormalities in a single-subject.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Asphyxia, a critical respiratory condition, poses significant risks to newborns and can lead to catastrophic outcomes. Early detection of asphyxia is crucial for reducing infant mortality rates. Traditional medical diagnosis methods can be time-consuming, whereas early detection through artificial intelligence (AI) can expedite the process and improve survival rates. Despite the importance of early asphyxia detection, existing methods are often delayed and not always effective. This research addresses the need for a faster, more accurate approach to detecting infant asphyxia using machine learning (ML) and deep learning (DL) techniques. This study aims to develop a robust AI-driven system to detect asphyxia in newborns using ML and DL models, focusing on improving accuracy and efficiency over traditional diagnostic methods. This study explores feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), where the features are categorized into time and frequency domains. Data preprocessing techniques, such as noise removal, handling missing values, outliers, and label encoding, are applied to ensure clean data. To address class imbalance, the Random Oversampling (ROS) technique is employed. Hyperparameter optimization is performed using GridSearchCV for various machine-learning models. Deep learning models, including custom artificial neural networks (ANN1) and convolutional neural networks (CNN1, CNN2), are introduced with hidden layers for improved performance. The performance of different ML and DL models is evaluated, with Logistic Regression (LR) achieving an accuracy of 99.16% and a 0.008% error rate. In comparison, ANN1 outperforms other DL models with an accuracy of 98.20% and a 0.018% error rate. The results demonstrate that both ML and DL techniques can significantly enhance early asphyxia detection in newborns. The Logistic Regression model offers the highest accuracy in machine learning, while ANN1 performs optimally in deep learning, suggesting their potential for deployment in clinical settings to improve neonatal care.
{"title":"Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach","authors":"Samrat Kumar Dey , Khandaker Mohammad Mohi Uddin , Arpita Howlader , Md. Mahbubur Rahman , Hafiz Md. Hasan Babu , Nitish Biswas , Umme Raihan Siddiqi , Badhan Mazumder","doi":"10.1016/j.neuri.2025.100193","DOIUrl":"10.1016/j.neuri.2025.100193","url":null,"abstract":"<div><div>Asphyxia, a critical respiratory condition, poses significant risks to newborns and can lead to catastrophic outcomes. Early detection of asphyxia is crucial for reducing infant mortality rates. Traditional medical diagnosis methods can be time-consuming, whereas early detection through artificial intelligence (AI) can expedite the process and improve survival rates. Despite the importance of early asphyxia detection, existing methods are often delayed and not always effective. This research addresses the need for a faster, more accurate approach to detecting infant asphyxia using machine learning (ML) and deep learning (DL) techniques. This study aims to develop a robust AI-driven system to detect asphyxia in newborns using ML and DL models, focusing on improving accuracy and efficiency over traditional diagnostic methods. This study explores feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs), where the features are categorized into time and frequency domains. Data preprocessing techniques, such as noise removal, handling missing values, outliers, and label encoding, are applied to ensure clean data. To address class imbalance, the Random Oversampling (ROS) technique is employed. Hyperparameter optimization is performed using GridSearchCV for various machine-learning models. Deep learning models, including custom artificial neural networks (ANN1) and convolutional neural networks (CNN1, CNN2), are introduced with hidden layers for improved performance. The performance of different ML and DL models is evaluated, with Logistic Regression (LR) achieving an accuracy of 99.16% and a 0.008% error rate. In comparison, ANN1 outperforms other DL models with an accuracy of 98.20% and a 0.018% error rate. The results demonstrate that both ML and DL techniques can significantly enhance early asphyxia detection in newborns. The Logistic Regression model offers the highest accuracy in machine learning, while ANN1 performs optimally in deep learning, suggesting their potential for deployment in clinical settings to improve neonatal care.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100193"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), significantly affecting patients' quality of life and posing challenges for clinical management. Early prediction of cognitive decline in PD is critical for timely diagnosis and intervention. However, the interplay of multivariate factors such as age, gender, and disease duration complicate early prediction. To address the multifactorial nature of cognitive impairment in PD, this study proposes a neuroscience-informed nomogram model constructed using multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. Performance evaluation of the model demonstrated high accuracy, consistency, and clinical applicability, significantly enhancing diagnostic efficiency for neurologists. Furthermore, the model provides visual comparisons of patient distributions across different predictor values, enabling personalized risk assessments. According to experimental analysis and verification, the model demonstrated outstanding prediction with a region under the ROC curve of 0.872 on the original training set and 0.870 on the validation set. Because the anticipated and observed probabilities were so consistent, the model was able to forecast the patient's likelihood of cognitive impairment.
{"title":"Neuroscience-informed nomogram model for early prediction of cognitive impairment in Parkinson's disease","authors":"Sudharshan Putha , Swaroop Reddy Gayam , Bhavani Prasad Kasaraneni , Krishna Kanth Kondapaka , Sateesh Kumar Nallamala , Praveen Thuniki","doi":"10.1016/j.neuri.2025.100189","DOIUrl":"10.1016/j.neuri.2025.100189","url":null,"abstract":"<div><div>Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD), significantly affecting patients' quality of life and posing challenges for clinical management. Early prediction of cognitive decline in PD is critical for timely diagnosis and intervention. However, the interplay of multivariate factors such as age, gender, and disease duration complicate early prediction. To address the multifactorial nature of cognitive impairment in PD, this study proposes a neuroscience-informed nomogram model constructed using multivariate logistic regression. The least absolute shrinkage and selection operator (LASSO) algorithm was applied to identify highly correlated clinical variables influencing cognitive function. Subsequently, these variables were integrated into a visualized nomogram model to facilitate early prediction of cognitive impairment (CI) risk. Performance evaluation of the model demonstrated high accuracy, consistency, and clinical applicability, significantly enhancing diagnostic efficiency for neurologists. Furthermore, the model provides visual comparisons of patient distributions across different predictor values, enabling personalized risk assessments. According to experimental analysis and verification, the model demonstrated outstanding prediction with a region under the ROC curve of 0.872 on the original training set and 0.870 on the validation set. Because the anticipated and observed probabilities were so consistent, the model was able to forecast the patient's likelihood of cognitive impairment.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100189"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-13DOI: 10.1016/j.neuri.2025.100188
Ibtihal Hassan Elshekhidris , Magdi B. M. Amien , Ahmed Fragoon
Background and objectives: The method of electroencephalography (EEG) is frequently employed to identify epileptic seizures. Visually inspecting nonlinear EEG waves is a very difficult and time-consuming process. Therefore, to help with patients' long-term assessment and treatment, an effective automatic detection system is required. Traditional methods of machine learning require step of feature extraction by manual which leads to time consuming, for it we modified in one-Dimensional convolution neural network architecture for features extraction and features dimension reduction for makes the classification low computational complexity and more accurate.
Methods: In this study, we did a comparison between three methods for classification: support vector machine, artificial neural network and one-dimensional convolution neural network. We used the stationary wavelet transform with mother function symlet2 for denoising EEG signal and used the empirical mode decomposition for signal decomposition. After that, features extraction step is necessary when used the support vector machine and artificial neural network, but when use the convolution neural network the features are extracted by layers.
Results: The highest value of a classification accuracy was 100%, and a sensitivity 100%, a specificity 100%, and a precision 100%, which appeared five times when using the one-dimensional convolution neural network after empirical mode decomposition method.
Conclusions: The efficiency of the three methods has been compared and evaluated by using four metrics: Accuracy, Sensitivity, specificity, and Precision, and the result showed the one-dimensional convolution neural network is the best method for classification with empirical mode decomposition.
{"title":"Automated classification of epileptic seizures using modified one-dimensional convolution neural network based on empirical mode decomposition with high accuracy","authors":"Ibtihal Hassan Elshekhidris , Magdi B. M. Amien , Ahmed Fragoon","doi":"10.1016/j.neuri.2025.100188","DOIUrl":"10.1016/j.neuri.2025.100188","url":null,"abstract":"<div><div>Background and objectives: The method of electroencephalography (EEG) is frequently employed to identify epileptic seizures. Visually inspecting nonlinear EEG waves is a very difficult and time-consuming process. Therefore, to help with patients' long-term assessment and treatment, an effective automatic detection system is required. Traditional methods of machine learning require step of feature extraction by manual which leads to time consuming, for it we modified in one-Dimensional convolution neural network architecture for features extraction and features dimension reduction for makes the classification low computational complexity and more accurate.</div><div>Methods: In this study, we did a comparison between three methods for classification: support vector machine, artificial neural network and one-dimensional convolution neural network. We used the stationary wavelet transform with mother function symlet2 for denoising EEG signal and used the empirical mode decomposition for signal decomposition. After that, features extraction step is necessary when used the support vector machine and artificial neural network, but when use the convolution neural network the features are extracted by layers.</div><div>Results: The highest value of a classification accuracy was 100%, and a sensitivity 100%, a specificity 100%, and a precision 100%, which appeared five times when using the one-dimensional convolution neural network after empirical mode decomposition method.</div><div>Conclusions: The efficiency of the three methods has been compared and evaluated by using four metrics: Accuracy, Sensitivity, specificity, and Precision, and the result showed the one-dimensional convolution neural network is the best method for classification with empirical mode decomposition.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 2","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143430131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-14DOI: 10.1016/j.neuri.2025.100186
Haewon Byeon , Udit Mahajan , Ashish Kumar , V. Rama Krishna , Mukesh Soni , Monika Bansal
Background: Manual visual inspection and analysis of electroencephalogram (EEG) signals of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO improved the categorization accuracy of both the EP (Evoked potential) and normal groups by automatically screening and optimizing the best feature combination of brain networks. Therefore, selecting effective EEG features for automatic recognition of EP is particularly important for Neuroscience.
New method: A phase synchronization index (PSI) brain stimulation is constructed from multi-channel EEG signals, extracting 15 topological features from the perspectives of network nodes and structural functions. In order to optimize and screen feature combinations in both single and cross-frequency bands, the GA-PSO algorithm is utilized as a feature selection tool for choosing epileptic EEG network features.
Result: Feature combinations are made both within and between bands, and the optimal feature mix is found using the PSO and GA-PSO algorithms. The study found that the GA-PSO algorithm outperformed the PSO algorithm, achieving a higher EP recognition accuracy of 0.9335 under cross-frequency band conditions.
Comparison with existing methods: The results indicate that the introduction of the genetic algorithm enables the GA-PSO algorithm to maintain population diversity and avoid premature convergence to local optima, thereby enhancing the search capabilities of the population.
Conclusion: Based on the findings, topological aspects provide a good way to describe the anomalies in the brain networks of epileptic patients and enhance the classification accuracy through combination, which provides help for pathological research and clinical diagnosis of epilepsy.
{"title":"EEG signal based brain stimulation model to detect epileptic neurological disorders","authors":"Haewon Byeon , Udit Mahajan , Ashish Kumar , V. Rama Krishna , Mukesh Soni , Monika Bansal","doi":"10.1016/j.neuri.2025.100186","DOIUrl":"10.1016/j.neuri.2025.100186","url":null,"abstract":"<div><div><strong>Background:</strong> Manual visual inspection and analysis of electroencephalogram (EEG) signals of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO improved the categorization accuracy of both the EP (Evoked potential) and normal groups by automatically screening and optimizing the best feature combination of brain networks. Therefore, selecting effective EEG features for automatic recognition of EP is particularly important for Neuroscience.</div><div><strong>New method:</strong> A phase synchronization index (PSI) brain stimulation is constructed from multi-channel EEG signals, extracting 15 topological features from the perspectives of network nodes and structural functions. In order to optimize and screen feature combinations in both single and cross-frequency bands, the GA-PSO algorithm is utilized as a feature selection tool for choosing epileptic EEG network features.</div><div><strong>Result:</strong> Feature combinations are made both within and between bands, and the optimal feature mix is found using the PSO and GA-PSO algorithms. The study found that the GA-PSO algorithm outperformed the PSO algorithm, achieving a higher EP recognition accuracy of 0.9335 under cross-frequency band conditions.</div><div><strong>Comparison with existing methods:</strong> The results indicate that the introduction of the genetic algorithm enables the GA-PSO algorithm to maintain population diversity and avoid premature convergence to local optima, thereby enhancing the search capabilities of the population.</div><div><strong>Conclusion:</strong> Based on the findings, topological aspects provide a good way to describe the anomalies in the brain networks of epileptic patients and enhance the classification accuracy through combination, which provides help for pathological research and clinical diagnosis of epilepsy.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100186"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-09DOI: 10.1016/j.neuri.2024.100184
Mustafa Halimeh , Michele Jackson , Tobias Loddenkemper , Christian Meisel
Objective: Wrist-worn wearable devices that monitor autonomous nervous system function and movement have shown promise in providing non-invasive, broadly applicable seizure forecasts that increase in accuracy with larger training size. Nevertheless, challenges related to missing validation, small number of enrolled patients, insufficient training data, and lack of patient seizure cycles data hinder its clinical implementation. Here we sought to prospectively validate a previously implemented seizure forecasting algorithm using a larger cohort of pediatric patients with epilepsy (pwe), improve it by including information on seizure cycles, and (3) assess the utility of precise power-laws to predict performance as a function of dataset size.
Methods: We used video-EEG recordings from 166 pwe as ground-truth for seizures, recorded electrodermal activity (EDA), peripheral body temperature (TEMP), blood volume pulse (BVP), accelerometery (ACC) and applied a deep neural LSTM network model (NN) on these data along with information on 24-hour cycles to forecast seizures in a leave-one-subject-out cross validation. Evaluations were made using improvement over chance (IoC) and the Brier skill score (BSS), which measured the improvement of the NN Brier score compared to the Brier score of a rate-matched random (RMR) forecast.
Results: Performance quantified by IoC and BSS increased with training data following precise power-law scaling laws, thereby exceeding prior reported performance levels from smaller datasets. Including information on 24-hour seizure cycles further improved performance. For the largest training set we achieved significant IoC in 68% of pwe, an IoC of 27.3% and a BSS of 0.087.
Interpretation: Our results validate a previous forecast approach and indicate that performance improves predictably as a function of dataset size following power-law scaling.
{"title":"Training size predictably improves machine learning-based epileptic seizure forecasting from wearables","authors":"Mustafa Halimeh , Michele Jackson , Tobias Loddenkemper , Christian Meisel","doi":"10.1016/j.neuri.2024.100184","DOIUrl":"10.1016/j.neuri.2024.100184","url":null,"abstract":"<div><div>Objective: Wrist-worn wearable devices that monitor autonomous nervous system function and movement have shown promise in providing non-invasive, broadly applicable seizure forecasts that increase in accuracy with larger training size. Nevertheless, challenges related to missing validation, small number of enrolled patients, insufficient training data, and lack of patient seizure cycles data hinder its clinical implementation. Here we sought to prospectively validate a previously implemented seizure forecasting algorithm using a larger cohort of pediatric patients with epilepsy (pwe), improve it by including information on seizure cycles, and (3) assess the utility of precise power-laws to predict performance as a function of dataset size.</div><div>Methods: We used video-EEG recordings from 166 pwe as ground-truth for seizures, recorded electrodermal activity (EDA), peripheral body temperature (TEMP), blood volume pulse (BVP), accelerometery (ACC) and applied a deep neural LSTM network model (NN) on these data along with information on 24-hour cycles to forecast seizures in a leave-one-subject-out cross validation. Evaluations were made using improvement over chance (IoC) and the Brier skill score (BSS), which measured the improvement of the NN Brier score compared to the Brier score of a rate-matched random (RMR) forecast.</div><div>Results: Performance quantified by IoC and BSS increased with training data following precise power-law scaling laws, thereby exceeding prior reported performance levels from smaller datasets. Including information on 24-hour seizure cycles further improved performance. For the largest training set we achieved significant IoC in 68% of pwe, an IoC of 27.3% and a BSS of 0.087.</div><div>Interpretation: Our results validate a previous forecast approach and indicate that performance improves predictably as a function of dataset size following power-law scaling.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100184"},"PeriodicalIF":0.0,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-29DOI: 10.1016/j.neuri.2024.100181
David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo
Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.
{"title":"Understanding risk factors of post-stroke mortality","authors":"David Castro , Nuno Antonio , Ana Marreiros , Hipólito Nzwalo","doi":"10.1016/j.neuri.2024.100181","DOIUrl":"10.1016/j.neuri.2024.100181","url":null,"abstract":"<div><div>Stroke is one of the leading causes of death worldwide. Understanding the risk factors for post-stroke mortality is crucial for improving patient outcomes. This study analyzes and predicts post-stroke mortality using the modified Rankin Scale (mRS), a functional neurological evaluation scale. Several Machine Learning models were developed and assessed using a dataset of 332 stroke patients from Hospital de Faro, Portugal, from 2016 to 2018. The Random Forest model outperformed others, achieving an accuracy of 98.5% and a recall of 91.3. Twenty-four risk factors were identified, with stroke severity as the most critical. These findings provide healthcare professionals with valuable tools for early identification and intervention for high-risk stroke patients, enabling informed decision-making and customized treatment plans. This research advances healthcare predictive analytics, offering a precise mortality prediction model and a comprehensive analysis of risk factors, potentially improving clinical outcomes and reducing mortality rates. Future applications could extend to patient monitoring and management across various medical conditions.</div></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"5 1","pages":"Article 100181"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143160181","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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