Emotion recognition refers to the process of determining and identifying an individual's current emotional state by analyzing various signals such as voice, facial expressions, and physiological indicators etc. Using electroencephalogram (EEG) signals and virtual reality (VR) technology for emotion recognition research helps to better understand human emotional changes, enabling applications in areas such as psychological therapy, education, and training to enhance people’s quality of life. However, there is a lack of comprehensive review literature summarizing the combined researches of EEG signals and VR environments for emotion recognition. Therefore, this paper summarizes and synthesizes relevant research from the past five years. Firstly, it introduces the relevant theories of VR and EEG signal emotion recognition. Secondly, it focuses on the analysis of emotion induction, feature extraction, and classification methods in emotion recognition using EEG signals within VR environments. The article concludes by summarizing the research’s application directions and providing an outlook on future development trends, aiming to serve as a reference for researchers in related fields.
Sleep apnea causes cardiac arrest, sleep rhythm disorders, nocturnal hypoxia and abnormal blood pressure fluctuations in patients, which eventually lead to nocturnal target organ damage in hypertensive patients. The incidence of obstructive sleep apnea hypopnea syndrome (OSAHS) is extremely high, which seriously affects the physical and mental health of patients. This study attempts to extract features associated with OSAHS from 24-hour ambulatory blood pressure data and identify OSAHS by machine learning models for the differential diagnosis of this disease. The study data were obtained from ambulatory blood pressure examination data of 339 patients collected in outpatient clinics of the Chinese PLA General Hospital from December 2018 to December 2019, including 115 patients with OSAHS diagnosed by polysomnography (PSG) and 224 patients with non-OSAHS. Based on the characteristics of clinical changes of blood pressure in OSAHS patients, feature extraction rules were defined and algorithms were developed to extract features, while logistic regression and lightGBM models were then used to classify and predict the disease. The results showed that the identification accuracy of the lightGBM model trained in this study was 80.0%, precision was 82.9%, recall was 72.5%, and the area under the working characteristic curve (AUC) of the subjects was 0.906. The defined ambulatory blood pressure features could be effectively used for identifying OSAHS. This study provides a new idea and method for OSAHS screening.
ObjectiveTo systematically evaluate the risk prediction models of acute kidney injury in patients with acute coronary syndrome (ACS) based on machine learning, providing a reference for clinical selection of appropriate risk assessment tools. MethodsClinical studies using machine learning methods for predicting the risk of acute kidney injury in ACS patients were retrieved from PubMed, Cochrane Library, EMbase, Web of Science core database, CNKI, Wanfang Database, Chinese Biomedical Literature Database, and VIP Journal Database. The retrieval time was from the establishment of the database to May 24, 2025, and the quality of the model was evaluated using the prediction model bias risk assessment tool. ResultsNine articles were included, using 20 machine learning methods to construct 58 prediction models. The area under the receiver operating characteristic curve ranged from 0.740 to 0.894. The most commonly used predictors were age and creatinine. The overall bias risk of the included studies was relatively high, but the applicability was good. Conclusion: Machine learning models can identify the risk of acute kidney injury in ACS patients. All models have good predictive potential, but they are still in the development stage. It is recommended that future studies adopt prospective research and external validation to improve the stability and predictive accuracy of the model.
Hypertension is the primary disease that endangers human health. A convenient and accurate blood pressure measurement method can help to prevent the hypertension. This paper proposed a continuous blood pressure measurement method based on facial video signal. Firstly, color distortion filtering and independent component analysis were used to extract the video pulse wave of the region of interest in the facial video signal, and the multi-dimensional feature extraction of the pulse wave was preformed based on the time-frequency domain and physiological principles; Secondly, an integrated feature selection method was designed to extract the universal optimal feature subset; After that, we compared the single person blood pressure measurement models established by Elman neural network based on particle swarm optimization, support vector machine (SVM) and deep belief network; Finally, we used SVM algorithm to build a general blood pressure prediction model, which was compared and evaluated with the real blood pressure value. The experimental results showed that the blood pressure measurement results based on facial video were in good agreement with the standard blood pressure values. Comparing the estimated blood pressure from the video with standard blood pressure value, the mean absolute error (MAE) of systolic blood pressure was 4.9 mm Hg with a standard deviation (STD) of 5.9 mm Hg, and the MAE of diastolic blood pressure was 4.6 mm Hg with a STD of 5.0 mm Hg, which met the AAMI standards. The non-contact blood pressure measurement method based on video stream proposed in this paper can be used for blood pressure measurement.
Objective To establish a machine learning-based risk prediction model of combined chronic obstructive pulmonary disease (COPD) with lung cancer, so as to explore the high risk factors for COPD patients with lung cancer and to lay the foundation for early detection of lung cancer risk in COPD patients. Methods A total of 154 patients from the Second Hospital of Dalian Medical University from 2010 to 2021 were retrospectively analyzed, including 99 patients in the COPD group and 55 patients in the COPD with lung cancer group. the chest high resolution computed tomography (HRCT) scans and pulmonary function test of each patient were acquired. The main analyses were as follow: (1) to valid the statistically differences of the basic information (such as age, body mass index, smoking index), laboratory test results, pulmonary function parameters and quantitative parameters of chest HRCT between the two groups; (2) to analyze the indicators of high risk factors for lung cancer in COPD patients using univariate and binary logistic regression (LR) methods; and (3) to establish the machine learning model (such as LR and Gaussian process) for COPD with lung cancer patients. Results Based on the statistical analysis and LR methods, decreased BMI, increased whole lung emphysema index, increased whole lung mean density, and increased percentage activity of exertional spirometry and prothrombin time were risk factors for COPD with lung cancer patients. Based on the machine learning prediction model for COPD with lung cancer patients, the area under the receiver operating characteristic curve for LR and Gaussian process were obtained as 0.88 using the soluble fragments of prothrombin time percentage activity, whole lung emphysema index, whole lung mean density, and forced vital capacity combined with neuron-specific enolase and cytokeratin 19 as features. Conclusion The prediction model of COPD with lung cancer patients using a machine learning approach can be used for early detection of lung cancer risk in COPD patients.
Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% (P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet’s 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
ObjectiveTo systematically evaluate the clinical value of machine learning (ML) for predicting the neurological outcome of out-of-hospital cardiac arrest (OHCA), and to develop a prediction model. MethodsWe searched the PubMed, Web of Science, EMbase, CNKI, Wanfang database from January 1, 2011 to November 24, 2021. Studies on ML for predicting neurological outcomes in OHCA pateints were collected. Two researchers independently screened the literature, extracted the data and evaluated the bias of the included literature, evaluated the accuracy of different models and compared the area under the receiver operating characteristic curve (AUC). ResultsA total of 20 studies were included. Eleven of the studies were from open source databases and nine were from retrospective studies. Sixteen studies directly predicted OHCA neurological outcomes, and four predicted OHCA neurological outcomes after target temperature management. A total of seven ML algorithms were used, among which neural network was the ML algorithm with the highest frequency (n=5), followed by support vector machine and random forest (n=4). Three papers used multiple algorithms. The most frequently used input characteristic was age (n=19), followed by heart rate (n=17) and gender (n=13). A total of 4 studies compared the predictive value of ML with other classical statistical models, and the AUC value of ML model was higher than that of classical statistical models. ConclusionExisting evidence suggests that ML can more accurately predict OHCA nervous system outcomes, and the predictive performance of ML is superior to traditional statistical models in certain situations.
Systematic reviews can provide important evidence support for clinical practice and health decision-making. In this process, literature screening and data extraction are extensively time-consuming procedures. Natural language processing (NLP), as one of the research directions of computer science and artificial intelligence, can accelerate the process of literature screening and data extraction in systematic reviews. This paper introduced the requirements of systematic reviews for rapid literature screening and data extraction, the development of NLP and types of machine learning; and systematically collated the NLP tools for the title and abstract screening, full-text screening and data extraction in systematic reviews; and discussed the problems in the application of NLP tools in the field of systematic reviews and proposed a prospect for its future development.
This paper expounds the classification and characteristics of healthcare-associated infections (HAI) surveillance systems from the perspective of the informatization needs of HAI monitoring, explains the determination requirements of numerator and denominator in the surveillance statistical data, and introduces the regular verification for auditing the quality of HAI surveillance. The basic knowledge of machine learning and its achievements are introduced in processing surveillance data as well. Machine learning may become the mainstream algorithm of HAI automatic monitoring system in the future. Infection control professionals should learn relevant knowledge, cooperate with computer engineers and data analysts to establish more effective, reasonable and accurate monitoring systems, and improve the outcomes of HAI prevention and control in medical institutions.
Alzheimer’s disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject’s MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.