1
|
Cheng H, Yang Y, Shi J, Li Z, Feng Y, Wang X. Comparison of automated deep neural network against manual sleep stage scoring in clinical data. Comput Biol Med 2024; 179:108855. [PMID: 39029432 DOI: 10.1016/j.compbiomed.2024.108855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVE To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines. METHODS Sleep recordings from 104 participants were analyzed by a convolutional neural network (CNN), the Somnolyzer and skillful technicians. Evaluation metrics were derived for different combinations of sleep stages. A further comparison between the Somnolyzer and the CNN model using a single-channel signal as input was also performed. Sleep recordings from 263 participants with a lower prevalence of OSA served as a cross-validation dataset to validate the generalizability of the CNN model. RESULTS The overall agreement between automated and manual scoring for sleep staging in 104 participants outperformed that of the Somnolyzer according to various metrics (accuracy: 81.81 % vs. 77.07 %; F1: 76.36 % vs. 73.80 %; Cohen's kappa: 0.7403 vs. 0.6848). The results showed that the left electrooculography (EOG) single-channel model had minor advantages over the Somnolyzer. In terms of consistency with manual sleep staging, the CNN model demonstrated superior performance in identifying more pronounced sleep transitions, particularly in the N2 stage and sleep latency metrics. Conversely, the Somnolyzer showed enhanced proficiency in the analysis of REM stages, notably in measuring REM latency. The accuracy in the cross-validation set of 263 participants was also above 80 %. CONCLUSIONS The CNN-based automated deep neural network outperformed the Somnolyzer and is sufficiently accurate for sleep study analyses using the AASM classification criteria.
Collapse
Affiliation(s)
- Hanrong Cheng
- Department of Sleep Medicine, Institute of Respiratory Diseases, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University of Science and Technology, Shenzhen, 518020, China.
| | - Yifei Yang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Jingshu Shi
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Zhangbo Li
- Shenzhen Gianta Information Technology Co., LTD, Shenzhen, 518048, China
| | - Yang Feng
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China
| | - Xingjun Wang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.
| |
Collapse
|
2
|
Jiang C, Xie W, Zheng J, Yan B, Luo J, Zhang J. MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice. BIOSENSORS 2024; 14:406. [PMID: 39194635 DOI: 10.3390/bios14080406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 08/20/2024] [Accepted: 08/20/2024] [Indexed: 08/29/2024]
Abstract
Over the past decades, feature-based statistical machine learning and deep neural networks have been extensively utilized for automatic sleep stage classification (ASSC). Feature-based approaches offer clear insights into sleep characteristics and require low computational power but often fail to capture the spatial-temporal context of the data. In contrast, deep neural networks can process raw sleep signals directly and deliver superior performance. However, their overfitting, inconsistent accuracy, and computational cost were the primary drawbacks that limited their end-user acceptance. To address these challenges, we developed a novel neural network model, MLS-Net, which integrates the strengths of neural networks and feature extraction for automated sleep staging in mice. MLS-Net leverages temporal and spectral features from multimodal signals, such as EEG, EMG, and eye movements (EMs), as inputs and incorporates a bidirectional Long Short-Term Memory (bi-LSTM) to effectively capture the spatial-temporal nonlinear characteristics inherent in sleep signals. Our studies demonstrate that MLS-Net achieves an overall classification accuracy of 90.4% and REM state precision of 91.1%, sensitivity of 84.7%, and an F1-Score of 87.5% in mice, outperforming other neural network and feature-based algorithms in our multimodal dataset.
Collapse
Affiliation(s)
- Chengyong Jiang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Wenbin Xie
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Jiadong Zheng
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Biao Yan
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Junwen Luo
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| | - Jiayi Zhang
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science, Institute for Medical and Engineering Innovation, Department of Ophthalmology and Vision Science, Eye & ENT Hospital, Fudan University, Shanghai 200032, China
| |
Collapse
|
3
|
Bi X, Zhang T. Pedagogical sentiment analysis based on the BERT-CNN-BiGRU-attention model in the context of intercultural communication barriers. PeerJ Comput Sci 2024; 10:e2166. [PMID: 38983236 PMCID: PMC11232589 DOI: 10.7717/peerj-cs.2166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Accepted: 06/09/2024] [Indexed: 07/11/2024]
Abstract
Amid the wave of globalization, the phenomenon of cultural amalgamation has surged in frequency, bringing to the fore the heightened prominence of challenges inherent in cross-cultural communication. To address these challenges, contemporary research has shifted its focus to human-computer dialogue. Especially in the educational paradigm of human-computer dialogue, analysing emotion recognition in user dialogues is particularly important. Accurately identify and understand users' emotional tendencies and the efficiency and experience of human-computer interaction and play. This study aims to improve the capability of language emotion recognition in human-computer dialogue. It proposes a hybrid model (BCBA) based on bidirectional encoder representations from transformers (BERT), convolutional neural networks (CNN), bidirectional gated recurrent units (BiGRU), and the attention mechanism. This model leverages the BERT model to extract semantic and syntactic features from the text. Simultaneously, it integrates CNN and BiGRU networks to delve deeper into textual features, enhancing the model's proficiency in nuanced sentiment recognition. Furthermore, by introducing the attention mechanism, the model can assign different weights to words based on their emotional tendencies. This enables it to prioritize words with discernible emotional inclinations for more precise sentiment analysis. The BCBA model has achieved remarkable results in emotion recognition and classification tasks through experimental validation on two datasets. The model has significantly improved both accuracy and F1 scores, with an average accuracy of 0.84 and an average F1 score of 0.8. The confusion matrix analysis reveals a minimal classification error rate for this model. Additionally, as the number of iterations increases, the model's recall rate stabilizes at approximately 0.7. This accomplishment demonstrates the model's robust capabilities in semantic understanding and sentiment analysis and showcases its advantages in handling emotional characteristics in language expressions within a cross-cultural context. The BCBA model proposed in this study provides effective technical support for emotion recognition in human-computer dialogue, which is of great significance for building more intelligent and user-friendly human-computer interaction systems. In the future, we will continue to optimize the model's structure, improve its capability in handling complex emotions and cross-lingual emotion recognition, and explore applying the model to more practical scenarios to further promote the development and application of human-computer dialogue technology.
Collapse
Affiliation(s)
- Xin Bi
- School of Literature, Heilongjiang University, Harbin, Heilongjiang, China
| | - Tian Zhang
- Department of Modern Languages, The University of Mississippi, Mississippi, United States of America
| |
Collapse
|
4
|
Satapathy SK, Brahma B, Panda B, Barsocchi P, Bhoi AK. Machine learning-empowered sleep staging classification using multi-modality signals. BMC Med Inform Decis Mak 2024; 24:119. [PMID: 38711099 DOI: 10.1186/s12911-024-02522-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/29/2024] [Indexed: 05/08/2024] Open
Abstract
The goal is to enhance an automated sleep staging system's performance by leveraging the diverse signals captured through multi-modal polysomnography recordings. Three modalities of PSG signals, namely electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), were considered to obtain the optimal fusions of the PSG signals, where 63 features were extracted. These include frequency-based, time-based, statistical-based, entropy-based, and non-linear-based features. We adopted the ReliefF (ReF) feature selection algorithms to find the suitable parts for each signal and superposition of PSG signals. Twelve top features were selected while correlated with the extracted feature sets' sleep stages. The selected features were fed into the AdaBoost with Random Forest (ADB + RF) classifier to validate the chosen segments and classify the sleep stages. This study's experiments were investigated by obtaining two testing schemes: epoch-wise testing and subject-wise testing. The suggested research was conducted using three publicly available datasets: ISRUC-Sleep subgroup1 (ISRUC-SG1), sleep-EDF(S-EDF), Physio bank CAP sleep database (PB-CAPSDB), and S-EDF-78 respectively. This work demonstrated that the proposed fusion strategy overestimates the common individual usage of PSG signals.
Collapse
Affiliation(s)
- Santosh Kumar Satapathy
- Department of Information and Communication Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, 382007, India.
| | - Biswajit Brahma
- McKesson Corporation, 1 Post St, San Francisco, CA, 94104, USA
| | - Baidyanath Panda
- LTIMindtree, 1 American Row, 3Rd Floor, Hartford, CT, 06103, USA
| | - Paolo Barsocchi
- Institute of Information Science and Technologies, National Research Council, 56124, Pisa, Italy.
| | - Akash Kumar Bhoi
- Directorate of Research, Sikkim Manipal University, Gangtok, 737102, Sikkim, India.
| |
Collapse
|
5
|
Yue H, Chen Z, Guo W, Sun L, Dai Y, Wang Y, Ma W, Fan X, Wen W, Lei W. Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice. Sleep Med Rev 2024; 74:101897. [PMID: 38306788 DOI: 10.1016/j.smrv.2024.101897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/30/2023] [Accepted: 01/04/2024] [Indexed: 02/04/2024]
Abstract
Over the past few decades, researchers have attempted to simplify and accelerate the process of sleep stage classification through various approaches; however, only a few such approaches have gained widespread acceptance. Artificial intelligence technology, particularly deep learning, is promising for earning the trust of the sleep medicine community in automated sleep-staging systems, thus facilitating its application in clinical practice and integration into daily life. We aimed to comprehensively review the latest methods that are applying deep learning for enhancing sleep staging efficiency and accuracy. Starting from the requisite "data" for constructing deep learning algorithms, we elucidated the current landscape of this domain and summarized the fundamental modeling process, encompassing signal selection, data pre-processing, model architecture, classification tasks, and performance metrics. Furthermore, we reviewed the applications of automated sleep staging in scenarios such as sleep-disorder screening, diagnostic procedures, and health monitoring and management. Finally, we conducted an in-depth analysis and discussion of the challenges and future in intelligent sleep staging, particularly focusing on large-scale sleep datasets, interdisciplinary collaborations, and human-computer interactions.
Collapse
Affiliation(s)
- Huijun Yue
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Zhuqi Chen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenbin Guo
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Lin Sun
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Yidan Dai
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Yiming Wang
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China
| | - Wenjun Ma
- School of Computer Science, South China Normal University, Guangzhou, People's Republic of China
| | - Xiaomao Fan
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, People's Republic of China
| | - Weiping Wen
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China; Department of Otolaryngology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| | - Wenbin Lei
- Otorhinolaryngology Hospital, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People's Republic of China.
| |
Collapse
|
6
|
Du X, Meng Y, Qiu S, Lv Y, Liu Q. EEG Emotion Recognition by Fusion of Multi-Scale Features. Brain Sci 2023; 13:1293. [PMID: 37759894 PMCID: PMC10526490 DOI: 10.3390/brainsci13091293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/27/2023] [Accepted: 08/29/2023] [Indexed: 09/29/2023] Open
Abstract
Electroencephalogram (EEG) signals exhibit low amplitude, complex background noise, randomness, and significant inter-individual differences, which pose challenges in extracting sufficient features and can lead to information loss during the mapping process from low-dimensional feature matrices to high-dimensional ones in emotion recognition algorithms. In this paper, we propose a Multi-scale Deformable Convolutional Interacting Attention Network based on Residual Network (MDCNAResnet) for EEG-based emotion recognition. Firstly, we extract differential entropy features from different channels of EEG signals and construct a three-dimensional feature matrix based on the relative positions of electrode channels. Secondly, we utilize deformable convolution (DCN) to extract high-level abstract features by replacing standard convolution with deformable convolution, enhancing the modeling capability of the convolutional neural network for irregular targets. Then, we develop the Bottom-Up Feature Pyramid Network (BU-FPN) to extract multi-scale data features, enabling complementary information from different levels in the neural network, while optimizing the feature extraction process using Efficient Channel Attention (ECANet). Finally, we combine the MDCNAResnet with a Bidirectional Gated Recurrent Unit (BiGRU) to further capture the contextual semantic information of EEG signals. Experimental results on the DEAP dataset demonstrate the effectiveness of our approach, achieving accuracies of 98.63% and 98.89% for Valence and Arousal dimensions, respectively.
Collapse
Affiliation(s)
- Xiuli Du
- Communication and Network Laboratory, Dalian University, Dalian 116622, China; (Y.M.); (S.Q.); (Y.L.); (Q.L.)
- School of Information Engineering, Dalian University, Dalian 116622, China
| | - Yifei Meng
- Communication and Network Laboratory, Dalian University, Dalian 116622, China; (Y.M.); (S.Q.); (Y.L.); (Q.L.)
- School of Information Engineering, Dalian University, Dalian 116622, China
| | - Shaoming Qiu
- Communication and Network Laboratory, Dalian University, Dalian 116622, China; (Y.M.); (S.Q.); (Y.L.); (Q.L.)
- School of Information Engineering, Dalian University, Dalian 116622, China
| | - Yana Lv
- Communication and Network Laboratory, Dalian University, Dalian 116622, China; (Y.M.); (S.Q.); (Y.L.); (Q.L.)
- School of Information Engineering, Dalian University, Dalian 116622, China
| | - Qingli Liu
- Communication and Network Laboratory, Dalian University, Dalian 116622, China; (Y.M.); (S.Q.); (Y.L.); (Q.L.)
- School of Information Engineering, Dalian University, Dalian 116622, China
| |
Collapse
|
7
|
Albaqami H, Hassan GM, Datta A. Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM. SENSORS (BASEL, SWITZERLAND) 2023; 23:5960. [PMID: 37447810 DOI: 10.3390/s23135960] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/23/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023]
Abstract
Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet-Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.
Collapse
Affiliation(s)
- Hezam Albaqami
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia
- Department of Computer Science and Artificial Intelligence, University of Jeddah, Jeddah 201589, Saudi Arabia
| | - Ghulam Mubashar Hassan
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia
| | - Amitava Datta
- Department of Computer Science and Software Engineering, The University of Western Australia, Perth 6009, Australia
| |
Collapse
|