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Hu Y, Liu J, Sun R, Yu Y, Sui Y. Classification of epileptic seizures in EEG data based on iterative gated graph convolution network. Front Comput Neurosci 2024; 18:1454529. [PMID: 39268152 PMCID: PMC11390464 DOI: 10.3389/fncom.2024.1454529] [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: 06/25/2024] [Accepted: 08/09/2024] [Indexed: 09/15/2024] Open
Abstract
Introduction The automatic and precise classification of epilepsy types using electroencephalogram (EEG) data promises significant advancements in diagnosing patients with epilepsy. However, the intricate interplay among multiple electrode signals in EEG data poses challenges. Recently, Graph Convolutional Neural Networks (GCN) have shown strength in analyzing EEG data due to their capability to describe complex relationships among different EEG regions. Nevertheless, several challenges remain: (1) GCN typically rely on predefined or prior graph topologies, which may not accurately reflect the complex correlations between brain regions. (2) GCN struggle to capture the long-temporal dependencies inherent in EEG signals, limiting their ability to effectively extract temporal features. Methods To address these challenges, we propose an innovative epileptic seizure classification model based on an Iterative Gated Graph Convolutional Network (IGGCN). For the epileptic seizure classification task, the original EEG graph structure is iteratively optimized using a multi-head attention mechanism during training, rather than relying on a static, predefined prior graph. We introduce Gated Graph Neural Networks (GGNN) to enhance the model's capacity to capture long-term dependencies in EEG series between brain regions. Additionally, Focal Loss is employed to alleviate the imbalance caused by the scarcity of epileptic EEG data. Results Our model was evaluated on the Temple University Hospital EEG Seizure Corpus (TUSZ) for classifying four types of epileptic seizures. The results are outstanding, achieving an average F1 score of 91.5% and an average Recall of 91.8%, showing a substantial improvement over current state-of-the-art models. Discussion Ablation experiments verified the efficacy of iterative graph optimization and gated graph convolution. The optimized graph structure significantly differs from the predefined EEG topology. Gated graph convolutions demonstrate superior performance in capturing the long-term dependencies in EEG series. Additionally, Focal Loss outperforms other commonly used loss functions in the TUSZ classification task.
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Affiliation(s)
- Yue Hu
- College of Computer Science and Technology, University of Qingdao, Qingdao, China
| | - Jian Liu
- Yunxiao Road Outpatient Department, Qingdao Stomatological Hospital, Qingdao, China
| | - Rencheng Sun
- College of Computer Science and Technology, University of Qingdao, Qingdao, China
| | - Yongqiang Yu
- College of Computer Science and Technology, University of Qingdao, Qingdao, China
| | - Yi Sui
- College of Computer Science and Technology, University of Qingdao, Qingdao, China
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Delgado-Munoz J, Matsunaka R, Hiraki K. Classification of Known and Unknown Study Items in a Memory Task Using Single-Trial Event-Related Potentials and Convolutional Neural Networks. Brain Sci 2024; 14:860. [PMID: 39335356 PMCID: PMC11430714 DOI: 10.3390/brainsci14090860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2024] [Revised: 08/18/2024] [Accepted: 08/25/2024] [Indexed: 09/30/2024] Open
Abstract
This study examines the feasibility of using event-related potentials (ERPs) obtained from electroencephalographic (EEG) recordings as biomarkers for long-term memory item classification. Previous studies have identified old/new effects in memory paradigms associated with explicit long-term memory and familiarity. Recent advancements in convolutional neural networks (CNNs) have enabled the classification of ERP trials under different conditions and the identification of features related to neural processes at the single-trial level. We employed this approach to compare three CNN models with distinct architectures using experimental data. Participants (N = 25) performed an association memory task while recording ERPs that were used for training and validation of the CNN models. The EEGNET-based model achieved the most reliable performance in terms of precision, recall, and specificity compared with the shallow and deep convolutional approaches. The classification accuracy of this model reached 62% for known items and 66% for unknown items. Good overall accuracy requires a trade-off between recall and specificity and depends on the architecture of the model and the dataset size. These results suggest the possibility of integrating ERP and CNN into online learning tools and identifying the underlying processes related to long-term memorization.
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Affiliation(s)
- Jorge Delgado-Munoz
- Graduate School of Arts and Sciences, The University of Tokyo, Meguro-Ku, Tokyo 153-8902, Japan; (R.M.); (K.H.)
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Vu T, Petty T, Yakut K, Usman M, Xue W, Haas FM, Hirsh RA, Zhao X. Real-time arrhythmia detection using convolutional neural networks. Front Big Data 2023; 6:1270756. [PMID: 38058406 PMCID: PMC10696646 DOI: 10.3389/fdata.2023.1270756] [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: 08/01/2023] [Accepted: 10/24/2023] [Indexed: 12/08/2023] Open
Abstract
Cardiovascular diseases, such as heart attack and congestive heart failure, are the leading cause of death both in the United States and worldwide. The current medical practice for diagnosing cardiovascular diseases is not suitable for long-term, out-of-hospital use. A key to long-term monitoring is the ability to detect abnormal cardiac rhythms, i.e., arrhythmia, in real-time. Most existing studies only focus on the accuracy of arrhythmia classification, instead of runtime performance of the workflow. In this paper, we present our work on supporting real-time arrhythmic detection using convolutional neural networks, which take images of electrocardiogram (ECG) segments as input, and classify the arrhythmia conditions. To support real-time processing, we have carried out extensive experiments and evaluated the computational cost of each step of the classification workflow. Our results show that it is feasible to achieve real-time arrhythmic detection using convolutional neural networks. To further demonstrate the generalizability of this approach, we used the trained model with processed data collected by a customized wearable sensor from a lab setting, and the results shown that our approach is highly accurate and efficient. This research provides the potentials to enable in-home real-time heart monitoring based on 2D image data, which opens up opportunities for integrating both machine learning and traditional diagnostic approaches.
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Affiliation(s)
- Thong Vu
- School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States
| | - Tyler Petty
- School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States
| | - Kemal Yakut
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Muhammad Usman
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Wei Xue
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Francis M. Haas
- Department of Mechanical Engineering, Rowan University, Glassboro, NJ, United States
| | - Robert A. Hirsh
- Department of Anesthesiology, Cooper University Hospital, Camden, NJ, United States
| | - Xinghui Zhao
- School of Engineering and Computer Science, Washington State University, Vancouver, WA, United States
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Choi Y, Lee H. Interpretation of lung disease classification with light attention connected module. Biomed Signal Process Control 2023; 84:104695. [PMID: 36879856 PMCID: PMC9978539 DOI: 10.1016/j.bspc.2023.104695] [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: 08/05/2022] [Revised: 12/21/2022] [Accepted: 02/11/2023] [Indexed: 03/06/2023]
Abstract
Lung diseases lead to complications from obstructive diseases, and the COVID-19 pandemic has increased lung disease-related deaths. Medical practitioners use stethoscopes to diagnose lung disease. However, an artificial intelligence model capable of objective judgment is required since the experience and diagnosis of respiratory sounds differ. Therefore, in this study, we propose a lung disease classification model that uses an attention module and deep learning. Respiratory sounds were extracted using log-Mel spectrogram MFCC. Normal and five types of adventitious sounds were effectively classified by improving VGGish and adding a light attention connected module to which the efficient channel attention module (ECA-Net) was applied. The performance of the model was evaluated for accuracy, precision, sensitivity, specificity, f1-score, and balanced accuracy, which were 92.56%, 92.81%, 92.22%, 98.50%, 92.29%, and 95.4%, respectively. We confirmed high performance according to the attention effect. The classification causes of lung diseases were analyzed using gradient-weighted class activation mapping (Grad-CAM), and the performances of their models were compared using open lung sounds measured using a Littmann 3200 stethoscope. The experts' opinions were also included. Our results will contribute to the early diagnosis and interpretation of diseases in patients with lung disease by utilizing algorithms in smart medical stethoscopes.
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Affiliation(s)
- Youngjin Choi
- School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
| | - Hongchul Lee
- School of Industrial Management Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
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Albaqami H, Hassan GM, Datta A. MP-SeizNet: A multi-path CNN Bi-LSTM Network for seizure-type classification using EEG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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Massoud YM, Abdelzaher M, Kuhlmann L, Abd El Ghany MA. General and patient-specific seizure classification using deep neural networks. ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING 2023. [DOI: 10.1007/s10470-023-02153-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 01/04/2023] [Accepted: 02/22/2023] [Indexed: 09/02/2023]
Abstract
AbstractSeizure prediction algorithms have been central in the field of data analysis for the improvement of epileptic patients’ lives. The most recent advancements of which include the use of deep neural networks to present an optimized, accurate seizure prediction system. This work puts forth deep learning methods to automate the process of epileptic seizure detection with electroencephalogram (EEG) signals as input; both a patient-specific and general approach are followed. EEG signals are time structure series motivating the use of sequence algorithms such as temporal convolutional neural networks (TCNNs), and long short-term memory networks. We then compare this methodology to other prior pre-implemented structures, including our previous work for seizure prediction using machine learning approaches support vector machine and random under-sampling boost. Moreover, patient-specific and general seizure prediction approaches are used to evaluate the performance of the best algorithms. Area under curve (AUC) is used to select the best performing algorithm to account for the imbalanced dataset. The presented TCNN model showed the best patient-specific results than that of the general approach with, AUC of 0.73, while ML model had the best results for general classification with AUC of 0.75.
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Quiles V, Ferrero L, Iáñez E, Ortiz M, Gil-Agudo Á, Azorín JM. Brain-machine interface based on transfer-learning for detecting the appearance of obstacles during exoskeleton-assisted walking. Front Neurosci 2023; 17:1154480. [PMID: 36998726 PMCID: PMC10043233 DOI: 10.3389/fnins.2023.1154480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
IntroductionBrain-machine interfaces (BMIs) attempt to establish communication between the user and the device to be controlled. BMIs have great challenges to face in order to design a robust control in the real field of application. The artifacts, high volume of training data, and non-stationarity of the signal of EEG-based interfaces are challenges that classical processing techniques do not solve, showing certain shortcomings in the real-time domain. Recent advances in deep-learning techniques open a window of opportunity to solve some of these problems. In this work, an interface able to detect the evoked potential that occurs when a person intends to stop due to the appearance of an unexpected obstacle has been developed.Material and methodsFirst, the interface was tested on a treadmill with five subjects, in which the user stopped when an obstacle appeared (simulated by a laser). The analysis is based on two consecutive convolutional networks: the first one to discern the intention to stop against normal walking and the second one to correct false detections of the previous one.Results and discussionThe results were superior when using the methodology of the two consecutive networks vs. only the first one in a cross-validation pseudo-online analysis. The false positives per min (FP/min) decreased from 31.8 to 3.9 FP/min and the number of repetitions in which there were no false positives and true positives (TP) improved from 34.9% to 60.3% NOFP/TP. This methodology was tested in a closed-loop experiment with an exoskeleton, in which the brain-machine interface (BMI) detected an obstacle and sent the command to the exoskeleton to stop. This methodology was tested with three healthy subjects, and the online results were 3.8 FP/min and 49.3% NOFP/TP. To make this model feasible for non-able bodied patients with a reduced and manageable time frame, transfer-learning techniques were applied and validated in the previous tests, and were then applied to patients. The results for two incomplete Spinal Cord Injury (iSCI) patients were 37.9% NOFP/TP and 7.7 FP/min.
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Affiliation(s)
- Vicente Quiles
- Brain-Machine Interface Systems Lab, Universidad Miguel Hernández de Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche - I3E, Universidad Miguel Hernández de Elche, Elche, Spain
| | - Laura Ferrero
- Brain-Machine Interface Systems Lab, Universidad Miguel Hernández de Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche - I3E, Universidad Miguel Hernández de Elche, Elche, Spain
- The European University of Brain and Technology (NeurotechEU), European Union
| | - Eduardo Iáñez
- Brain-Machine Interface Systems Lab, Universidad Miguel Hernández de Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche - I3E, Universidad Miguel Hernández de Elche, Elche, Spain
- *Correspondence: Eduardo Iáñez
| | - Mario Ortiz
- Brain-Machine Interface Systems Lab, Universidad Miguel Hernández de Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche - I3E, Universidad Miguel Hernández de Elche, Elche, Spain
- The European University of Brain and Technology (NeurotechEU), European Union
| | - Ángel Gil-Agudo
- Biomechanics Unit of the National Paraplegic Hospital, Toledo, Spain
| | - José M. Azorín
- Brain-Machine Interface Systems Lab, Universidad Miguel Hernández de Elche, Elche, Spain
- Instituto de Investigación en Ingeniería de Elche - I3E, Universidad Miguel Hernández de Elche, Elche, Spain
- The European University of Brain and Technology (NeurotechEU), European Union
- ValGRAI: Valencian Graduated School and Research Network of Artificial Intelligence, Valencia, Spain
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Li Q, Liu Y, Liu Q, Zhang Q, Yan F, Ma Y, Zhang X. Multidimensional Feature in Emotion Recognition Based on Multi-Channel EEG Signals. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1830. [PMID: 36554234 PMCID: PMC9778308 DOI: 10.3390/e24121830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
As a major daily task for the popularization of artificial intelligence technology, more and more attention has been paid to the scientific research of mental state electroencephalogram (EEG) in recent years. To retain the spatial information of EEG signals and fully mine the EEG timing-related information, this paper proposes a novel EEG emotion recognition method. First, to obtain the frequency, spatial, and temporal information of multichannel EEG signals more comprehensively, we choose the multidimensional feature structure as the input of the artificial neural network. Then, a neural network model based on depthwise separable convolution is proposed, extracting the input structure's frequency and spatial features. The network can effectively reduce the computational parameters. Finally, we modeled using the ordered neuronal long short-term memory (ON-LSTM) network, which can automatically learn hierarchical information to extract deep emotional features hidden in EEG time series. The experimental results show that the proposed model can reasonably learn the correlation and temporal dimension information content between EEG multi-channel and improve emotion classification performance. We performed the experimental validation of this paper in two publicly available EEG emotional datasets. In the experiments on the DEAP dataset (a dataset for emotion analysis using EEG, physiological, and video signals), the mean accuracy of emotion recognition for arousal and valence is 95.02% and 94.61%, respectively. In the experiments on the SEED dataset (a dataset collection for various purposes using EEG signals), the average accuracy of emotion recognition is 95.49%.
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Affiliation(s)
- Qi Li
- Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Yunqing Liu
- Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Quanyang Liu
- Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Qiong Zhang
- Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Fei Yan
- Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Yimin Ma
- Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130000, China
| | - Xinyu Zhang
- Economics School, Jilin University, Changchun 130000, China
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Sharma A, Singh K, Koundal D. A novel fusion based convolutional neural network approach for classification of COVID-19 from chest X-ray images. Biomed Signal Process Control 2022; 77:103778. [PMID: 35530169 PMCID: PMC9057938 DOI: 10.1016/j.bspc.2022.103778] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/09/2022] [Accepted: 04/27/2022] [Indexed: 01/31/2023]
Abstract
Coronavirus disease is a viral infection caused by a novel coronavirus (CoV) which was first identified in the city of Wuhan, China somewhere in the early December 2019. It affects the human respiratory system by causing respiratory infections with symptoms (mild to severe) like fever, cough, and weakness but can further lead to other serious diseases and has resulted in millions of deaths until now. Therefore, an accurate diagnosis for such types of diseases is highly needful for the current healthcare system. In this paper, a state of the art deep learning method is described. We propose COVDC-Net, a Deep Convolutional Network-based classification method which is capable of identifying SARS-CoV-2 infected amongst healthy and/or pneumonia patients from their chest X-ray images. The proposed method uses two modified pre-trained models (on ImageNet) namely MobileNetV2 and VGG16 without their classifier layers and fuses the two models using the Confidence fusion method to achieve better classification accuracy on the two currently publicly available datasets. It is observed through exhaustive experiments that the proposed method achieved an overall classification accuracy of 96.48% for 3-class (COVID-19, Normal and Pneumonia) classification tasks. For 4-class classification (COVID-19, Normal, Pneumonia Viral, and Pneumonia Bacterial) COVDC-Net method delivered 90.22% accuracy. The experimental results demonstrate that the proposed COVDC-Net method has shown better overall classification accuracy as compared to the existing deep learning methods proposed for the same task in the current COVID-19 pandemic.
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Affiliation(s)
- Anubhav Sharma
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Karamjeet Singh
- Computer Science and Engineering Department, Thapar Institute of Engineering and Technology, Patiala, Punjab, India
| | - Deepika Koundal
- Department of Virtualization, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, Uttrakhand, India
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A Novel Stress State Assessment Method for College Students Based on EEG. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4565968. [PMID: 35712070 PMCID: PMC9197644 DOI: 10.1155/2022/4565968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/22/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
Abstract
Stress is an unavoidable problem for today's college students. Stress can arouse strong personal emotional and behavioral responses. Compared with other groups of the same age, college students have a special way of life and living environment. They have complex interpersonal relationships and relatively weak social support systems. At the same time, they also face fierce competition in both academic and employment. However, they lack the skills to deal with the crisis and are reluctant to ask others for help, which leads to a simultaneous increase in mental stress. The pressure on college students mainly comes from study, family, social, employment, society, and economy. When students face multiple pressures from family, school, society, etc., some students are prone to some psychological problems due to their own personality or external environment and other reasons. Therefore, regular assessment of students' stress status is an important means to prevent college students' psychological problems. Considering that in real life, the number of students whose pressure is within the tolerable range is the majority, while the number of students who are under too much pressure is a minority. Therefore, the actual dataset to be identified belongs to a kind of imbalanced data. In this study, an improved extreme learning machine (IELM) is used to improve the performance of the recognition model as much as possible. IELM takes the idea of label weighting as the starting point, introduces the AdaBoost algorithm, and combines its weight distribution with the label weighted extreme learning machine (ELM). During the weight update process, the advantage of the imbalanced nature of multi-label datasets is taken. IELM was used to classify EEG data to determine the stress level of college students. The experimental results demonstrate that the algorithm used in this study has excellent classification performance and can accurately assess students' stress levels. The accurate assessment of stress has provided a solid foundation for the development of students' mental health and has significant practical implications.
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Kim Y, Hyon Y, Lee S, Woo SD, Ha T, Chung C. The coming era of a new auscultation system for analyzing respiratory sounds. BMC Pulm Med 2022; 22:119. [PMID: 35361176 PMCID: PMC8969404 DOI: 10.1186/s12890-022-01896-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/20/2022] [Indexed: 01/28/2023] Open
Abstract
Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea
| | - YunKyong Hyon
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea
| | - Sunju Lee
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea
| | - Seong-Dae Woo
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea
| | - Taeyoung Ha
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea.
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea. .,Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.
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Abstract
Deep learning models, such as convolutional neural networks (CNN), have been successfully applied in pattern recognition and system identification recent years. But for the cases of missing data and big noises, CNN does not work well for dynamic system modeling. In this paper, complex valued convolution neural network (CVCNN) is presented for modeling nonlinear systems with large uncertainties. Novel training methods are proposed for CVCNN. Comparisons with other classical neural networks are made to show the advantages of the proposed methods.
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Kim Y, Hyon Y, Jung SS, Lee S, Yoo G, Chung C, Ha T. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Sci Rep 2021; 11:17186. [PMID: 34433880 PMCID: PMC8387488 DOI: 10.1038/s41598-021-96724-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respiratory sounds with a stethoscope is important in diagnosing respiratory diseases and providing first aid. However, accurate interpretation of respiratory sounds requires clinician's considerable expertise, so trainees such as interns and residents sometimes misidentify respiratory sounds. To overcome such limitations, we tried to develop an automated classification of breath sounds. We utilized deep learning convolutional neural network (CNN) to categorize 1918 respiratory sounds (normal, crackles, wheezes, rhonchi) recorded in the clinical setting. We developed the predictive model for respiratory sound classification combining pretrained image feature extractor of series, respiratory sound, and CNN classifier. It detected abnormal sounds with an accuracy of 86.5% and the area under the ROC curve (AUC) of 0.93. It further classified abnormal lung sounds into crackles, wheezes, or rhonchi with an overall accuracy of 85.7% and a mean AUC of 0.92. On the other hand, as a result of respiratory sound classification by different groups showed varying degree in terms of accuracy; the overall accuracies were 60.3% for medical students, 53.4% for interns, 68.8% for residents, and 80.1% for fellows. Our deep learning-based classification would be able to complement the inaccuracies of clinicians' auscultation, and it may aid in the rapid diagnosis and appropriate treatment of respiratory diseases.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - YunKyong Hyon
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Sung Soo Jung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Sunju Lee
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea
| | - Geon Yoo
- Clinical Research Division, National Institute of Food and Drug Safety Evaluation, Cheongju-si, Chungcheongbuk-do, Republic of Korea
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Republic of Korea. .,Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.
| | - Taeyoung Ha
- Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea.
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