Background Video electroencephalographies(VEEGs)are often affected by artifacts,which can diminish clinicians’efficiency in interpreting VEEG data and potentially result in diagnostic errors.Conversely,certain ictal ...Background Video electroencephalographies(VEEGs)are often affected by artifacts,which can diminish clinicians’efficiency in interpreting VEEG data and potentially result in diagnostic errors.Conversely,certain ictal VEEG artifacts caused by automatisms,such as rhythmic chewing and blinking,may offer significant diagnostic clues for temporal lobe epilepsy.To address the challenges mentioned above,this study aims to develop an algorithm capable of automatically identifying and classifying artifact types,assisting clinicians in distinguishing interference signals from seizure signals.Methods This paper proposes a Local-Global Feature Fusion Network based on Time-Domain and Time-Frequency Domain(LG-TDTFD-Net)for detecting and classifying temporal lobe epilepsy artifacts.The model processes time-domain and time-frequency signals separately,leveraging convolutional neural network(CNN)to extract local features from both domains.Additionally,convolution operations are performed on the features extracted by the Transformer to capture deep local features,resulting in a feature set that simultaneously encompasses global,deep,and shallow local information.Results Experimental results demonstrate that the proposed method excels in detecting and classifying temporal lobe epilepsy artifacts,outperforming existing baseline models on clinical and public datasets.Conclusions The method effectively exploits the complementarity among the three feature types by utilizing feature concatenation and CNN-based fusion,enhancing feature representation and improving model generalization.展开更多
Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of a...Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.展开更多
Objective:Ballistocardiogram(BCG)is a kind of signal that reflects the movement of human body caused by the mechanical activity of cardiovascular system,especially during the heart contraction.Compared to other method...Objective:Ballistocardiogram(BCG)is a kind of signal that reflects the movement of human body caused by the mechanical activity of cardiovascular system,especially during the heart contraction.Compared to other methods on assessing vascular healthy condition,the acquisition of BCG didn't need any direct contact with human body.This paper uses Hilbert-Huang transformation(HHT)to calculate the heart rate and detect the artifacts.Methods:HHT was a newly-developed method for non-linear data analysis,and ensemble empirical mode decomposition(EEMD)based HHT was a modified HHT method which used white noise to improve the analysis result.A device that could record BCG signal and ECG signal synchronously was built in our lab and 10 subjects'signals were collected and analyzed.EEMD based HHT was applied to BCG signal to calculate the heart rate.Heart rate calculated using ECG was used as a standard value to verify the result calculated from BCG.Besides,BCG was easily affected by the body movement,so we tried to use HHT to detect the artifacts in the BCG signal.Results:Our research showed that EEMD based HHT with a proper white noise level could be used to calculate the heart rate in BCG.Artifacts in the decomposition component were enhanced in the decomposition components of EEMD and became easier to detect than that in original BCG signal.Conclusion:Therefore,HHT could help to calculate the heart rate,enhance and detect artifacts caused by movements of human body in BCG signal.展开更多
基金supported by the Chengdu Science and Technology Bureau(Grant No.2024-YF05-02565-SN)the National Natural Science Foundation of China(Grant No.82360266).
文摘Background Video electroencephalographies(VEEGs)are often affected by artifacts,which can diminish clinicians’efficiency in interpreting VEEG data and potentially result in diagnostic errors.Conversely,certain ictal VEEG artifacts caused by automatisms,such as rhythmic chewing and blinking,may offer significant diagnostic clues for temporal lobe epilepsy.To address the challenges mentioned above,this study aims to develop an algorithm capable of automatically identifying and classifying artifact types,assisting clinicians in distinguishing interference signals from seizure signals.Methods This paper proposes a Local-Global Feature Fusion Network based on Time-Domain and Time-Frequency Domain(LG-TDTFD-Net)for detecting and classifying temporal lobe epilepsy artifacts.The model processes time-domain and time-frequency signals separately,leveraging convolutional neural network(CNN)to extract local features from both domains.Additionally,convolution operations are performed on the features extracted by the Transformer to capture deep local features,resulting in a feature set that simultaneously encompasses global,deep,and shallow local information.Results Experimental results demonstrate that the proposed method excels in detecting and classifying temporal lobe epilepsy artifacts,outperforming existing baseline models on clinical and public datasets.Conclusions The method effectively exploits the complementarity among the three feature types by utilizing feature concatenation and CNN-based fusion,enhancing feature representation and improving model generalization.
基金the National Natural Science Foundation of China(No.61874171)the Alibaba Innovative Research Program of Alibaba Group。
文摘Removing different types of artifacts from the electroencephalography(EEG)recordings is a critical step in performing EEG signal analysis and diagnosis.Most of the existing algorithms aim for removing single type of artifacts,leading to a complex system if an EEG recording contains different types of artifacts.With the advancement in wearable technologies,it is necessary to develop an energy-efficient algorithm to deal with different types of artifacts for single-channel wearable EEG devices.In this paper,an automatic EEG artifact removal algorithm is proposed that effectively reduces three types of artifacts,i.e.,ocular artifact(OA),transmission-line/harmonic-wave artifact(TA/HA),and muscle artifact(MA),from a single-channel EEG recording.The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset.The experimental results show that the proposed algorithm effectively suppresses OA,MA and TA/HA from a single-channel EEG recording as well as physical movement artifact.
文摘Objective:Ballistocardiogram(BCG)is a kind of signal that reflects the movement of human body caused by the mechanical activity of cardiovascular system,especially during the heart contraction.Compared to other methods on assessing vascular healthy condition,the acquisition of BCG didn't need any direct contact with human body.This paper uses Hilbert-Huang transformation(HHT)to calculate the heart rate and detect the artifacts.Methods:HHT was a newly-developed method for non-linear data analysis,and ensemble empirical mode decomposition(EEMD)based HHT was a modified HHT method which used white noise to improve the analysis result.A device that could record BCG signal and ECG signal synchronously was built in our lab and 10 subjects'signals were collected and analyzed.EEMD based HHT was applied to BCG signal to calculate the heart rate.Heart rate calculated using ECG was used as a standard value to verify the result calculated from BCG.Besides,BCG was easily affected by the body movement,so we tried to use HHT to detect the artifacts in the BCG signal.Results:Our research showed that EEMD based HHT with a proper white noise level could be used to calculate the heart rate in BCG.Artifacts in the decomposition component were enhanced in the decomposition components of EEMD and became easier to detect than that in original BCG signal.Conclusion:Therefore,HHT could help to calculate the heart rate,enhance and detect artifacts caused by movements of human body in BCG signal.
