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学者姓名：郝冬梅
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Abstract ：
Resting state electroencephalography (EEG) has been shown to provide relevant information for detecting neuropathological changes of the brain's electrical activity in neurodegenerative patients. Studies conducted on local field potential recordings have shown that exaggerated beta oscillations and abnormally high beta-gamma phase amplitude coupling (PAC) are hallmark Parkinson's disease (PD) signatures. Extracting beta bursts from non-invasive magnetoencephalography has also been found to be feasible, as it provides a better signal-to-noise ratio than electroencephalography and is less affected by volume conduction. It is still unclear whether beta burst dynamic features and beta-gamma PAC from resting state EEG can be used to assess the progress of PD. In the present study, it has been proposed to assess the potential utility of beta burst dynamic and the beta-gamma PAC to discriminate PD patients from healthy subjects, as well as their relationship with clinical symptoms. Resting state EEG data have been analysed in both eyes closed (EC) and open (EO) and reactivity-to-eyes opening (REO) of a public database consisting of 20 healthy and 13 Parkinson patients. Beta burst events from EEG spectrograms were extracted to determine their dynamic features, i.e. burst duration, rate, peak frequency, spectral bandwidth and power as well as the normalized beta-gamma PAC. Permutation test while controlling the family-wise error rate was used to assess statistical significance. The results indicate that REO is more sensitive than EC and EO alone, and also that the higher variability of burst duration is linked to PD, while the lower burst rate is negatively correlated with clinical symptoms. PD patients had a higher periodicity of duration in the left frontal area, and a higher periodicity of peak frequency, spectral bandwidth and power of the bursts in the left central area than healthy subjects, together with a significant positive correlation with clinical symptoms. Beta-gamma PAC not only found abnormalities in the central regions but also in the frontal, fronto-central, parietal and occipital regions, suggesting impaired motor, working memory and visuospatial skills. It was also possible to extract beta burst dynamic features and the beta-gamma PAC from resting state EEG and that these provided reliable PD progress biomarkers. These advances are expected to help clinicians design patientpersonalised therapies and improve their quality of life.
Keyword ：
Phase amplitude coupling Phase amplitude coupling Dynamic features Dynamic features Eyes influence Eyes influence Beta burst Beta burst Resting state electroencephalography Resting state electroencephalography Parkinson Parkinson
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| GB/T 7714 | Gimenez-Aparisi, G. , Guijarro-Estelles, E. , Chornet-Lurbe, A. et al. Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 107 . |
| MLA | Gimenez-Aparisi, G. et al. "Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 107 (2025) . |
| APA | Gimenez-Aparisi, G. , Guijarro-Estelles, E. , Chornet-Lurbe, A. , Diaz-Roman, M. , Hao, Dongmei , Li, Guangfei et al. Early detection of Parkinson's disease based on beta dynamic features and beta-gamma coupling from non-invasive resting state EEG: Influence of the eyes . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2025 , 107 . |
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Abstract ：
Background and objective: Preterm labor is a global problem affecting the health of newborns. Despite numerous studies reporting electrophysiological changes throughout pregnancy, the underlying mechanism that triggers labor remains unclear. Electrophysiological modeling can provide additional information to better understand the physiological transition from pregnancy to labor. Previous uterine electrophysiological models do not consider either the tissue thickness or fiber structure, which have both been shown to significantly impact propagation patterns. Methods: This paper presents a parallel computational model of the uterus using the bioengineering modeling environment OpenCMISS. This model is a multiscale anisotropic model that spans different levels from cell to organ. At the cellular level, the model utilizes a mathematical representation of uterine myocytes based on multiple ion channels. In the 3D uterine model, fiber structures are added, ranging from horizontal rings in the inner layer to vertically downward fibers in the outer layer, to more accurately depict the electrophysiological activities of the uterus. Additionally, we have developed a multilayer volume conduction model based on the boundary element method to describe the propagation of electrical signals from the myometrium to the abdominal surface. Results: Our model can not only reproduce faithfully both local non-propagated and global propagated electrical activity, but also simulate the fast wave low and fast wave high components of the electrohysterogram (EHG) on the abdominal surface. The model results support the hypothesis that the fast wave high of the EHG signal is related to uterine excitability and fast wave low is related to signal propagation. The amplitude of the simulated signal on the abdominal surface falls in the ranges of real EHG data, which is inversely proportional to the abdominal subcutaneous fat thickness, and the signal waveform highly depends on electrode position and the relative distance to the pacemaker. In addition, the propagation velocity is highly dependent on the uterus geometry and falls in the real-world data range Conclusions: Our models facilitate a better understanding of the electrophysiological changes of the uterus during pregnancy and labor, and allow for an investigation of drug effects and/or structural or anatomical abnormalities.
Keyword ：
Isotropic/anisotropic model Isotropic/anisotropic model Fiber structure Fiber structure Electrophysiological simulation Electrophysiological simulation Uterus model Uterus model Volume conductor model Volume conductor model
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| GB/T 7714 | Yang, Yongxiu , Bradley, Chris , Li, Guangfei et al. A computationally efficient anisotropic electrophysiological multiscale uterus model: From cell to organ and myometrium to abdominal surface [J]. | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2024 , 257 . |
| MLA | Yang, Yongxiu et al. "A computationally efficient anisotropic electrophysiological multiscale uterus model: From cell to organ and myometrium to abdominal surface" . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 257 (2024) . |
| APA | Yang, Yongxiu , Bradley, Chris , Li, Guangfei , Monfort-Ortiz, Rogelio , Nieto-del-Amor, Felix , Hao, Dongmei et al. A computationally efficient anisotropic electrophysiological multiscale uterus model: From cell to organ and myometrium to abdominal surface . | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE , 2024 , 257 . |
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Background: Both alcohol misuse and sleep deficiency are associated with deficits in semantic processing. However, alcohol misuse and sleep deficiency are frequently comorbid and their inter-related effects on semantic processing as well as the underlying neural mechanisms remain to be investigated. Methods: We curated the Human Connectome Project data of 973 young adults (508 women) to examine the neural correlates of semantic processing in link with the severity of alcohol use and sleep deficiency. The latter were each evaluated using the first principal component (PC1) of principal component analysis of all drinking metrics and the Pittsburgh Sleep Quality Index (PSQI). We employed path modeling to elucidate the interplay among clinical, behavioral, and neural variables. Results: Among women, we observed a significant negative correlation between the left precentral gyrus (PCG) and PSQI scores. Mediation analysis revealed that the left PCG activity fully mediated the relationship between PSQI scores and word comprehension in language tasks. In women alone also, the right middle frontal gyrus (MFG) exhibited a significant negative correlation with PC1. The best path model illustrated the associations among PC1, PSQI scores, PCG activity, and MFG activation during semantic processing in women. Conclusions: Alcohol misuse may lead to reduced MFG activation while sleep deficiency hinder semantic processing by suppressing PCG activity in women. The pathway model underscores the influence of sleep quality and alcohol consumption severity on semantic processing in women, suggesting that sex differences in these effects need to be further investigated.
Keyword ：
Sleep deficiency Sleep deficiency Semantic processing Semantic processing Alcohol use severity Alcohol use severity fMRI fMRI
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| GB/T 7714 | Li, Guangfei , Zhong, Dandan , Zhang, Ning et al. The inter-related effects of alcohol use severity and sleep deficiency on semantic processing in young adults [J]. | NEUROSCIENCE , 2024 , 555 : 116-124 . |
| MLA | Li, Guangfei et al. "The inter-related effects of alcohol use severity and sleep deficiency on semantic processing in young adults" . | NEUROSCIENCE 555 (2024) : 116-124 . |
| APA | Li, Guangfei , Zhong, Dandan , Zhang, Ning , Dong, Jianyu , Yan, Yan , Xu, Qixiao et al. The inter-related effects of alcohol use severity and sleep deficiency on semantic processing in young adults . | NEUROSCIENCE , 2024 , 555 , 116-124 . |
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Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.
Keyword ：
video video optical flow optical flow non-contact non-contact fetal movement fetal movement
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| GB/T 7714 | Han, Qiao , Hao, Dongmei , Yang, Lin et al. Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording [J]. | SENSORS , 2023 , 23 (10) . |
| MLA | Han, Qiao et al. "Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording" . | SENSORS 23 . 10 (2023) . |
| APA | Han, Qiao , Hao, Dongmei , Yang, Lin , Yang, Yimin , Li, Guangfei . Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording . | SENSORS , 2023 , 23 (10) . |
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Abstract ：
This study evaluates the progression of visual fatigue induced by visual display terminal (VDT) using automatically detected blink features. A total of 23 subjects were recruited to participate in a VDT task, during which they were required to watch a 120-min video on a laptop and answer a questionnaire every 30 min. Face video recordings were captured by a camera. The blinking and incomplete blinking images were recognized by automatic detection of the parameters of the eyes. Then, the blink features were extracted including blink number (BN), mean blink interval (Mean_BI), mean blink duration (Mean_BD), group blink number (GBN), mean group blink interval (Mean_GBI), incomplete blink number (IBN), and mean incomplete blink interval (Mean_IBI). The results showed that BN and GBN increased significantly, and that Mean_BI and Mean_GBI decreased significantly over time. Mean_BD and Mean_IBI increased and IBN decreased significantly only in the last 30 min. The blink features automatically detected in this study can be used to evaluate the progression of visual fatigue.
Keyword ：
visual fatigue visual fatigue incomplete blink incomplete blink blink feature blink feature visual display terminal visual display terminal
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| GB/T 7714 | Yin, Zhijie , Liu, Bing , Hao, Dongmei et al. Evaluation of VDT-Induced Visual Fatigue by Automatic Detection of Blink Features [J]. | SENSORS , 2022 , 22 (3) . |
| MLA | Yin, Zhijie et al. "Evaluation of VDT-Induced Visual Fatigue by Automatic Detection of Blink Features" . | SENSORS 22 . 3 (2022) . |
| APA | Yin, Zhijie , Liu, Bing , Hao, Dongmei , Yang, Lin , Feng, Yongkang . Evaluation of VDT-Induced Visual Fatigue by Automatic Detection of Blink Features . | SENSORS , 2022 , 22 (3) . |
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Abstract ：
BACKGROUND: As an essential indicator of labour and delivery, uterine contraction (UC) can be detected by manual palpation, external tocodynamometry and internal uterine pressure catheter. However, these methods are not applicable for long-term monitoring. OBJECTIVE: This paper aims to recognize UCs with electrohysterogram (EHG) and find the best electrode combination with fewer electrodes. METHODS: 112 EHG recordings were collected by our bespoke device in our study. Thirteen features were extracted from EHG segments of UC and non-UC. Four classifiers of the decision tree, support vector machine (SVM), artificial neural network, and convolutional neural network were established to identify UCs. The optimal classifier among them was determined by comparing their classification results. The optimal classifier was applied to evaluate all the possible electrode combinations with one to eight electrodes. RESULTS: The results showed that SVM achieved the best classification capability. With SVM, the combination of electrodes on the right part of the uterine fundus and around the uterine body's median axis achieved the overall best performance. CONCLUSIONS: The optimal electrode combination with fewer electrodes was confirmed to improve the clinical application for long-term monitoring of UCs.
Keyword ：
classifier classifier Electrode combination Electrode combination uterine contraction uterine contraction electrohysterogram (EHG) electrohysterogram (EHG)
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| GB/T 7714 | Du, Mengqing , Qiu, Qian , Hao, Dongmei et al. Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination [J]. | TECHNOLOGY AND HEALTH CARE , 2022 , 30 : S235-S242 . |
| MLA | Du, Mengqing et al. "Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination" . | TECHNOLOGY AND HEALTH CARE 30 (2022) : S235-S242 . |
| APA | Du, Mengqing , Qiu, Qian , Hao, Dongmei , Zhou, Xiya , Yang, Lin , Liu, Xiaohong . Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination . | TECHNOLOGY AND HEALTH CARE , 2022 , 30 , S235-S242 . |
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Abstract ：
The relatively limited understanding of the physiology of uterine activation prevents us from achieving optimal clinical outcomes for managing serious pregnancy disorders such as preterm birth or uterine dystocia. There is increasing awareness that multi-scale computational modeling of the uterus is a promising approach for providing a qualitative and quantitative description of uterine physiology. The overarching objective of such approach is to coalesce previously fragmentary information into a predictive and testable model of uterine activity that, in turn, informs the development of new diagnostic and therapeutic approaches to these pressing clinical problems. This article assesses current progress towards this goal. We summarize the electrophysiological basis of uterine activation as presently understood and review recent research approaches to uterine modeling at different scales from single cell to tissue, whole organ and organism with particular focus on transformative data in the last decade. We describe the positives and limitations of these approaches, thereby identifying key gaps in our knowledge on which to focus, in parallel, future computational and biological research efforts.
Keyword ：
Surface reconstruction Surface reconstruction uterine activity uterine activity uterine physiology uterine physiology Biological system modeling Biological system modeling computational modeling computational modeling Data models Data models Pregnancy Pregnancy Uterus Uterus Computational modeling Computational modeling Physiology Physiology Biological systems Biological systems
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| GB/T 7714 | Xu, Yuhang , Liu, Haipeng , Hao, Dongmei et al. Uterus Modeling From Cell to Organ Level: Towards Better Understanding of Physiological Basis of Uterine Activity [J]. | IEEE REVIEWS IN BIOMEDICAL ENGINEERING , 2022 , 15 : 341-353 . |
| MLA | Xu, Yuhang et al. "Uterus Modeling From Cell to Organ Level: Towards Better Understanding of Physiological Basis of Uterine Activity" . | IEEE REVIEWS IN BIOMEDICAL ENGINEERING 15 (2022) : 341-353 . |
| APA | Xu, Yuhang , Liu, Haipeng , Hao, Dongmei , Taggart, Michael , Zheng, Dingchang . Uterus Modeling From Cell to Organ Level: Towards Better Understanding of Physiological Basis of Uterine Activity . | IEEE REVIEWS IN BIOMEDICAL ENGINEERING , 2022 , 15 , 341-353 . |
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Abstract ：
Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as deliveries within and beyond 24 h. A total of 219 pregnant women were grouped in two ways: (1) term delivery (TD), threatened preterm labor (TPL) with the outcome of preterm birth (TPL_PB), and TPL with the outcome of term delivery (TPL_TD); (2) EHG recording time to delivery (TTD) <= 24 h and TTD > 24 h. Three bipolar EHG signals were analyzed for the 30 min recording. Six EHG features between multiple channels, including multivariate sample entropy, mutual information, correlation coefficient, coherence, direct partial Granger causality, and direct transfer entropy, were extracted to characterize the coupling and information flow between channels. Significant differences were found for these six features between TPL and TD, and between TTD <= 24 h and TTD > 24 h. No significant difference was found between TPL_PB and TPL_TD. The results indicated that EHG signals of TD were more regular and synchronized than TPL, and stronger coupling between multichannel EHG signals was exhibited as delivery approaches. In addition, EHG signals propagate downward for the majority of pregnant women regardless of different labors. In conclusion, the coupling and propagation features extracted from multichannel EHG signals could be used to differentiate term delivery and preterm birth and may predict delivery within and beyond 24 h.
Keyword ：
features features term delivery term delivery electrohysterogram electrohysterogram time to delivery time to delivery preterm birth preterm birth
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| GB/T 7714 | Zhang, Yajun , Hao, Dongmei , Yang, Lin et al. Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors [J]. | SENSORS , 2022 , 22 (9) . |
| MLA | Zhang, Yajun et al. "Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors" . | SENSORS 22 . 9 (2022) . |
| APA | Zhang, Yajun , Hao, Dongmei , Yang, Lin , Zhou, Xiya , Ye-Lin, Yiyao , Yang, Yimin . Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors . | SENSORS , 2022 , 22 (9) . |
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Abstract ：
Due to its high sensitivity, electrohysterography (EHG) has emerged as an alternative technique for predicting preterm labor. The main obstacle in designing preterm labor prediction models is the inherent preterm/term imbalance ratio, which can give rise to relatively low performance. Numerous studies obtained promising preterm labor prediction results using the synthetic minority oversampling technique. However, these studies generally overestimate mathematical models' real generalization capacity by generating synthetic data before splitting the dataset, leaking information between the training and testing partitions and thus reducing the complexity of the classification task. In this work, we analyzed the effect of combining feature selection and resampling methods to overcome the class imbalance problem for predicting preterm labor by EHG. We assessed undersampling, oversampling, and hybrid methods applied to the training and validation dataset during feature selection by genetic algorithm, and analyzed the resampling effect on training data after obtaining the optimized feature subset. The best strategy consisted of undersampling the majority class of the validation dataset to 1:1 during feature selection, without subsequent resampling of the training data, achieving an AUC of 94.5 +/- 4.6%, average precision of 84.5 +/- 11.7%, maximum F1-score of 79.6 +/- 13.8%, and recall of 89.8 +/- 12.1%. Our results outperformed the techniques currently used in clinical practice, suggesting the EHG could be used to predict preterm labor in clinics.
Keyword ：
uterine electromyography uterine electromyography machine learning machine learning electrohysterography electrohysterography imbalance data learning imbalance data learning preterm labor prediction preterm labor prediction resampling methods resampling methods genetic algorithm genetic algorithm
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| GB/T 7714 | Nieto-del-Amor, Felix , Prats-Boluda, Gema , Garcia-Casado, Javier et al. Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data [J]. | SENSORS , 2022 , 22 (14) . |
| MLA | Nieto-del-Amor, Felix et al. "Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data" . | SENSORS 22 . 14 (2022) . |
| APA | Nieto-del-Amor, Felix , Prats-Boluda, Gema , Garcia-Casado, Javier , Diaz-Martinez, Alba , Jose Diago-Almela, Vicente , Monfort-Ortiz, Rogelio et al. Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data . | SENSORS , 2022 , 22 (14) . |
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Abstract ：
One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 +/- 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.
Keyword ：
uterine electrical activity uterine electrical activity electrohysterography electrohysterography preterm birth prediction preterm birth prediction sample entropy sample entropy uterine electromyogram uterine electromyogram fuzzy entropy fuzzy entropy feature selection feature selection genetic algorithm genetic algorithm bubble entropy bubble entropy dispersion entropy dispersion entropy
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| GB/T 7714 | Nieto-del-Amor, Felix , Beskhani, Raja , Ye-Lin, Yiyao et al. Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals [J]. | SENSORS , 2021 , 21 (18) . |
| MLA | Nieto-del-Amor, Felix et al. "Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals" . | SENSORS 21 . 18 (2021) . |
| APA | Nieto-del-Amor, Felix , Beskhani, Raja , Ye-Lin, Yiyao , Garcia-Casado, Javier , Diaz-Martinez, Alba , Monfort-Ortiz, Rogelio et al. Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals . | SENSORS , 2021 , 21 (18) . |
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