The Experts below are selected from a list of 53292 Experts worldwide ranked by ideXlab platform
Chengyu Liu - One of the best experts on this subject based on the ideXlab platform.
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Cardiorespiratory Coupling Analysis Based on Entropy and Cross-Entropy in Distinguishing Different Depression Stages
Frontiers Media S.A., 2019Co-Authors: Lulu Zhao, Licai Yang, Chengyu LiuAbstract:AimsThis study used Entropy- and cross Entropy-based methods to explore the cardiorespiratory coupling of depressive patients, and thus to assess the values of those Entropy methods for identifying depression patients with different disease severities.MethodsElectrocardiogram (ECG) and respiration signals from 69 depression patients were recorded simultaneously for 5 min. Patients were classified into three groups according to the Hamilton Depression Rating Scale (HDRS) scores: group Non-De (HDRS 0–7), Mid-De (HDRS 8–17), and Con-De (HDRS >17). Sample Entropy (SEn), fuzzy Measure Entropy (FMEn) and high-frequency power (HF) were computed on the original RR interval time series and breath-to-breath interval time series. Cross sample Entropy (CSEn) and cross fuzzy Measure Entropy (CFMEn) were computed on interval time series resampled at 2 Hz and 4 Hz, respectively. The difference among three patient groups and correlation between Entropy values and HDRS scores were analyzed by statistical analysis. Surrogate data were also employed to confirm the validation of Entropy Measures in this study.ResultsA consistent increasing trend has been found among most Entropy Measures from Non-De, to Mid-De, and to Con-De groups, and a significant (p < 0.05) difference in FMEn of RR intervals exists between Non-De and Mid-De or Con-De groups. Significant differences have been also found in all cross entropies, between Non-De and Con-De groups and between Mid-De and Con-De groups. Furthermore, significant correlations also exist between HDRS scores and FMEn of RR intervals (R = 0.24, p < 0.05), CSEn at 4 Hz (R = 0.26, p < 0.05) or 2 Hz (R = 0.28, p < 0.05) resampling, and CFMEn at 4 Hz (R = 0.31, p < 0.01) or 2 Hz (R = 0.30, p < 0.05) resampling. A significant difference of cardiorespiratory coupling parameters between different depression stages and significant correlations between Entropy Measures and depression severity both indicate central autonomic dysregulation in depression patients and reflect varying degrees of vagal modulation reduction among different depression levels. Analysis based on surrogate data confirms that the non-linear properties of the physiological signals played a major role in depression recognition.ConclusionThe current study demonstrates the potential of cardiorespiratory coupling in the auxiliary diagnosis of depression based on the Entropy method
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combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
IEEE Access, 2018Co-Authors: Yao Zhang, Chengyu Liu, Lina Zhao, Yang Zhang, Li Zhang, Liuxin Zhang, Binhua WangAbstract:Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in Entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three Entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample Entropy, fuzzy local Measure Entropy, and fuzzy global Measure Entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases ( http://www.physionet.org ). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.
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determination of sample Entropy and fuzzy Measure Entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects
Entropy, 2015Co-Authors: Lina Zhao, Feng Liu, Shoushui Wei, Chengqiu Zhang, Yatao Zhang, Xinge Jiang, Chengyu LiuAbstract:Entropy provides a valuable tool for quantifying the regularity of physiological time series and provides important insights for understanding the underlying mechanisms of the cardiovascular system. Before any Entropy calculation, certain common parameters need to be initialized: embedding dimension m, tolerance threshold r and time series length N. However, no specific guideline exists on how to determine the appropriate parameter values for distinguishing congestive heart failure (CHF) from normal sinus rhythm (NSR) subjects in clinical application. In the present study, a thorough analysis on the selection of appropriate values of m, r and N for sample Entropy (SampEn) and recently proposed fuzzy Measure Entropy (FuzzyMEn) is presented for distinguishing two group subjects. 44 long-term NRS and 29 long-term CHF RR interval recordings from http://www.physionet.org were used as the non-pathological and pathological data respectively. Extreme (>2 s) and abnormal heartbeat RR intervals were firstly removed from each RR recording and then the recording was segmented with a non-overlapping segment length N of 300 and 1000, respectively. SampEn and FuzzyMEn were performed for each RR segment under different parameter combinations: m of 1, 2, 3 and 4, and r of 0.10, 0.15, 0.20 and 0.25 respectively. The statistical significance between NSR and CHF groups under each combination of m, r and N was observed. The results demonstrated that the selection of m, r and N plays a critical role in determining the SampEn and FuzzyMEn outputs. Compared with SampEn, FuzzyMEn shows a better regularity when selecting the parameters m and r. In addition, FuzzyMEn shows a better relative consistency for distinguishing the two groups, that is, the results of FuzzyMEn in the NSR group were consistently lower than those in the CHF group while SampEn were not. The selections of m of 2 and 3 and r of 0.10 and 0.15 for SampEn and the selections of m of 1 and 2 whenever r (herein, rL = rG = r) are for FuzzyMEn (in addition to setting nL = 3 and nG = 2) were recommended to yield the fine classification results for the NSR and CHF groups.
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analysis of heart rate variability using fuzzy Measure Entropy
Computers in Biology and Medicine, 2013Co-Authors: Chengyu Liu, Lina Zhao, Feng Liu, Dingchang Zheng, Changchun Liu, Shutang LiuAbstract:This paper proposed a new Entropy Measure, Fuzzy Measure Entropy (FuzzyMEn), for the analysis of heart rate variability (HRV) signals. FuzzyMEn was calculated based on the fuzzy set theory and improved the poor statistical stability in the approximate Entropy (ApEn) and sample Entropy (SampEn). The simulation results also demonstrated that the FuzzyMEn had better algorithm discrimination ability when compared with the recently published fuzzy Entropy (FuzzyEn), The validity of FuzzyMEn was tested for clinical HRV analysis on 120 subjects (60 heart failure and 60 healthy control subjects). It is concluded that FuzzyMEn could be considered as a valid and reliable method for a clinical HRV application.
Ming Tang - One of the best experts on this subject based on the ideXlab platform.
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managing information Measures for hesitant fuzzy linguistic term sets and their applications in designing clustering algorithms
Information Fusion, 2019Co-Authors: Ming Tang, Huchang LiaoAbstract:Abstract Recently, the Hesitant Fuzzy Linguistic Term Sets (HFLTSs) have been widely used to address cognitive complex linguistic information because of its advantage in representing vagueness and hesitation in qualitative decision-making process. Information Measures, including distance Measure, similarity Measure, Entropy Measure, inclusion Measure and correlation Measure, are used to characterize the relationships between linguistic elements. Many decision-making theories are based on information Measures. Up to now, distance, similarity, Entropy and correlation Measures have been proposed by scholars but there is no paper focuses on inclusion Measure. This paper dedicates to filling this gap and the inclusion Measure between HFLTSs are proposed. We discuss the relationships among distance, similarity, inclusion and Entropy Measures of HFLTSs. Given that clustering algorithm is an important application of information Measures but there are few papers related to clustering algorithm based on information Measures in the environment of HFLTS, in this paper, we propose two clustering algorithms based on correlation Measure and distance Measure, respectively. After that, a case study concerning water resource bearing capacity is illustrated to verify the applicability of the proposed clustering algorithms.
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inclusion Measures of probabilistic linguistic term sets and their application in classifying cities in the economic zone of chengdu plain
Applied Soft Computing, 2019Co-Authors: Ming Tang, Yilu Long, Huchang Liao, Zeshui XuAbstract:Abstract The probabilistic linguistic term set is a powerful tool to express and characterize people’s cognitive complex information and thus has obtained a great development in the last several years. To better use the probabilistic linguistic term sets in decision making, information Measures such as the distance Measure, similarity Measure, Entropy Measure and correlation Measure should be defined. However, as an important kind of information Measure, the inclusion Measure has not been defined by scholars. This study aims to propose the inclusion Measure for probabilistic linguistic term sets. Formulas to calculate the inclusion degrees are put forward Then, we introduce the normalized axiomatic definitions of the distance, similarity and Entropy Measures of probabilistic linguistic term sets to construct a unified framework of information Measures for probabilistic linguistic term sets. Based on these definitions, we present the relationships and transformation functions among the distance, similarity, Entropy and inclusion Measures. We believe that more formulas to calculate the distance, similarity, inclusion degree and Entropy can be induced based on these transformation functions. Finally, we put forward an orthogonal clustering algorithm based on the inclusion Measure and use it in classifying cities in the Economic Zone of Chengdu Plain, China.
Binhua Wang - One of the best experts on this subject based on the ideXlab platform.
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combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
IEEE Access, 2018Co-Authors: Yao Zhang, Chengyu Liu, Lina Zhao, Yang Zhang, Li Zhang, Liuxin Zhang, Binhua WangAbstract:Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in Entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three Entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample Entropy, fuzzy local Measure Entropy, and fuzzy global Measure Entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases ( http://www.physionet.org ). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.
Lina Zhao - One of the best experts on this subject based on the ideXlab platform.
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combining convolutional neural network and distance distribution matrix for identification of congestive heart failure
IEEE Access, 2018Co-Authors: Yao Zhang, Chengyu Liu, Lina Zhao, Yang Zhang, Li Zhang, Liuxin Zhang, Binhua WangAbstract:Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in Entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three Entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample Entropy, fuzzy local Measure Entropy, and fuzzy global Measure Entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases ( http://www.physionet.org ). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.
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determination of sample Entropy and fuzzy Measure Entropy parameters for distinguishing congestive heart failure from normal sinus rhythm subjects
Entropy, 2015Co-Authors: Lina Zhao, Feng Liu, Shoushui Wei, Chengqiu Zhang, Yatao Zhang, Xinge Jiang, Chengyu LiuAbstract:Entropy provides a valuable tool for quantifying the regularity of physiological time series and provides important insights for understanding the underlying mechanisms of the cardiovascular system. Before any Entropy calculation, certain common parameters need to be initialized: embedding dimension m, tolerance threshold r and time series length N. However, no specific guideline exists on how to determine the appropriate parameter values for distinguishing congestive heart failure (CHF) from normal sinus rhythm (NSR) subjects in clinical application. In the present study, a thorough analysis on the selection of appropriate values of m, r and N for sample Entropy (SampEn) and recently proposed fuzzy Measure Entropy (FuzzyMEn) is presented for distinguishing two group subjects. 44 long-term NRS and 29 long-term CHF RR interval recordings from http://www.physionet.org were used as the non-pathological and pathological data respectively. Extreme (>2 s) and abnormal heartbeat RR intervals were firstly removed from each RR recording and then the recording was segmented with a non-overlapping segment length N of 300 and 1000, respectively. SampEn and FuzzyMEn were performed for each RR segment under different parameter combinations: m of 1, 2, 3 and 4, and r of 0.10, 0.15, 0.20 and 0.25 respectively. The statistical significance between NSR and CHF groups under each combination of m, r and N was observed. The results demonstrated that the selection of m, r and N plays a critical role in determining the SampEn and FuzzyMEn outputs. Compared with SampEn, FuzzyMEn shows a better regularity when selecting the parameters m and r. In addition, FuzzyMEn shows a better relative consistency for distinguishing the two groups, that is, the results of FuzzyMEn in the NSR group were consistently lower than those in the CHF group while SampEn were not. The selections of m of 2 and 3 and r of 0.10 and 0.15 for SampEn and the selections of m of 1 and 2 whenever r (herein, rL = rG = r) are for FuzzyMEn (in addition to setting nL = 3 and nG = 2) were recommended to yield the fine classification results for the NSR and CHF groups.
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analysis of heart rate variability using fuzzy Measure Entropy
Computers in Biology and Medicine, 2013Co-Authors: Chengyu Liu, Lina Zhao, Feng Liu, Dingchang Zheng, Changchun Liu, Shutang LiuAbstract:This paper proposed a new Entropy Measure, Fuzzy Measure Entropy (FuzzyMEn), for the analysis of heart rate variability (HRV) signals. FuzzyMEn was calculated based on the fuzzy set theory and improved the poor statistical stability in the approximate Entropy (ApEn) and sample Entropy (SampEn). The simulation results also demonstrated that the FuzzyMEn had better algorithm discrimination ability when compared with the recently published fuzzy Entropy (FuzzyEn), The validity of FuzzyMEn was tested for clinical HRV analysis on 120 subjects (60 heart failure and 60 healthy control subjects). It is concluded that FuzzyMEn could be considered as a valid and reliable method for a clinical HRV application.
Zeshui Xu - One of the best experts on this subject based on the ideXlab platform.
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inclusion Measures of probabilistic linguistic term sets and their application in classifying cities in the economic zone of chengdu plain
Applied Soft Computing, 2019Co-Authors: Ming Tang, Yilu Long, Huchang Liao, Zeshui XuAbstract:Abstract The probabilistic linguistic term set is a powerful tool to express and characterize people’s cognitive complex information and thus has obtained a great development in the last several years. To better use the probabilistic linguistic term sets in decision making, information Measures such as the distance Measure, similarity Measure, Entropy Measure and correlation Measure should be defined. However, as an important kind of information Measure, the inclusion Measure has not been defined by scholars. This study aims to propose the inclusion Measure for probabilistic linguistic term sets. Formulas to calculate the inclusion degrees are put forward Then, we introduce the normalized axiomatic definitions of the distance, similarity and Entropy Measures of probabilistic linguistic term sets to construct a unified framework of information Measures for probabilistic linguistic term sets. Based on these definitions, we present the relationships and transformation functions among the distance, similarity, Entropy and inclusion Measures. We believe that more formulas to calculate the distance, similarity, inclusion degree and Entropy can be induced based on these transformation functions. Finally, we put forward an orthogonal clustering algorithm based on the inclusion Measure and use it in classifying cities in the Economic Zone of Chengdu Plain, China.