Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

Pan, Shing‐Tai; Li, Wei‐Ching

doi:10.1002/asjc.2375

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…FuzzyEn uses the exponential function to fuzzify the similarity formula, which overcomes the defects of sample entropy being sensitive to data length and discontinuous boundary. Due to the monotonicity and continuity of the fuzzy exponential function, FuzzyEn changes smoothly and continuously with the change of parameters [29]. This allows for more detailed marginal information.…”

Section: Methodsmentioning

confidence: 99%

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

Chen

2023

Asian Journal of Control

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This paper proposed a hybrid feature extraction algorithm based on local mean decomposition (LMD), which has better solved the existing problems of low classification performance and adaptability limitation. LMD is employed to decompose the electroencephalogram (EEG) signal into multiple components, and then, the hybrid features based on instantaneous energy, fuzzy entropy, and mathematical morphological features are extracted on specific components, and the optimal feature combination is selected by analysis of variance (ANOVA). Finally, the classification result is output by the linear discriminant analysis (LDA) classifier. The results show that the maximum accuracy of the subjects in Data Set III of BCI‐II by the method in this paper is 92.14%, and the maximum mutual information value is 0.8. The number of novel features used in this paper is small, and the complexity of the algorithm is reduced. It can adaptively select effective features according to individual differences and has good robustness.

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Section: Methodsmentioning

confidence: 99%

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

Chen

2023

Asian Journal of Control

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“…The back-propagation algorithm with the steepest descent method (SDM) is used to train ANN by updating the weights based on the error function between the output and the goal to find the optimal parameters of ANN. The back-propagation algorithm is described in equations ( 16)- (21).…”

Section: Deep Neural Networkmentioning

confidence: 99%

Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network

Pan¹,

Chen

Hong

2022

BIJIAM

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This paper proposes a novel method for speech emotion recognition. Empirical mode decomposition (EMD) is applied in this paper for the extraction of emotional features from speeches, and a deep neural network (DNN) is used to classify speech emotions. This paper enhances the emotional components in speech signals by using EMD with acoustic feature Mel-Scale Frequency Cepstral Coefficients (MFCCs) to improve the recognition rates of emotions from speeches using the classifier DNN. In this paper, EMD is first used to decompose the speech signals, which contain emotional components into multiple intrinsic mode functions (IMFs), and then emotional features are derived from the IMFs and are calculated using MFCC. Then, the emotional features are used to train the DNN model. Finally, a trained model that could recognize the emotional signals is then used to identify emotions in speeches. Experimental results reveal that the proposed method is effective.

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“…In the case of continuation in receiving data points and the interest in finding hidden states by modeling observable sequential data, HMM is a capable model. HMM has been used in various medical applications [ 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ]. Moreover, it has been applied in human activity recognition [ 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 ].…”

Section: Introductionmentioning

confidence: 99%

Human Activity Recognition with an HMM-Based Generative Model

Manouchehri

Bouguila

2023

Sensors

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Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded–scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

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Fuzzy‐HMM modeling for emotion detection using electrocardiogram signals

Cited by 10 publications

References 24 publications

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

Emotion Recognition Based on Speech Signals by Combining Empirical Mode Decomposition and Deep Neural Network

Human Activity Recognition with an HMM-Based Generative Model

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