Dataset Information

Heart sound signals can be used for emotion recognition.

ABSTRACT: This article studies whether heart sound signals can be used for emotion recognition. First, we built a small emotion heart sound database, and simultaneously recorded the participants' ECG for comparative analysis. Second, according to the characteristics of the heart sound signals, two emotion evaluation indicators were proposed: HRV of heart sounds (difference between successive heartbeats) and DSV of heart sounds (the ratio of diastolic to systolic duration variability). Then, we extracted linear and nonlinear features from two emotion evaluation indicators to recognize four kinds of emotions. Moreover, we used valence dimension, arousal dimension and valence-arousal synthesis as evaluation standards. The experimental results demonstrated that heart sound signals can be used for emotion recognition. It was more effective to achieve recognition results by combining the features of HRV and DSV of heart sounds. Finally, the average accuracy of four emotion recognitions on valence dimension, arousal dimension and valence-arousal synthesis was up to 96.875%, 88.5417% and 81.25%, respectively.

SUBMITTER: Xiefeng C

PROVIDER: S-EPMC6482302 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Heart sound signals can be used for emotion recognition.

Xiefeng Cheng C Wang Yue Y Dai Shicheng S Zhao Pengjun P Liu Qifa Q

Scientific reports 20190424 1

This article studies whether heart sound signals can be used for emotion recognition. First, we built a small emotion heart sound database, and simultaneously recorded the participants' ECG for comparative analysis. Second, according to the characteristics of the heart sound signals, two emotion evaluation indicators were proposed: HRV of heart sounds (difference between successive heartbeats) and DSV of heart sounds (the ratio of diastolic to systolic duration variability). Then, we extracted l ...[more]

PMID: 31019217

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Project description:Electroencephalography (EEG) is one of the most widely-used biosignal capturing technology for investigating brain activities, cognitive diseases, and affective disorders. To understand the underlying principles of brain activities and affective disorders using EEG data, one of the fundamental tasks is to accurately identify emotions from EEG signals, which has attracted huge attention in the field of affective computing. To improve the accuracy and effectiveness of emotion recognition based on EEG data, previous studies have successfully developed numerous feature extraction methods and classifiers. Among them, ensemble empirical mode decomposition (EEMD) is an efficient signal decomposition technique for extracting EEG features. It can alleviate the mode-mixing problem by adding white noise to the source signal. However, there remain some issues when applying this method to recognition tasks. As the added noise cannot be filtered completely, spurious modes are generated due to the residual noise. Therefore, it is crucial to perform intrinsic mode function (IMF) selection to find the most valuable IMF components that represent brain activities. Furthermore, the number of decomposed IMFs is various to different original signals, thus how to unify feature dimensions needs better solutions. To solve these issues, we propose a novel forecasting framework, named DEEMD-SPP, to identify emotions from EEG signals, based on the combination of denoising ensemble empirical mode decomposition (DEEMD) and Spatial Pyramid Pooling Network (SPP-Net). First, DEEMD is proposed to decompose the EEG signals, which effectively eliminates residual noise in the IMFs and selects the most valuable IMFs. Second, time-domain and frequency-domain features are extracted from the selected IMFs. Finally, SPP-net is employed as the classifier to recognize emotions, which can effectively transform various-sized feature maps into fixed-sized feature vectors through the pyramid pooling layer. The experimental results demonstrate that our proposed DEEMD-SPP framework can effectively reduce the effect of spike-in white noise, accurately extract EEG features, and significantly improve the performance of emotion recognition.

Project description:Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Microstate analysis could capture important spatio-temporal properties of EEG signals. At the same time, it could reduce the fast-changing EEG signals to a sequence of prototypical topographical maps. While microstate analysis has been widely used to study brain function, few studies have used this method to analyze how brain responds to emotional auditory stimuli. In this study, the authors proposed a novel feature extraction method based on EEG microstates for emotion recognition. Determining the optimal number of microstates automatically is a challenge for applying microstate analysis to emotion. This research proposed dual-threshold-based atomize and agglomerate hierarchical clustering (DTAAHC) to determine the optimal number of microstate classes automatically. By using the proposed method to model the temporal dynamics of auditory emotion process, we extracted microstate characteristics as novel temporospatial features to improve the performance of emotion recognition from EEG signals. We evaluated the proposed method on two datasets. For public music-evoked EEG Dataset for Emotion Analysis using Physiological signals, the microstate analysis identified 10 microstates which together explained around 86% of the data in global field power peaks. The accuracy of emotion recognition achieved 75.8% in valence and 77.1% in arousal using microstate sequence characteristics as features. Compared to previous studies, the proposed method outperformed the current feature sets. For the speech-evoked EEG dataset, the microstate analysis identified nine microstates which together explained around 85% of the data. The accuracy of emotion recognition achieved 74.2% in valence and 72.3% in arousal using microstate sequence characteristics as features. The experimental results indicated that microstate characteristics can effectively improve the performance of emotion recognition from EEG signals.

Dataset Information

Heart sound signals can be used for emotion recognition.

Publications

Heart sound signals can be used for emotion recognition.

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