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Goshvarpour A, Goshvarpour A. Innovative Poincare's plot asymmetry descriptors for EEG emotion recognition. Cogn Neurodyn 2022; 16:545-559. [PMID: 35603058 PMCID: PMC9120274 DOI: 10.1007/s11571-021-09735-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 09/18/2021] [Accepted: 10/13/2021] [Indexed: 10/20/2022] Open
Abstract
Given the importance of emotion recognition in both medical and non-medical applications, designing an automatic system has captured the attention of several scholars. Currently, EEG-based emotion recognition has a special position, which has not fulfilled the desired accuracy rates yet. This experiment intended to provide novel EEG asymmetry measures to improve emotion recognition rates. Four emotional states have been classified using the k-nearest neighbor (kNN), support vector machine, and Naïve Bayes. Feature selection has been performed, and the role of employing a different number of top-ranked features on emotion recognition rates has been assessed. To validate the efficiency of the proposed scheme, two public databases, including the SJTU Emotion EEG Dataset-IV (SEED-IV) and a Database for Emotion Analysis using Physiological signals (DEAP) were evaluated. The experimental results indicated that kNN outperformed the other classifiers with a maximum accuracy of 95.49 and 98.63% using SEED-IV and DEAP datasets, respectively. In conclusion, the results of the proposed novel EEG-asymmetry measures make the framework a superior one compared to the state-of-art EEG emotion recognition approaches.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Rezvan Campus, Phalestine Sq., Mashhad, Razavi Khorasan Iran
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Faust O, Hong W, Loh HW, Xu S, Tan RS, Chakraborty S, Barua PD, Molinari F, Acharya UR. Heart rate variability for medical decision support systems: A review. Comput Biol Med 2022; 145:105407. [DOI: 10.1016/j.compbiomed.2022.105407] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/09/2022] [Accepted: 03/12/2022] [Indexed: 12/22/2022]
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Characterisation of neonatal cardiac dynamics using ordinal partition network. Med Biol Eng Comput 2022; 60:829-842. [PMID: 35119556 DOI: 10.1007/s11517-021-02481-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 11/24/2021] [Indexed: 10/19/2022]
Abstract
The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods. Few researchers have addressed this problem from the perspective nonlinear data analysis. This paper proposes a new data-driven methodology using nonlinear time series analysis, based on complex networks, to classify ANS conditions in newborns. We map 74 time series given by RR intervals from premature and full-term newborns to ordinal partition networks and use complexity quantifiers to discriminate the dynamical process present in both conditions. We obtain three complexity quantifiers (permutation, conditional, and global node entropies) using network mappings from forward and reverse directions, and considering different time lags and embedding dimensions. The results indicate that time asymmetry is present in the data of both groups and the complexity quantifiers can differentiate the groups analysed. We show that the conditional and global node entropies are sensitive for detecting subtle differences between the neonates, particularly for small embedding dimensions (m < 7). This study reinforces the assessment of nonlinear techniques for RR interval time series analysis. Graphical Abstract.
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Goshvarpour A, Goshvarpour A. Human Emotion Recognition using Polar-Based Lagged Poincare Plot Indices of Eye-Blinking Data. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2021. [DOI: 10.1142/s1469026821500231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Emotion recognition using bio-signals is currently a hot and challenging topic in human–computer interferences, robotics, and affective computing. A broad range of literature has been published by analyzing the internal/external behaviors of the subjects in confronting emotional events/stimuli. Eye movements, as an external behavior, are frequently used in the multi-modal emotion recognition system. On the other hand, classic statistical features of the signal have generally been assessed, and the evaluation of its dynamics has been neglected so far. For the first time, the dynamics of single-modal eye-blinking data are characterized. Novel polar-based indices of the lagged Poincare plots were introduced. The optimum lag was estimated using mutual information. After reconstruction of the plot, the polar measures of all points were characterized using statistical measures. The support vector machine (SVM), decision tree, and Naïve Bayes were implemented to complete the process of classification. The highest accuracy of 100% with an average accuracy of 84.17% was achieved for fear/sad discrimination using SVM. The suggested framework provided outstanding performances in terms of recognition rates, simplicity of the methodology, and less computational cost. Our results also showed that eye-blinking data possesses the potential for emotion recognition, especially in classifying fear emotion.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran
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Asymmetry of lagged Poincare plot in heart rate signals during meditation. J Tradit Complement Med 2021; 11:16-21. [PMID: 33511057 PMCID: PMC7817711 DOI: 10.1016/j.jtcme.2020.01.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2019] [Revised: 01/06/2020] [Accepted: 01/08/2020] [Indexed: 12/03/2022] Open
Abstract
Background and aim Heart rate variability (HRV) quantifies the variability in the heart’s beat-to-beat intervals. This signal is a potential marker of cardiac function in normal, pathological, and psychological states. Signal asymmetry refers to an unequal distribution in the signal, which can be found by a two-dimensional Poincare plot. Earlier, heart rate asymmetry (HRA) was assessed using a conventional Poincare plot (lag of 1). In this study, we have investigated the effect of delay on the phase space asymmetry using lagged Poincare’s plot. Experimental procedure This study compared the presence/lack of asymmetries in the HRV data of 12 meditators (four Kundalini yoga (Yoga) at an advanced level of meditation, eight Chinese Chi meditators (Chi) ∼1–3 months) to 25 non-meditators (11 spontaneous nocturnal breathing (Normal) and 14 metronomic breathing (Metron)). Poincare’s plots were constructed with six different lags, and HRA was calculated. The analysis was conducted using HRV data provided in the Physionet database. Results The results showed that using conventional Poincare’s plot (lag of 1), the lowest HRA was observed in the Metron group. In addition, the HRA index was different between meditators and non-meditator groups. Moreover, as the most significant difference between groups was observed in a delay of 6, the role of the delay selection on the signal asymmetry was revealed. Conclusion The difference between lagged HRA responses on Yoga in comparison with other groups can be an emphasis on the importance of choosing the type of meditation technique and its effects on the cardiovascular system. Asymmetries in HRV was assessed in different meditator and non-meditator groups. The role of delay selection was explored on the phase space asymmetry using lagged Poincare plot. A weaker asymmetry was observed in the metronomic breathing group. The most significant difference between groups was perceived in a delay of six.
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Sumi Y, Nakayama C, Kadotani H, Matsuo M, Ozeki Y, Kinoshita T, Goto Y, Kano M, Yamakawa T, Hasegawa-Ohira M, Ogawa K, Fujiwara K. Resting Heart Rate Variability Is Associated With Subsequent Orthostatic Hypotension: Comparison Between Healthy Older People and Patients With Rapid Eye Movement Sleep Behavior Disorder. Front Neurol 2020; 11:567984. [PMID: 33329309 PMCID: PMC7719719 DOI: 10.3389/fneur.2020.567984] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 10/12/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Orthostatic hypotension (OH) caused by autonomic dysfunction is a common symptom in older people and patients with idiopathic rapid eye movement sleep behavior disorder (iRBD). The orthostatic challenge test is a standard autonomic function test that measures a decrease of blood pressure during a postural change from supine to standing positions. Although previous studies have reported that changes in heart rate variability (HRV) are associated with autonomic dysfunction, no study has investigated the relationship between HRV before standing and the occurrence of OH in an orthostatic challenge test. This study aims to examine the connection between HRV in the supine position and the occurrence of OH in an orthostatic challenge test. Methods: We measured the electrocardiograms of patients with iRBD and healthy older people during an orthostatic challenge test, in which the supine and standing positions were held for 15 min, respectively. The subjects were divided into three groups: healthy controls (HC), OH-negative iRBD [OH (-) iRBD], and OH-positive iRBD [OH (+) iRBD]. HRV measured in the supine position during the test were calculated by time-domain analysis and Poincaré plots to evaluate the autonomic dysfunction. Results: Forty-two HC, 12 OH (-) iRBD, and nine OH (+) iRBD subjects were included. HRV indices in the OH (-) and the OH (+) iRBD groups were significantly smaller than those in the HC group. The multivariate logistic regression analysis for OH identification for the iRBD groups showed the model whose inputs were the HRV indices, i.e., standard deviation 2 (SD2) and the percentage of adjacent intervals that varied by more than 50 ms (pNN50), had a receiver operating characteristic curve with area under the curve of 0.840, the sensitivity to OH (+) of 1.000, and the specificity to OH (-) of 0.583 (p = 0.023). Conclusions: This study showed the possibility that short-term HRV indices in the supine position would predict subsequent OH in iRBD patients. Our results are of clinical importance in terms of showing the possibility that OH can be predicted using only HRV in the supine position without an orthostatic challenge test, which would improve the efficiency and safety of testing.
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Affiliation(s)
- Yukiyoshi Sumi
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan
| | - Chikao Nakayama
- Department of Systems Science, Kyoto University, Kyoto, Japan
| | - Hiroshi Kadotani
- Department of Sleep and Behavioral Sciences, Shiga University of Medical Science, Otsu, Japan
| | - Masahiro Matsuo
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan
| | - Yuji Ozeki
- Department of Psychiatry, Shiga University of Medical Science, Otsu, Japan
| | | | - Yuki Goto
- Department of Systems Science, Kyoto University, Kyoto, Japan
| | - Manabu Kano
- Department of Systems Science, Kyoto University, Kyoto, Japan
| | - Toshitaka Yamakawa
- Department of Priority Organization for Innovation and Excellence, Kumamoto University, Kumamoto, Japan
| | | | - Keiko Ogawa
- Graduate School of Humanities and Social Sciences, Hiroshima University, Higashihiroshima, Japan
| | - Koichi Fujiwara
- Department of Systems Science, Kyoto University, Kyoto, Japan.,Department of Materials Process Engineering, Nagoya University, Nagoya, Japan
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Ladeira G, Marwan N, Destro-Filho JB, Davi Ramos C, Lima G. Frequency spectrum recurrence analysis. Sci Rep 2020; 10:21241. [PMID: 33277526 PMCID: PMC7718872 DOI: 10.1038/s41598-020-77903-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 11/02/2020] [Indexed: 11/09/2022] Open
Abstract
In this paper, we present the new frequency spectrum recurrence analysis technique by means of electro-encephalon signals (EES) analyses. The technique is suitable for time series analysis with noise and disturbances. EES were collected, and alpha waves of the occipital region were analysed by comparing the signals from participants in two states, eyes open and eyes closed. Firstly, EES were characterized and analysed by means of techniques already known to compare with the results of the innovative technique that we present here. We verified that, standard recurrence quantification analysis by means of EES time series cannot statistically distinguish the two states. However, the new frequency spectrum recurrence quantification exhibit quantitatively whether the participants have their eyes open or closed. In sequence, new quantifiers are created for analysing the recurrence concentration on frequency bands. These analyses show that EES with similar frequency spectrum have different recurrence levels revealing different behaviours of the nervous system. The technique can be used to deepen the study on depression, stress, concentration level and other neurological issues and also can be used in any complex system.
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Affiliation(s)
- Guênia Ladeira
- Faculty of Mechanical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil.
| | - Norbert Marwan
- Potsdam Institute for Climate Impact Research, P.O. Box 601203, 14412, Potsdam, Germany.,Interdisciplinary Centre for Dynamics of Complex Systems, University of Potsdam, 14415, Potsdam, Germany
| | - João-Batista Destro-Filho
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Camila Davi Ramos
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
| | - Gabriela Lima
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia, Minas Gerais, Brazil
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Evaluation of Novel Entropy-Based Complex Wavelet Sub-bands Measures of PPG in an Emotion Recognition System. J Med Biol Eng 2020. [DOI: 10.1007/s40846-020-00526-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Goshvarpour A, Goshvarpour A. Schizophrenia diagnosis using innovative EEG feature-level fusion schemes. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2020; 43:10.1007/s13246-019-00839-1. [PMID: 31898243 DOI: 10.1007/s13246-019-00839-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 12/21/2019] [Indexed: 11/25/2022]
Abstract
Electroencephalogram (EEG) has become a practical tool for monitoring and diagnosing pathological/psychological brain states. To date, an increasing number of investigations considered differences between brain dynamic of patients with schizophrenia and healthy controls. However, insufficient studies have been performed to provide an intelligent and accurate system that detects the schizophrenia using EEG signals. This paper concerns this issue by providing new feature-level fusion algorithms. Firstly, we analyze EEG dynamics using three well-known nonlinear measures, including complexity (Cx), Higuchi fractal dimension (HFD), and Lyapunov exponents (Lya). Next, we propose some innovative feature-level fusion strategies to combine the information of these indices. We evaluate the effect of the classifier parameter (σ) adjustment and the cross-validation partitioning criteria on classification accuracy. The performance of EEG classification using combined features was compared with the non-combined attributes. Experimental results showed higher classification accuracy when feature-level features were utilized, compared to when each feature was used individually or all fed to the classifier simultaneously. Using the proposed algorithm, the classification accuracy increased up to 100%. These results establish the suggested framework as a superior scheme compared to the state-of-the-art EEG schizophrenia diagnosis tool.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, PO. BOX: 91735-553, Rezvan Campus (Female Students), Phalestine Sq., Mashhad, Razavi Khorasan, Iran.
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Goshvarpour A, Goshvarpour A. A Novel Approach for EEG Electrode Selection in Automated Emotion Recognition Based on Lagged Poincare’s Indices and sLORETA. Cognit Comput 2019. [DOI: 10.1007/s12559-019-09699-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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11
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Goshvarpour A, Goshvarpour A. The potential of photoplethysmogram and galvanic skin response in emotion recognition using nonlinear features. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2019; 43:10.1007/s13246-019-00825-7. [PMID: 31776972 DOI: 10.1007/s13246-019-00825-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 11/20/2019] [Indexed: 12/15/2022]
Abstract
Recently, developing an accurate automatic emotion recognition system using a minimum number of bio-signals has become a challenging issue in "affective computing." This study aimed to propose a reliable system by examining nonlinear dynamics of photoplethysmogram (PPG) and galvanic skin response (GSR). To address this goal, two strategies were adopted. First, the efficiency of each signal in valence/arousal based emotion categorization was examined. Then, the proficiency of a hybrid feature, by combining both GSR and PPG features was studied. Lyapunov exponents, lagged Poincare's measures, and approximate entropy were extracted to characterize the irregularity and chaotic behavior of the phase space. To discriminate two levels of arousal and two levels of the valence, a probabilistic neural network (PNN) with different sigma adjustment parameter was examined. The results showed that the phase space geometry and consequently, the signal dynamics are influenced by the emotional music video. Additionally, distinctive patterns of the phase space behavior were observed under the influence of different lags. For both signals, the most irregularity was observed during the high valence, and the least irregularity was seen during the low valence. Consequently, signals' irregularity is affected by the valence dimension. The results showed that the fusion has more potential for emotion recognition than that of using each signal separately. For sigma = 0.1, the highest recognition rate was 100% in a subject-dependent mode. In a subject-independent mode, the maximum accuracies of 88.57 and 86.8% were obtained for arousal and valence dimensions, respectively.
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Affiliation(s)
- Atefeh Goshvarpour
- Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, Tabriz, Iran
| | - Ateke Goshvarpour
- Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran.
- Imam Reza International University, Rezvan Campus (Female Students), Phalestine Sq., PO. BOX 91735-553, Mashhad, Razavi Khorasan, Iran.
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Valderas MT, Bolea J, Laguna P, Bailón R, Vallverdú M. Mutual information between heart rate variability and respiration for emotion characterization. Physiol Meas 2019; 40:084001. [DOI: 10.1088/1361-6579/ab310a] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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13
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Detection of Emotions Induced by Colors in Compare of Two Nonlinear Mapping of Heart Rate Variability Signal: Triangle and Parabolic Phase Space (TPSM, PPSM). J Med Biol Eng 2019. [DOI: 10.1007/s40846-018-0458-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Nardelli M, Greco A, Valenza G, Lanata A, Bailon R, Scilingo EP. A Multiclass Arousal Recognition using HRV Nonlinear Analysis and Affective Images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2018:392-395. [PMID: 30440417 DOI: 10.1109/embc.2018.8512426] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This paper reports on a multiclass arousal recognition system based on autonomic nervous system linear and nonlinear dynamics during affective visual elicitation. We propose a new hybrid method based on Lagged Poincaré Plot (LPP) and symbolic analysis, hereinafter called LPPsymb. This tool uses symbolic analysis to evaluate the irregularity of the trends of Lagged Poincaré Plot (LPP) quantifiers over the lags, and is here applied to investigate complex Heart Rate Variability (HRV) changes during emotion stimuli. In the experimental protocol 22 healthy subjects were elicited through a passive visualization of affective images gathered from the international affective picture system. LPPsymb and standard HRV analysis (defined in time and frequency domains) were applied to HRV series of one minute length. Then, an ad-hoc pattern recognition algorithm based on quadratic discriminant classifier was implemented and validated through a leave-onesubject-out procedure. The best performance of the proposed classification algorithm for recognizing the four classes of arousal was obtained using nine features comprising heartbeat complex dynamics, achieving an accuracy of 71.59%.
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Xia Y, Yang L, Zunino L, Shi H, Zhuang Y, Liu C. Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series. ENTROPY 2018; 20:e20030148. [PMID: 33265239 PMCID: PMC7512665 DOI: 10.3390/e20030148] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Revised: 02/14/2018] [Accepted: 02/15/2018] [Indexed: 11/18/2022]
Abstract
This study’s aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.
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Affiliation(s)
- Yirong Xia
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Licai Yang
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
- Correspondence: (L.Y.); (C.L.); Tel.: +86-135-83111153 (L.Y.); +86-159-52039150 (C.L.); Fax: +86-531-88392024 (L.Y.); +86-25-83793993 (C.L.)
| | - Luciano Zunino
- Centro de Investigaciones Ópticas (CONICET La Plata—CIC), C.C. 3, 1897 Gonnet, Argentina
- Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina
| | - Hongyu Shi
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Yuan Zhuang
- School of Control Science and Engineering, Shandong University, Jinan, 250061, China
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing 210018, China
- Correspondence: (L.Y.); (C.L.); Tel.: +86-135-83111153 (L.Y.); +86-159-52039150 (C.L.); Fax: +86-531-88392024 (L.Y.); +86-25-83793993 (C.L.)
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Moharreri S, Dabanloo NJ, Maghooli K. Modeling the 2D space of emotions based on the poincare plot of heart rate variability signal. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.07.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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17
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Goshvarpour A, Abbasi A, Goshvarpour A. Fusion of heart rate variability and pulse rate variability for emotion recognition using lagged poincare plots. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:617-629. [PMID: 28717902 DOI: 10.1007/s13246-017-0571-1] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 07/04/2017] [Indexed: 01/01/2023]
Abstract
Designing an efficient automatic emotion recognition system based on physiological signals has attracted great interests within the research of human-machine interactions. This study was aimed to classify emotional responses by means of a simple dynamic signal processing technique and fusion frameworks. The electrocardiogram and finger pulse activity of 35 participants were recorded during rest condition and when subjects were listening to music intended to stimulate certain emotions. Four emotion categories, including happiness, sadness, peacefulness, and fear were chosen. Estimating heart rate variability (HRV) and pulse rate variability (PRV), 4 Poincare indices in 10 lags were extracted. The support vector machine classifier was used for emotion classification. Both feature level (FL) and decision level (DL) fusion schemes were examined. Significant differences have been observed between lag 1 Poincare plot indices and the other lagged measures. The mean accuracies of 84.1, 82.9, 79.68, and 76.05% were obtained for PRV, DL, FL, and HRV measures, respectively. However, DL outperformed others in discriminating sadness and peacefulness, using SD1 and total features, correspondingly. In both cases, the classification rates improved up to 92% (with the sensitivity of 95% and specificity of 83.33%). Totally, DL resulted in better performances compared to FL. In addition, the impact of the fusion rules on the classification performances has been confirmed.
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Affiliation(s)
- Atefeh Goshvarpour
- Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, New Sahand Town, P. O. BOX 51335/1996, Tabriz, Iran
| | - Ataollah Abbasi
- Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, New Sahand Town, P. O. BOX 51335/1996, Tabriz, Iran.
| | - Ateke Goshvarpour
- Computational Neuroscience Laboratory, Department of Biomedical Engineering, Faculty of Electrical Engineering, Sahand University of Technology, New Sahand Town, P. O. BOX 51335/1996, Tabriz, Iran
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