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Abe T, Asai Y, Lintas A, Villa AEP. Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions. Sci Rep 2024; 14:8521. [PMID: 38609457 DOI: 10.1038/s41598-024-59004-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.
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Affiliation(s)
- Takeshi Abe
- AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Division of Systems Medicine and Informatics, Research Institute of Cell Design Medical Science, Yamaguchi University, Yamaguchi, 755-8505, Japan
| | - Yoshiyuki Asai
- AI Systems Medicine Research and Training Center, Graduate School of Medicine and University Hospital, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Department of Systems Bioinformatics, Graduate School of Medicine, Yamaguchi University, Yamaguchi, 755-8505, Japan
- Division of Systems Medicine and Informatics, Research Institute of Cell Design Medical Science, Yamaguchi University, Yamaguchi, 755-8505, Japan
| | - Alessandra Lintas
- HEC-LABEX, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland
- Neuroheuristic Research Group & Complexity Sciences Research Group, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland
| | - Alessandro E P Villa
- Neuroheuristic Research Group & Complexity Sciences Research Group, University of Lausanne, Quartier UNIL-Chamberonne, 1015, Lausanne, Switzerland.
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Mason F, Scarabello A, Taruffi L, Pasini E, Calandra-Buonaura G, Vignatelli L, Bisulli F. Heart Rate Variability as a Tool for Seizure Prediction: A Scoping Review. J Clin Med 2024; 13:747. [PMID: 38337440 PMCID: PMC10856437 DOI: 10.3390/jcm13030747] [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: 12/06/2023] [Revised: 01/04/2024] [Accepted: 01/22/2024] [Indexed: 02/12/2024] Open
Abstract
The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.
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Affiliation(s)
- Federico Mason
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
| | - Anna Scarabello
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
| | - Lisa Taruffi
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
| | - Elena Pasini
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| | - Giovanna Calandra-Buonaura
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| | - Luca Vignatelli
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
| | - Francesca Bisulli
- Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy; (F.M.); (A.S.); (L.T.); (G.C.-B.); (F.B.)
- IRCCS Institute of Neurological Sciences of Bologna, Full Member of the European Reference Network EpiCARE, 40139 Bologna, Italy;
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Pernice R, Faes L, Feucht M, Benninger F, Mangione S, Schiecke K. Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy. J Neural Eng 2022; 19. [PMID: 35803218 DOI: 10.1088/1741-2552/ac7fba] [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: 03/28/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger causality (GC) and Partial Information Decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy. APPROACH HRV and the envelopes of δ and α EEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV. MAIN RESULTS The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences between δ and α rhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for the δ waves and from the contra-EEG for the α waves in the pre-ictal phase, while these patterns were reversed after the seizure. SIGNIFICANCE These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Martha Feucht
- Epilepsy Monitoring Unit, Department of Child and Adolenscent Neuropsychiatry, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Franz Benninger
- Department of Child and Adolescent Medicine, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Stefano Mangione
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, Sicilia, 90128, ITALY
| | - Karin Schiecke
- Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, Jena, 07743, GERMANY
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Liu B, Zhang Z, Di X, Wang X, Xie L, Xie W, Zhang J. The Assessment of Autonomic Nervous System Activity Based on Photoplethysmography in Healthy Young Men. Front Physiol 2021; 12:733264. [PMID: 34630151 PMCID: PMC8497893 DOI: 10.3389/fphys.2021.733264] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/24/2021] [Indexed: 12/04/2022] Open
Abstract
Noninvasive assessment of autonomic nervous system (ANS) activity is of great importance, but the accuracy of the method used, which is primarily based on electrocardiogram-derived heart rate variability (HRV), has long been suspected. We investigated the feasibility of photoplethysmography (PPG) in ANS evaluation. Data of 32 healthy young men under four different ANS activation patterns were recorded: baseline, slow deep breathing (parasympathetic activation), cold pressor test (peripheral sympathetic activation), and mental arithmetic test (cardiac sympathetic activation). We extracted 110 PPG-based features to construct classification models for the four ANS activation patterns. Using interpretable models based on random forest, the main PPG features related to ANS activation were obtained. Results showed that pulse rate variability (PRV) exhibited similar changes to HRV across the different experiments. The four ANS patterns could be better classified using more PPG-based features compared with using HRV or PRV features, for which the classification accuracies were 0.80, 0.56, and 0.57, respectively. Sensitive features of parasympathetic activation included features of nonlinear (sample entropy), frequency, and time domains of PRV. Sensitive features of sympathetic activation were features of the amplitude and frequency domain of PRV of the PPG derivatives. Subsequently, these sensitive PPG-based features were used to fit the improved HRV parameters. The fitting results were acceptable (p < 0.01), which might provide a better method of evaluating ANS activity using PPG.
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Affiliation(s)
- Binbin Liu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Zhe Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiaohui Di
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoni Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lin Xie
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Wenjun Xie
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jianbao Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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Li Z, Dong Z, Bai X, Liu M. Characterizing the orientation selectivity in V1 and V4 of macaques by quadratic phase coupling. J Neural Eng 2020; 17:036028. [PMID: 32480396 DOI: 10.1088/1741-2552/ab9843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Orientation selectivity is one of the significant characteristics of neurons in the primary visual cortex (V1). Some neurons in extrastriate visual cortical areas also exhibit certain orientation selectivity. But it is still not well understood that how the orientation selectivity generates. Most previous studies about the orientation selectivity are based on the spike firing rate. However, the spikes are prone to be biased by the detection and sorting algorithms. Then, in this paper, the local field potential (LFP) is adopted to investigate the mechanism of orientation selectivity. APPROACH We used the quadratic phase coupling (QPC), which was calculated by wavelet bicoherence, to describe the characteristics of orientation selectivity available in V1 and V4. The raw wideband neural signals were recorded by two chronically implanted multi-electrode arrays, which were placed in V1 and V4 respectively in two macaques performing a selective visual attention task. MAIN RESULTS There is a strong correlation between the total bicoherence (TotalBic), which is a quantization for the overall QPC of frequency pairs in gamma band, and the grating orientation. Furthermore, the QPC distribution at the non-preferred orientation is mainly concentrated in the low frequencies (30-40 Hz) of gamma; while the QPC distribution at the preferred orientation concentrates in both the low frequencies and high frequencies (60-80 Hz) of gamma. In addition, the TotalBic of the gamma-band LFP between V1 and V4 varies with the grating orientations, indicating that the QPC is available in the feedforward link and the gamma-band LFP in V1 modulates the QPC in V4. SIGNIFICANCE The QPC reflects the orientations of the sinusoidal grating and describes the interaction of gamma-band LFP between different brain regions. Our results suggest that the QPC is an alternative avenue to explore the mechanism for generating orientation selectivity of visual neurons effectively.
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Affiliation(s)
- Zhaohui Li
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, People's Republic of China. Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao, People's Republic of China
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Schiecke K, Schumann A, Benninger F, Feucht M, Baer KJ, Schlattmann P. Brain–heart interactions considering complex physiological data: processing schemes for time-variant, frequency-dependent, topographical and statistical examination of directed interactions by convergent cross mapping. Physiol Meas 2019; 40:114001. [DOI: 10.1088/1361-6579/ab5050] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Liu B, Yan S, Wang X, Xie L, Tong J, Zhao F, Di X, Yan X, Zhang J. An improved method to evaluate heart rate variability based on time-variant cardiorespiratory relation. J Appl Physiol (1985) 2019; 127:320-327. [PMID: 31219773 DOI: 10.1152/japplphysiol.00125.2019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Frequency domain analysis of heart rate variability (HRV) is a noninvasive method to evaluate the autonomic nervous system (ANS), but the traditional parameters of HRV, i.e., the power spectra of the high-frequency (HF) and low-frequency bands (LF), cannot estimate the activity of the parasympathetic (PNS) and sympathetic nervous systems (SNS) well. The aim of our study was to provide a corrected method to better distinguish the contributions of the PNS and SNS in the HRV spectrum. Respiration has a gating effect on cardiac vagal efferent activity, which induces respiration-locked heart rate (HR) changes because of the fast effect of the PNS. So the respiration-related heart rate (HRr) is closely related to PNS activity. In this study, HR was decomposed into HRr and the respiration-unrelated component (HRru) based on empirical mode decomposition (EMD) and the relationship between HR and respiration. Time-frequency analysis of HRr and HRru was defined as HFr and LFru, respectively, with specific adaptive bands for every signal. Two experimental data sets, representing SNS and PNS activation, respectively, were used for efficiency analysis of our method. Our results show that the corrected HRV predicted ANS activity well. HFr could be an index of PNS activity, LFru mainly reflected SNS activity, and LFru/HFr could be more accurate in representing the sympathovagal balance.NEW & NOTEWORTHY This study includes the time-varying relationship between respiration and heart rate in the analysis of heart rate variability. Correction for low-frequency and high-frequency components based on respiration significantly improved evaluation of the sympathetic and parasympathetic nervous systems.
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Affiliation(s)
- Binbin Liu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Saisai Yan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoni Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lin Xie
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jie Tong
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Fadong Zhao
- Equipment Management and Support College, Engineering University of People's Armed Police, Xi'an, China
| | - Xiaohui Di
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiangguo Yan
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Jianbao Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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Estévez-Báez M, Machado C, García-Sánchez B, Rodríguez V, Alvarez-Santana R, Leisman G, Carrera JME, Schiavi A, Montes-Brown J, Arrufat-Pié E. Autonomic impairment of patients in coma with different Glasgow coma score assessed with heart rate variability. Brain Inj 2019; 33:496-516. [PMID: 30755043 DOI: 10.1080/02699052.2018.1553312] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
PRIMARY OBJECTIVE The objective of this study is to assess the functional state of the autonomic nervous system in healthy individuals and in individuals in coma using measures of heart rate variability (HRV) and to evaluate its efficiency in predicting mortality. DESIGN AND METHODS Retrospective group comparison study of patients in coma classified into two subgroups, according to their Glasgow coma score, with a healthy control group. HRV indices were calculated from 7 min of artefact-free electrocardiograms using the Hilbert-Huang method in the spectral range 0.02-0.6 Hz. A special procedure was applied to avoid confounding factors. Stepwise multiple regression logistic analysis (SMLRA) and ROC analysis evaluated predictions. RESULTS Progressive reduction of HRV was confirmed and was associated with deepening of coma and a mortality score model that included three spectral HRV indices of absolute power values of very low, low and very high frequency bands (0.4-0.6 Hz). The SMLRA model showed sensitivity of 95.65%, specificity of 95.83%, positive predictive value of 95.65%, and overall efficiency of 95.74%. CONCLUSIONS HRV is a reliable method to assess the integrity of the neural control of the caudal brainstem centres on the hearts of patients in coma and to predict patient mortality.
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Affiliation(s)
- Mario Estévez-Báez
- a Department of Clinical Neurophysiology , Institute of Neurology and Neurosurgery , Havana , Cuba
| | - Calixto Machado
- a Department of Clinical Neurophysiology , Institute of Neurology and Neurosurgery , Havana , Cuba
| | | | | | | | - Gerry Leisman
- d Faculty of Health Sciences , University of Haifa , Haifa , Israel
| | | | - Adam Schiavi
- e Anesthesiology and Critical Care Medicine, Neurosciences Critical Care Division , Johns Hopkins Hospital , Baltimore , MD , USA
| | - Julio Montes-Brown
- f Department of Medicine & Health Science , University of Sonora , Sonora , Mexico
| | - Eduardo Arrufat-Pié
- g Institute of Basic and Preclinical Sciences, "Victoria de Girón" , Havana , Cuba
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Schmidt C, Piper D, Putsche P, Feucht M, Witte H, Leistritz L, Schiecke K. Assignment of Empirical Mode Decomposition Components and Its Application to Biomedical Signals. Methods Inf Med 2018; 54:461-73. [DOI: 10.3414/me14-02-0024] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Accepted: 07/06/2015] [Indexed: 11/09/2022]
Abstract
SummaryObjectives: Empirical mode decomposition (EMD) is a frequently used signal processing approach which adaptively decomposes a signal into a set of narrow-band components known as intrinsic mode functions (IMFs). For multi-trial, multivariate (multiple simultaneous recordings), and multi-subject analyses the number and signal properties of the IMFs can deviate from each other between trials, channels and subjects. A further processing of IMFs, e.g. a simple ensemble averaging, should determine which IMFs of one signal correspond to IMFs from another signal. When the signal properties have similar characteristics, the IMFs are assigned to each other. This problem is known as correspondence problem.Methods: From the mathematical point of view, in some cases the correspondence problem can be transformed into an assignment problem which can be solved e.g. by the Kuhn-Munkres algorithm (KMA) by which a minimal cost matching can be found. We use the KMA for solving classic assignment problems, i.e. the pairwise correspondence between two sets of IMFs of equal cardinalities, and for pairwise correspondences between two sets of IMFs with different cardinalities representing an unbalanced assignment problem which is a special case of the k-cardinality assignment problem.Results: A KMA-based approach to solve the correspondence problem was tested by using simulated, heart rate variability (HRV), and EEG data. The KMA-based results of HRV decomposition are compared with those obtained from a hierarchical cluster analysis (state-of-the-art). The major difference between the two approaches is that there is a more consistent assignment pattern using KMA. Integrating KMA into complex analysis concepts enables a comprehensive exploitation of the key advantages of the EMD. This can be demonstrated by non-linear analysis of HRV-related IMFs and by an EMD-based cross-frequency coupling analysis of the EEG data.Conclusions: The successful application to HRV and EEG analysis demonstrates that our solutions can be used for automated EMD-based processing concepts for biomedical signals.
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Schiecke K, Pester B, Piper D, Feucht M, Benninger F, Witte H, Leistritz L. Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course. ACTA ACUST UNITED AC 2017. [DOI: 10.1051/epjnbp/2017002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Chouchou F, Bouet R, Pichot V, Catenoix H, Mauguière F, Jung J. The neural bases of ictal tachycardia in temporal lobe seizures. Clin Neurophysiol 2017; 128:1810-1819. [PMID: 28709879 DOI: 10.1016/j.clinph.2017.06.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 05/03/2017] [Accepted: 06/02/2017] [Indexed: 02/07/2023]
Abstract
OBJECTIVE Due to limited information from scalp electroencephalographic (EEG) recordings, brain areas driving changes in cardiac rhythm during Temporal lobe (TL) seizures are not clearly identified. Using stereotactic EEG (SEEG) recordings, we aimed at identifying which of the brain regions involved in autonomic control trigger ictal tachycardia. METHODS The neural activity of several mesial temporal lobe structures including amygdala, hippocampus, insula, and lateral temporal lobe recorded with SEEG were collected during 37 TL seizures in 9 patients, using indices based on High Frequency Activity (HFA). R-R intervals (RR) monitoring and time-frequency spectral analysis were performed to assess parasympathetic (High frequency power (HF)) and sympathetic (Low frequency/High frequency (LF/HF) ratio) reactivities. RESULTS Tachycardia was associated with a significant increase in LF/HF ratio and decrease in HF. Autonomic cardiac changes were accompanied by simultaneous SEEG signal changes with an increase in seizure-related HFA in anterior hippocampal formation and amygdala, but not in insula. CONCLUSION In our sample, TL seizures are thus accompanied by an early decrease in parasympathetic control of cardiac rhythm and by an increase of sympathetic tone, concomitant to seizure activity in anterior hippocampus and amygdala. SIGNIFICANCE These results support a pivotal role of hippocampus and amygdala in tachycardia occurring during TL seizures.
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Affiliation(s)
- Florian Chouchou
- NeuroPain Lab, Lyon Neuroscience Research Center - Inserm U 1028/CNRS UMR 5292, University of Lyon, France.
| | - Romain Bouet
- Dycog Lab, Lyon Neuroscience Research Center - Inserm U 1028/CNRS UMR 5292, University of Lyon, France
| | - Vincent Pichot
- Clinical Physiology Department, CHU Nord, Saint-Etienne, France; EA 4607 SNA-EPIS Lab, University of Jean Monnet, University of Lyon, Saint-Etienne, France
| | - Hélène Catenoix
- Epilepsy and Functional Neurology Department, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Bron, France
| | - François Mauguière
- NeuroPain Lab, Lyon Neuroscience Research Center - Inserm U 1028/CNRS UMR 5292, University of Lyon, France; Epilepsy and Functional Neurology Department, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Bron, France; Claude Bernard Lyon 1 University, Lyon, France
| | - Julien Jung
- Dycog Lab, Lyon Neuroscience Research Center - Inserm U 1028/CNRS UMR 5292, University of Lyon, France; Epilepsy and Functional Neurology Department, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Bron, France
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Wang X, Liu B, Xie L, Yu X, Li M, Zhang J. Cerebral and neural regulation of cardiovascular activity during mental stress. Biomed Eng Online 2016; 15:160. [PMID: 28155673 PMCID: PMC5260034 DOI: 10.1186/s12938-016-0255-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
Background Mental arithmetic has been verified inducing cerebral and cardiovascular responses. However, the mechanism and sequential responses are still ambiguous. This study aims to reveal the mechanism of cardiovascular and autonomic responses and the related scalp positions that regulate the autonomic nerves system (ANS) during MA task. Methods 34 healthy male subjects aged between 19 and 27 years old (mean age 23.6 ± 2.3 years) were recruited in. Electrocardiogram, impedance cardiography, beat-to-beat blood pressure and electroencephalography were measured simultaneously and continuously during the experiments. And the analysis of time–frequency, approximate entropy and Pearson correlation coefficient were adopted. For statistical comparison, paired t test is utilized in the study. Results The results showed that mental arithmetic task increased heart rate (from 72.35 ± 1.88 to 80.38 ± 2.34), blood pressure (systolic blood pressure: from 112.09 ± 3.23 to 126.79 ± 3.44; diastolic blood pressure: from 74.15 ± 1.93 to 81.20 ± 1.97), and cardiac output (from 8.71 ± 0.30 to 9.68 ± 0.35), and the mental arithmetic induced physiological responses could be divided into two stages, the first stage (10–110 s) and late stage (150–250 s). The high frequency power component (HF) of HRV decreased during MA, but the normalized low frequency power component (nLF) and LF/HF ratio of HRV increased only at the late stage. Moreover, during first stage, the correlations between approximate entropy of electroencephalography at Fp2, Fz, F4, F7 and the corresponding time–frequency results of HF were significant. During the late stage, the correlations between approximate entropy of electroencephalography at Fp2, Fz, C3, C4 and the corresponding nLF was significant. Conclusions Our results demonstrated that (1) mental stress induces time-dependent ANS activity and cardiovascular response. (2) Parasympathetic activity is lower during mental arithmetic task, but sympathetic nerve is activated only during late stage of mental arithmetic task. (3) Brain influences the cardiac activity through prefrontal and temporal cortex with the activation of ANS during mental arithmetic.
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Affiliation(s)
- Xiaoni Wang
- Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Binbin Liu
- Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Lin Xie
- Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xiaolin Yu
- Department of Information Engineering, Officers College of CAPF, Chengdu, 610213, China
| | - Mengjun Li
- Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Jianbao Zhang
- Key Laboratory of Biomedical Information Engineering of Education Ministry, Xi'an Jiaotong University, Xi'an, 710049, China.
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13
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Schiecke K, Pester B, Piper D, Benninger F, Feucht M, Leistritz L, Witte H. Nonlinear Directed Interactions Between HRV and EEG Activity in Children With TLE. IEEE Trans Biomed Eng 2016; 63:2497-2504. [DOI: 10.1109/tbme.2016.2579021] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Piper D, Schiecke K, Witte H, Leistritz L. Comparative study of methods for solving the correspondence problem in EMD applications. CURRENT DIRECTIONS IN BIOMEDICAL ENGINEERING 2016. [DOI: 10.1515/cdbme-2016-0050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Abstract
We address the correspondence problem which arises when applying empirical mode decomposition (EMD) to multi-trial and multi-subject data. EMD decomposes a signal into a set of narrow-band components named intrinsic mode functions (IMFs). The number of IMFs and their signal properties can be different between trials, channels and subjects. In order to assign IMFs with similar characteristics to each other, we compare two assignment methods, unbalanced assignment and k-cardinality assignment and two clustering algorithms, namely hierarchical clustering and density-based spatial clustering of applications with noise based on heart rate variability data of children with temporal lobe epilepsy.
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Affiliation(s)
- Diana Piper
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Karin Schiecke
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Herbert Witte
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany
| | - Lutz Leistritz
- Bernstein Group for Computational Neuroscience Jena, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Germany
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15
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Baumgartner C, Caiani EG, Dickhaus H, Kulikowski CA, Schiecke K, van Bemmel JH, Witte H. Discussion of "Computational Electrocardiography: Revisiting Holter ECG Monitoring". Methods Inf Med 2016; 55:312-21. [PMID: 27406570 DOI: 10.3414/me15-15-0009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Computational Electrocardiography: Revisiting Holter ECG Monitoring" written by Thomas M. Deserno and Nikolaus Marx. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Deserno and Marx. In subsequent issues the discussion can continue through letters to the editor.
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Affiliation(s)
| | | | | | | | | | | | - Herbert Witte
- Herbert Witte, Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich-Schiller University, Bachstraße 18, 07743 Jena, E-mail:
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16
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Qaraqe M, Ismail M, Serpedin E, Zulfi H. Epileptic seizure onset detection based on EEG and ECG data fusion. Epilepsy Behav 2016; 58:48-60. [PMID: 27057745 DOI: 10.1016/j.yebeh.2016.02.039] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 02/24/2016] [Accepted: 02/26/2016] [Indexed: 11/19/2022]
Abstract
This paper presents a novel method for seizure onset detection using fused information extracted from multichannel electroencephalogram (EEG) and single-channel electrocardiogram (ECG). In existing seizure detectors, the analysis of the nonlinear and nonstationary ECG signal is limited to the time-domain or frequency-domain. In this work, heart rate variability (HRV) extracted from ECG is analyzed using a Matching-Pursuit (MP) and Wigner-Ville Distribution (WVD) algorithm in order to effectively extract meaningful HRV features representative of seizure and nonseizure states. The EEG analysis relies on a common spatial pattern (CSP) based feature enhancement stage that enables better discrimination between seizure and nonseizure features. The EEG-based detector uses logical operators to pool SVM seizure onset detections made independently across different EEG spectral bands. Two fusion systems are adopted. In the first system, EEG-based and ECG-based decisions are directly fused to obtain a final decision. The second fusion system adopts an override option that allows for the EEG-based decision to override the fusion-based decision in the event that the detector observes a string of EEG-based seizure decisions. The proposed detectors exhibit an improved performance, with respect to sensitivity and detection latency, compared with the state-of-the-art detectors. Experimental results demonstrate that the second detector achieves a sensitivity of 100%, detection latency of 2.6s, and a specificity of 99.91% for the MAJ fusion case.
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Affiliation(s)
- Marwa Qaraqe
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
| | - Muhammad Ismail
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
| | - Erchin Serpedin
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.
| | - Haneef Zulfi
- Baylor Neurology Clinic, Baylor College of Medicine, 7200 Cambridge St. BCM 609, Houston, TX 77030, USA.
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Schiecke K, Wacker M, Benninger F, Feucht M, Leistritz L, Witte H. Advantages of signal-adaptive approaches for the nonlinear, time-variant analysis of heart rate variability of children with temporal lobe epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:6377-80. [PMID: 25571455 DOI: 10.1109/embc.2014.6945087] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Major aim of our study is to demonstrate that signal-adaptive approaches improve the nonlinear and time-variant analysis of heart rate variability (HRV) of children with temporal lobe epilepsy (TLE). Nonlinear HRV analyses are frequently applied in epileptic patients. As HRV is characterized by components with oscillatory properties frequency-selective methods are in the focus, whereby application of nonlinear analysis to linear filtered signals seems to be doubtful. Signal-adaptive methods that preserve nonlinear properties and utilize only the signal data for an automatic computation of the result could benefit to nonlinear analysis of HRV. Combinations of (1) the signal-adaptive Matched Gabor Transform with time-variant nonlinear bispectral analysis and of (2) signal-adaptive Empirical Mode Decomposition methods with time-variant nonlinear stability analysis are investigated with regard to their application in the analysis of specific HRV components (respiratory sinus arrhythmia and Mayer wave associated low-frequency HRV components) of 18 children with TLE. Changes of timing and coordination of both HRV components during preictal, ictal and postictal periods occur which can be better quantified by advanced signal-adaptive methods. Both approaches contribute with specific importance to the analysis.
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Schiecke K, Wacker M, Benninger F, Feucht M, Leistritz L, Witte H. Matching Pursuit-Based Time-Variant Bispectral Analysis and its Application to Biomedical Signals. IEEE Trans Biomed Eng 2015; 62:1937-48. [DOI: 10.1109/tbme.2015.2407573] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Schiecke K, Pester B, Feucht M, Leistritz L, Witte H. Convergent Cross Mapping: Basic concept, influence of estimation parameters and practical application. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:7418-7421. [PMID: 26738006 DOI: 10.1109/embc.2015.7320106] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In neuroscience, data are typically generated from neural network activity. Complex interactions between measured time series are involved, and nothing or only little is known about the underlying dynamic system. Convergent Cross Mapping (CCM) provides the possibility to investigate nonlinear causal interactions between time series by using nonlinear state space reconstruction. Aim of this study is to investigate the general applicability, and to show potentials and limitation of CCM. Influence of estimation parameters could be demonstrated by means of simulated data, whereas interval-based application of CCM on real data could be adapted for the investigation of interactions between heart rate and specific EEG components of children with temporal lobe epilepsy.
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