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Porcaro C, Seppi D, Pellegrino G, Dainese F, Kassabian B, Pellegrino L, De Nardi G, Grego A, Corbetta M, Ferreri F. Characterization of antiseizure medications effects on the EEG neurodynamic by fractal dimension. Front Neurosci 2024; 18:1401068. [PMID: 38911599 PMCID: PMC11192015 DOI: 10.3389/fnins.2024.1401068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2024] [Accepted: 05/20/2024] [Indexed: 06/25/2024] Open
Abstract
Objectives An important challenge in epilepsy is to define biomarkers of response to treatment. Many electroencephalography (EEG) methods and indices have been developed mainly using linear methods, e.g., spectral power and individual alpha frequency peak (IAF). However, brain activity is complex and non-linear, hence there is a need to explore EEG neurodynamics using nonlinear approaches. Here, we use the Fractal Dimension (FD), a measure of whole brain signal complexity, to measure the response to anti-seizure therapy in patients with Focal Epilepsy (FE) and compare it with linear methods. Materials Twenty-five drug-responder (DR) patients with focal epilepsy were studied before (t1, named DR-t1) and after (t2, named DR-t2) the introduction of the anti-seizure medications (ASMs). DR-t1 and DR-t2 EEG results were compared against 40 age-matched healthy controls (HC). Methods EEG data were investigated from two different angles: frequency domain-spectral properties in δ, θ, α, β, and γ bands and the IAF peak, and time-domain-FD as a signature of the nonlinear complexity of the EEG signals. Those features were compared among the three groups. Results The δ power differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The θ power differed between DR-t1 and DR-t2 (p = 0.015) and between DR-t1 and HC (p = 0.01). The α power, similar to the δ, differed between DR patients pre and post-ASM and HC (DR-t1 vs. HC, p < 0.01 and DR-t2 vs. HC, p < 0.01). The IAF value was lower for DR-t1 than DR-t2 (p = 0.048) and HC (p = 0.042). The FD value was lower in DR-t1 than in DR-t2 (p = 0.015) and HC (p = 0.011). Finally, Bayes Factor analysis showed that FD was 195 times more likely to separate DR-t1 from DR-t2 than IAF and 231 times than θ. Discussion FD measured in baseline EEG signals is a non-linear brain measure of complexity more sensitive than EEG power or IAF in detecting a response to ASMs. This likely reflects the non-oscillatory nature of neural activity, which FD better describes. Conclusion Our work suggests that FD is a promising measure to monitor the response to ASMs in FE.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Institute of Cognitive Sciences and Technologies (ISTC) – National Research Council (CNR), Rome, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| | - Dario Seppi
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Giovanni Pellegrino
- Epilepsy Program, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Filippo Dainese
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Benedetta Kassabian
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Luciano Pellegrino
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Gianluigi De Nardi
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Alberto Grego
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
| | - Maurizio Corbetta
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), Fondazione Biomedica, Padua, Italy
| | - Florinda Ferreri
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Neurology Clinics, Azienda Ospedale Università, Padua, Italy
- Unit of Clinical Neurophysiology, Azienda Ospedale Università, Padua, Italy
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Olejarczyk E, Cukic M, Porcaro C, Zappasodi F, Tecchio F. Clinical Sensitivity of Fractal Neurodynamics. ADVANCES IN NEUROBIOLOGY 2024; 36:285-312. [PMID: 38468039 DOI: 10.1007/978-3-031-47606-8_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Among the significant advances in the understanding of the organization of the neuronal networks that coordinate the body and brain, their complex nature is increasingly important, resulting from the interaction between the very large number of constituents strongly organized hierarchically and at the same time with "self-emerging." This awareness drives us to identify the measures that best quantify the "complexity" that accompanies the continuous evolutionary dynamics of the brain. In this chapter, after an introductory section (Sect. 15.1), we examine how the Higuchi fractal dimension is able to perceive physiological processes (15.2), neurological (15.3) and psychiatric (15.4) disorders, and neuromodulation effects (15.5), giving a mention of other methods of measuring neuronal electrical activity in addition to electroencephalography, such as magnetoencephalography and functional magnetic resonance. Conscious that further progress will support a deeper understanding of the temporal course of neuronal activity because of continuous interaction with the environment, we conclude confident that the fractal dimension has begun to uncover important features of the physiology of brain activity and its alterations.
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Affiliation(s)
- Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland.
| | - Milena Cukic
- Department of Biomimetic Membranes and Textiles, EMPA Material Science and Technology, St. Gallen, Switzerland
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
| | - Filippo Zappasodi
- Department of Neuroscienze, Imaging and Clinical Sciences, Gabriele D'annunzio University, Chieti, Italy
| | - Franca Tecchio
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy
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3
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Hagan B, Mujumdar R, Sahoo JP, Das A, Dutta A. Technical feasibility of multimodal imaging in neonatal hypoxic-ischemic encephalopathy from an ovine model to a human case series. Front Pediatr 2023; 11:1072663. [PMID: 37425273 PMCID: PMC10323750 DOI: 10.3389/fped.2023.1072663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 06/02/2023] [Indexed: 07/11/2023] Open
Abstract
Hypoxic-ischemic encephalopathy (HIE) secondary to perinatal asphyxia occurs when the brain does not receive enough oxygen and blood. A surrogate marker for "intact survival" is necessary for the successful management of HIE. The severity of HIE can be classified based on clinical presentation, including the presence of seizures, using a clinical classification scale called Sarnat staging; however, Sarnat staging is subjective, and the score changes over time. Furthermore, seizures are difficult to detect clinically and are associated with a poor prognosis. Therefore, a tool for continuous monitoring on the cot side is necessary, for example, an electroencephalogram (EEG) that noninvasively measures the electrical activity of the brain from the scalp. Then, multimodal brain imaging, when combined with functional near-infrared spectroscopy (fNIRS), can capture the neurovascular coupling (NVC) status. In this study, we first tested the feasibility of a low-cost EEG-fNIRS imaging system to differentiate between normal, hypoxic, and ictal states in a perinatal ovine hypoxia model. Here, the objective was to evaluate a portable cot-side device and perform autoregressive with extra input (ARX) modeling to capture the perinatal ovine brain states during a simulated HIE injury. So, ARX parameters were tested with a linear classifier using a single differential channel EEG, with varying states of tissue oxygenation detected using fNIRS, to label simulated HIE states in the ovine model. Then, we showed the technical feasibility of the low-cost EEG-fNIRS device and ARX modeling with support vector machine classification for a human HIE case series with and without sepsis. The classifier trained with the ovine hypoxia data labeled ten severe HIE human cases (with and without sepsis) as the "hypoxia" group and the four moderate HIE human cases as the "control" group. Furthermore, we showed the feasibility of experimental modal analysis (EMA) based on the ARX model to investigate the NVC dynamics using EEG-fNIRS joint-imaging data that differentiated six severe HIE human cases without sepsis from four severe HIE human cases with sepsis. In conclusion, our study showed the technical feasibility of EEG-fNIRS imaging, ARX modeling of NVC for HIE classification, and EMA that may provide a biomarker of sepsis effects on the NVC in HIE.
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Affiliation(s)
- Brian Hagan
- School of Engineering, University of Lincoln, Lincoln, United Kingdom
| | - Radhika Mujumdar
- School of Engineering, University of Lincoln, Lincoln, United Kingdom
| | - Jagdish P. Sahoo
- Department of Neonatology, IMS & SUM Hospital, Bhubaneswar, India
| | - Abhijit Das
- Department of Neurology, The Lancashire Teaching Hospitals NHS Foundation Trust, Preston, United Kingdom
| | - Anirban Dutta
- School of Engineering, University of Lincoln, Lincoln, United Kingdom
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A complex systems perspective on psychedelic brain action. Trends Cogn Sci 2023; 27:433-445. [PMID: 36740518 DOI: 10.1016/j.tics.2023.01.003] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 01/10/2023] [Accepted: 01/17/2023] [Indexed: 02/05/2023]
Abstract
Recent findings suggesting the potential transdiagnostic efficacy of psychedelic-assisted therapy have fostered the need to deepen our understanding of psychedelic brain action. Functional neuroimaging investigations have found that psychedelics reduce the functional segregation of large-scale brain networks. However, beyond this general trend, findings have been largely inconsistent. We argue here that a perspective based on complexity science that foregrounds the distributed, interactional, and dynamic nature of brain function may render these inconsistencies intelligible. We propose that psychedelics induce a mode of brain function that is more dynamically flexible, diverse, integrated, and tuned for information sharing, consistent with greater criticality. This 'meta' perspective has the potential to unify past findings and guide intuitions toward compelling mechanistic models.
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N.J. S, M.S.P. S, S. TG. EEG-based classification of normal and seizure types using relaxed local neighbour difference pattern and artificial neural network. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108508] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Ehrens D, Cervenka MC, Bergey GK, Jouny CC. Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset. Clin Neurophysiol 2022; 135:85-95. [PMID: 35065325 PMCID: PMC8857071 DOI: 10.1016/j.clinph.2021.12.011] [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: 03/08/2021] [Revised: 11/19/2021] [Accepted: 12/26/2021] [Indexed: 11/25/2022]
Abstract
OBJECTIVE To develop an adaptive framework for seizure detection in real-time that is practical to use in the Epilepsy Monitoring Unit (EMU) as a warning signal, and whose output helps characterize epileptiform activity. METHODS Our algorithm was tested on intracranial EEG from epilepsy patients admitted to the EMU for presurgical evaluation. Our framework uses a one-class Support Vector Machine (SVM) that is being trained dynamically according to past activity in all available channels to classify the novelty of the current activity. In this study we compared multiple configurations using a one-class SVM to assess if there is significance over specific neural features or electrode locations. RESULTS Our results show that the algorithm reaches a sensitivity of 87% for early-onset seizure detection and of 97.7% as a generic seizure detection. CONCLUSIONS Our algorithm is capable of running in real-time and achieving a high performance for early seizure-onset detection with a low false positive rate and robustness in detection of different type of seizure-onset patterns. SIGNIFICANCE This algorithm offers a solution to warning systems in the EMU as well as a tool for seizure characterization during post-hoc analysis of intracranial EEG data for surgical resection of the epileptogenic network.
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Affiliation(s)
- Daniel Ehrens
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Mackenzie C. Cervenka
- Department of Neurology, Johns Hopkins University School of
Medicine, Baltimore, MD, USA
| | - Gregory K. Bergey
- Department of Neurology, Johns Hopkins University School of
Medicine, Baltimore, MD, USA
| | - Christophe C. Jouny
- Department of Neurology, Johns Hopkins University School of
Medicine, Baltimore, MD, USA
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7
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Ali E, Udhayakumar RK, Angelova M, Karmakar C. Performance Analysis of Entropy Methods in Detecting Epileptic Seizure from Surface Electroencephalograms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:1082-1085. [PMID: 34891475 DOI: 10.1109/embc46164.2021.9629538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Physiological signals like Electrocardiography (ECG) and Electroencephalography (EEG) are complex and nonlinear in nature. To retrieve diagnostic information from these, we need the help of nonlinear methods of analysis. Entropy estimation is a very popular approach in the nonlinear category, where entropy estimates are used as features for signal classification and analysis. In this study, we analyze and compare the performances of four entropy methods; namely Distribution entropy (DistEn), Shannon entropy (ShanEn), Renyi entropy (RenEn) and LempelZiv complexity (LempelZiv) as classification features to detect epileptic seizure (ES) from surface Electroencephalography (sEEG) signal. Experiments were conducted on sEEG data from 23 subjects, obtained from the CHB-MIT database of PhysioNet. ShanEn, RenEn and LempelZiv entropy are found to be potential features for accurate and consistent detection of ES from sEEG, across multiple channels and subjects.
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Mayor D, Panday D, Kandel HK, Steffert T, Banks D. CEPS: An Open Access MATLAB Graphical User Interface (GUI) for the Analysis of Complexity and Entropy in Physiological Signals. ENTROPY 2021; 23:e23030321. [PMID: 33800469 PMCID: PMC7998823 DOI: 10.3390/e23030321] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/28/2021] [Accepted: 03/03/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND We developed CEPS as an open access MATLAB® GUI (graphical user interface) for the analysis of Complexity and Entropy in Physiological Signals (CEPS), and demonstrate its use with an example data set that shows the effects of paced breathing (PB) on variability of heart, pulse and respiration rates. CEPS is also sufficiently adaptable to be used for other time series physiological data such as EEG (electroencephalography), postural sway or temperature measurements. METHODS Data were collected from a convenience sample of nine healthy adults in a pilot for a larger study investigating the effects on vagal tone of breathing paced at various different rates, part of a development programme for a home training stress reduction system. RESULTS The current version of CEPS focuses on those complexity and entropy measures that appear most frequently in the literature, together with some recently introduced entropy measures which may have advantages over those that are more established. Ten methods of estimating data complexity are currently included, and some 28 entropy measures. The GUI also includes a section for data pre-processing and standard ancillary methods to enable parameter estimation of embedding dimension m and time delay τ ('tau') where required. The software is freely available under version 3 of the GNU Lesser General Public License (LGPLv3) for non-commercial users. CEPS can be downloaded from Bitbucket. In our illustration on PB, most complexity and entropy measures decreased significantly in response to breathing at 7 breaths per minute, differentiating more clearly than conventional linear, time- and frequency-domain measures between breathing states. In contrast, Higuchi fractal dimension increased during paced breathing. CONCLUSIONS We have developed CEPS software as a physiological data visualiser able to integrate state of the art techniques. The interface is designed for clinical research and has a structure designed for integrating new tools. The aim is to strengthen collaboration between clinicians and the biomedical community, as demonstrated here by using CEPS to analyse various physiological responses to paced breathing.
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Affiliation(s)
- David Mayor
- School of Health and Social Work, University of Hertfordshire, Hatfield AL10 9AB, UK
- Correspondence:
| | - Deepak Panday
- School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| | - Hari Kala Kandel
- Department of Computing, Goldsmiths College, University of London, New Cross, London SE14 6NW, UK;
| | - Tony Steffert
- MindSpire, Napier House, 14-16 Mount Ephraim Rd, Tunbridge Wells TN1 1EE, UK;
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
| | - Duncan Banks
- School of Life, Health and Chemical Sciences, Walton Hall, The Open University, Milton Keynes MK7 6AA, UK;
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An N, Ye X, Liu Q, Xu J, Zhang P. Localization of the epileptogenic zone based on ictal stereo-electroencephalogram: Brain network and single-channel signal feature analysis. Epilepsy Res 2020; 167:106475. [PMID: 33045665 DOI: 10.1016/j.eplepsyres.2020.106475] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 06/22/2020] [Accepted: 09/17/2020] [Indexed: 01/21/2023]
Abstract
Accurate localization of the epileptogenic zone (EZ) is crucial for refractory focal epilepsy patients to achieve freedom from seizures following epilepsy surgery. In this study, ictal stereo-electroencephalography data from 35 patients with refractory focal epilepsy were analyzed. Effective networks based on partial directed coherence were analyzed, and a gray level co-occurrence matrix was applied to extract the time-varying features of the in-degree. These features, combined with the single-channel signal time-frequency features, including approximate entropy and line length, were used to localize the EZ based on a cluster algorithm. For all seizure-free patients (n = 23), the proposed method was effective in identifying the clinical-EZ-contacts and clinical-EZ-blocks, with an F1-score of 62.47 % and 72.18 %, respectively. The sensitivity was 96.00 % for the clinical-EZ-block identification, which provided the information for the decision-making of clinicians, prompting clinicians to focus on the identified EZ-blocks and their nearby contacts. The agreement between the EZ identified by the proposed method and the clinical-EZ was worse for non-seizure-free patients (n = 12) than for seizure-free patients. Furthermore, our method provided better results than using only brain network or single-channel signal features. This suggests that combining these complementary features can facilitate more accurate localization of the EZ.
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Affiliation(s)
- Nan An
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Xiaolai Ye
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Qiangqiang Liu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Jiwen Xu
- Department of Functional Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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The complexity of clinically-normal sinus-rhythm ECGs is decreased in equine athletes with a diagnosis of paroxysmal atrial fibrillation. Sci Rep 2020; 10:6822. [PMID: 32321950 PMCID: PMC7176685 DOI: 10.1038/s41598-020-63343-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 03/09/2020] [Indexed: 11/09/2022] Open
Abstract
Equine athletes have a pattern of exercise which is analogous to human athletes and the cardiovascular risks in both species are similar. Both species have a propensity for atrial fibrillation (AF), which is challenging to detect by ECG analysis when in paroxysmal form. We hypothesised that the proarrhythmic background present between fibrillation episodes in paroxysmal AF (PAF) might be detectable by complexity analysis of apparently normal sinus-rhythm ECGs. In this retrospective study ECG recordings were obtained during routine clinical work from 82 healthy horses and from 10 horses with a diagnosis of PAF. Artefact-free 60-second strips of normal sinus-rhythm ECGs were converted to binary strings using threshold crossing, beat detection and a novel feature detection parsing algorithm. Complexity of the resulting binary strings was calculated using Lempel-Ziv (‘76 & ‘78) and Titchener complexity estimators. Dependence of Lempel-Ziv ‘76 and Titchener T-complexity on the heart rate in ECG strips obtained at low heart rates (25–60 bpm) and processed by the feature detection method was found to be significantly different in control animals and those diagnosed with PAF. This allows identification of horses with PAF from sinus-rhythm ECGs with high accuracy.
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Makaram N, von Ellenrieder N, Tanaka H, Gotman J. Automated classification of five seizure onset patterns from intracranial electroencephalogram signals. Clin Neurophysiol 2020; 131:1210-1218. [PMID: 32299004 DOI: 10.1016/j.clinph.2020.02.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 01/13/2020] [Accepted: 02/04/2020] [Indexed: 11/19/2022]
Abstract
OBJECTIVE The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patterns reported in the literature, and these patterns have clinical significance. In this work, we propose a system to automatically classify five seizure onset patterns from intracerebral EEG signals. METHODS The EEG was segmented by clinicians indicating the start and end time of each seizure onset pattern, the channels involved at onset and the seizure onset pattern. Twelve features that represent the time domain characteristics and signal complexity were extracted from 663 seizures channels of 24 patients. The features were used for classification of the patterns with support vector machine - Error-Correcting Output Codes (SVM-ECOC). Three patient groups with a similar number of seizure segments were created, and one group was used for testing and the rest for training. This test was repeated by rotating the testing and training data. RESULTS The feature space formed by both time domain and multiscale sample entropy features perform well in classification of the data. An overall accuracy of 80.7% was obtained with these features and a linear kernel of SVM-ECOC. CONCLUSIONS The seizure onset patterns consist of varied time and complexity characteristics. It is possible to automatically classify various seizure onset patterns very similarly to visual classification. SIGNIFICANCE The proposed system could aid the medical team in assessing intracerebral EEG by providing an objective classification of seizure onset patterns.
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Affiliation(s)
- Navaneethakrishna Makaram
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Applied Mechanics - Biomedical Engineering Group, Indian Institute of Technology Madras, India.
| | | | - Hideaki Tanaka
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada; Department of Neurosurgery, Fukuoka University Hospital, Fukuoka City, Japan
| | - Jean Gotman
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Classification of intracavitary electrograms in atrial fibrillation using information and complexity measures. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101753] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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13
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Zhou M, Jiang W, Zhong D, Zheng J. Resting-state brain entropy in right temporal lobe epilepsy and its relationship with alertness. Brain Behav 2019; 9:e01446. [PMID: 31605452 PMCID: PMC6851803 DOI: 10.1002/brb3.1446] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Revised: 09/14/2019] [Accepted: 09/18/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND To date, no functional MRI (fMRI) studies have focused on brain entropy in right temporal lobe epilepsy (rTLE) patients. Here, we characterized brain entropy (BEN) alterations in patients with rTLE using resting-state functional MRI(rs-fMRI) and explored the relationship between BEN and alertness. METHOD Thirty-one rTLE patients and 33 controls underwent MRI scanning to investigate differences in BEN and resting-state functional connectivity (rs-FC) in regions of interest (ROIs) between patients and controls. Correlation analyses were performed to examine relationships between the BEN of each ROI and alertness reaction times (RTs) in rTLE patients. RESULTS Compared with controls, the BEN of rTLE patients was significantly increased in the right middle temporal gyrus, inferior temporal gyrus, and other regions of the left hemisphere and significantly decreased in the right middle frontal gyrus and left supplementary motor area (p < .05). The rs-FCs between the ROIs (at p < .01, with the left superior parietal lobule and right precentral gyrus defined as ROI1 and ROI2, respectively) and the whole brain showed an increasing trend in rTLE patients. In addition, the BEN of ROI2 was associated with the intrinsic alertness and phasic alertness RTs of patients with rTLE. CONCLUSIONS Our findings suggest that BEN is altered in patients with rTLE and that decreased BEN in the right precentral gyrus is positively related to intrinsic and phasic alertness; the abnormal FC in the brain regions with altered entropy suggests a reconstruction of brain functional connectivity. These findings suggest that BEN mapping may provide a useful tool for probing brain mechanisms related to TLE.
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Affiliation(s)
- Muhua Zhou
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Wenyu Jiang
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Dan Zhong
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jinou Zheng
- Department of Neurology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Xu K, Zheng Y, Zhang F, Jiang Z, Qi Y, Chen H, Zhu J. An Energy Efficient AdaBoost Cascade Method for Long-Term Seizure Detection in Portable Neurostimulators. IEEE Trans Neural Syst Rehabil Eng 2019; 27:2274-2283. [DOI: 10.1109/tnsre.2019.2947426] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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15
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Ehrens D, Assaf F, Cowan NJ, Sarma SV, Schiller Y. Ultra Broad Band Neural Activity Portends Seizure Onset in a Rat Model of Epilepsy. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2276-2279. [PMID: 30440860 DOI: 10.1109/embc.2018.8512769] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Epilepsy affects over 70 million people worldwide and 30% of patients' seizures cannot be controlled with medications, motivating the development of alternative therapies such as electrical stimulation. Current stimulation strategies attempt to stop seizures after they start, but none aim to prevent seizures altogether. Preventing seizures requires knowing when the brain is entering a preictal state (i.e., approaching seizure onset). Here we show that such preictal activity can be detected by an informative neural signal that progressively and monotonically changes as the brain approaches a seizure event. Specifically, we use local field potentials (LFP) from a rat model of epilepsy to develop an innovative measure of signal novelty relative to nonseizure activity, that shows the presence of progressive neural dynamics in an ultra broad band (4 Hz - 5 kHz). The measure is extracted from functional connectivity features computed from the LFPs which are used as an input to a one-class Support Vector Machine (SVM). The SVM outputs a scalar signal which quantifies how novel the current activity looks relative to baseline (non-seizure) activity and shows a progression towards seizure onset minutes ahead of time. The use of ultra broad band multivariate features into the SVM results in a novelty signal that has a significantly higher slope in the progression to seizure onset when compared to using power in conventional frequency bands (4 - 500 Hz) on individual channels as input features to the SVM. Functional connectivity in conjunction with the SVM is a strategy that generates a new measurement of novelty that can be used by closed-loop systems for seizure forecasting and prevention.
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Alexeenko V, Fraser JA, Dolgoborodov A, Bowen M, Huang CLH, Marr CM, Jeevaratnam K. The application of Lempel-Ziv and Titchener complexity analysis for equine telemetric electrocardiographic recordings. Sci Rep 2019; 9:2619. [PMID: 30796330 PMCID: PMC6385502 DOI: 10.1038/s41598-019-38935-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Accepted: 12/28/2018] [Indexed: 12/19/2022] Open
Abstract
The analysis of equine electrocardiographic (ECG) recordings is complicated by the absence of agreed abnormality classification criteria. We explore the applicability of several complexity analysis methods for characterization of non-linear aspects of electrocardiographic recordings. We here show that complexity estimates provided by Lempel-Ziv ’76, Titchener’s T-complexity and Lempel-Ziv ’78 analysis of ECG recordings of healthy Thoroughbred horses are highly dependent on the duration of analysed ECG fragments and the heart rate. The results provide a methodological basis and a feasible reference point for the complexity analysis of equine telemetric ECG recordings that might be applied to automate detection of equine arrhythmias in equine clinical practice.
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Affiliation(s)
- Vadim Alexeenko
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, United Kingdom.,Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom
| | - James A Fraser
- Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom
| | | | - Mark Bowen
- Faculty of Medicine & Health Sciences, University of Nottingham, Nottingham, NG7 2UH, United Kingdom
| | - Christopher L-H Huang
- Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.,Division of Cardiovascular Biology, Department of Biochemistry, University of Cambridge, Cambridge, CB2 1QW, United Kingdom
| | - Celia M Marr
- Rossdales Equine Hospital and Diagnostic Centre, Exning, CB8 7NN, Suffolk, United Kingdom
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, United Kingdom. .,Physiological Laboratory, University of Cambridge, Cambridge, CB2 3DY, United Kingdom.
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Mamli S, Kalbkhani H. Gray-level co-occurrence matrix of Fourier synchro-squeezed transform for epileptic seizure detection. Biocybern Biomed Eng 2019. [DOI: 10.1016/j.bbe.2018.10.006] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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18
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Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy. ENTROPY 2018; 20:e20060419. [PMID: 33265509 PMCID: PMC7512937 DOI: 10.3390/e20060419] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/20/2018] [Accepted: 05/26/2018] [Indexed: 12/02/2022]
Abstract
Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time–frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures’ onset. These trends pointed to a slow decrease of the epileptic brain’s global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.
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Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review. SENSORS 2018; 18:s18061720. [PMID: 29861451 PMCID: PMC6022076 DOI: 10.3390/s18061720] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 05/22/2018] [Accepted: 05/23/2018] [Indexed: 01/03/2023]
Abstract
Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.
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Karthick P, Tanaka H, Khoo HM, Gotman J. Prediction of secondary generalization from a focal onset seizure in intracerebral EEG. Clin Neurophysiol 2018; 129:1030-1040. [DOI: 10.1016/j.clinph.2018.02.122] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/01/2018] [Accepted: 02/08/2018] [Indexed: 01/06/2023]
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When the Waves Become Rainbows: Improving Seizure Detection in the Pediatric ICU. Epilepsy Curr 2018; 18:89-91. [DOI: 10.5698/1535-7597.18.2.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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22
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Generalized Stockwell transform and SVD-based epileptic seizure detection in EEG using random forest. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.03.007] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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23
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Kesić S, Spasić SZ. Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 133:55-70. [PMID: 27393800 DOI: 10.1016/j.cmpb.2016.05.014] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/24/2016] [Accepted: 05/27/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVE For more than 20 years, Higuchi's fractal dimension (HFD), as a nonlinear method, has occupied an important place in the analysis of biological signals. The use of HFD has evolved from EEG and single neuron activity analysis to the most recent application in automated assessments of different clinical conditions. Our objective is to provide an updated review of the HFD method applied in basic and clinical neurophysiological research. METHODS This article summarizes and critically reviews a broad literature and major findings concerning the applications of HFD for measuring the complexity of neuronal activity during different neurophysiological conditions. The source of information used in this review comes from the PubMed, Scopus, Google Scholar and IEEE Xplore Digital Library databases. RESULTS The review process substantiated the significance, advantages and shortcomings of HFD application within all key areas of basic and clinical neurophysiology. Therefore, the paper discusses HFD application alone, combined with other linear or nonlinear measures, or as a part of automated methods for analyzing neurophysiological signals. CONCLUSIONS The speed, accuracy and cost of applying the HFD method for research and medical diagnosis make it stand out from the widely used linear methods. However, only a combination of HFD with other nonlinear methods ensures reliable and accurate analysis of a wide range of neurophysiological signals.
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Affiliation(s)
- Srdjan Kesić
- University of Belgrade, Institute for Biological Research "Siniša Stanković", Department of Neurophysiology, Bulevar Despota Stefana 142, 11060 Belgrade, Serbia
| | - Sladjana Z Spasić
- University of Belgrade, Institute for Multidisciplinary Research, Department of Life Sciences, Kneza Višeslava 1, 11030 Belgrade, Serbia; Singidunum University, Danijelova 32, 11010 Belgrade, Serbia.
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Wang Y, Qi Y, Wang Y, Lei Z, Zheng X, Pan G. Delving intoα-stable distribution in noise suppression for seizure detection from scalp EEG. J Neural Eng 2016; 13:056009. [DOI: 10.1088/1741-2560/13/5/056009] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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25
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Artan NS. EEG analysis via multiscale Lempel-Ziv complexity for seizure detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:4535-4538. [PMID: 28269285 DOI: 10.1109/embc.2016.7591736] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Robust seizure detection and seizure prediction continues to be a challenge. Lempel-Ziv Complexity (LZC) is one of the features that has shown to be relevant in seizure detection. Recent work has shown that augmenting LZC can be beneficial to emphasize variations in amplitude or frequency when analyzing biomedical signals. In this paper, we present a first look into evaluating the feasibility of using a recently proposed feature stemmed from LZC, namely the Multiscale Lempel-Ziv Complexity (MLZC) for seizure detection. MLZC does not allow the high-frequency signal components to be overwhelmed by the low frequency signal components when calculating complexity values. We compare MLZC and LZC for identifying seizures for three cases and show MLZC can provide a clear separation between non-ictal and ictal periods for all three cases using a single threshold over 7 recordings and 7 seizures per patient, whereas LZC provided such a clear separation for only one of the patients.
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Zhang Y, Zhou W, Yuan S. Multifractal Analysis and Relevance Vector Machine-Based Automatic Seizure Detection in Intracranial EEG. Int J Neural Syst 2015; 25:1550020. [DOI: 10.1142/s0129065715500203] [Citation(s) in RCA: 79] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automatic seizure detection technology is of great significance for long-term electroencephalogram (EEG) monitoring of epilepsy patients. The aim of this work is to develop a seizure detection system with high accuracy. The proposed system was mainly based on multifractal analysis, which describes the local singular behavior of fractal objects and characterizes the multifractal structure using a continuous spectrum. Compared with computing the single fractal dimension, multifractal analysis can provide a better description on the transient behavior of EEG fractal time series during the evolvement from interictal stage to seizures. Thus both interictal EEG and ictal EEG were analyzed by multifractal formalism and their differences in the multifractal features were used to distinguish the two class of EEG and detect seizures. In the proposed detection system, eight features (α0, αmin, αmax, Δα, f(α min ), f(α max ), Δf and R) were extracted from the multifractal spectrums of the preprocessed EEG to construct feature vectors. Subsequently, relevance vector machine (RVM) was applied for EEG patterns classification, and a series of post-processing operations were used to increase the accuracy and reduce false detections. Both epoch-based and event-based evaluation methods were performed to appraise the system's performance on the EEG recordings of 21 patients in the Freiburg database. The epoch-based sensitivity of 92.94% and specificity of 97.47% were achieved, and the proposed system obtained a sensitivity of 92.06% with a false detection rate of 0.34/h in event-based performance assessment.
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Affiliation(s)
- Yanli Zhang
- School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China
- School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, P. R. China
| | - Weidong Zhou
- School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan 250100, P. R. China
- Suzhou Institute of Shandong University, Suzhou 215123, P. R. China
| | - Shasha Yuan
- School of Information Science and Engineering, Shandong University, 27 Shanda Road, Jinan 250100, P. R. China
- Suzhou Institute of Shandong University, Suzhou 215123, P. R. China
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Bai Y, Liang Z, Li X. A permutation Lempel-Ziv complexity measure for EEG analysis. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2015.04.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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28
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Inspecting temporal scales with non-linear signal features: A way to extract more information from brain activity? Clin Neurophysiol 2015; 126:435-6. [DOI: 10.1016/j.clinph.2014.07.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 07/11/2014] [Indexed: 11/21/2022]
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Amigó JM, Keller K, Unakafova VA. Ordinal symbolic analysis and its application to biomedical recordings. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2015; 373:20140091. [PMID: 25548264 PMCID: PMC4281864 DOI: 10.1098/rsta.2014.0091] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Ordinal symbolic analysis opens an interesting and powerful perspective on time-series analysis. Here, we review this relatively new approach and highlight its relation to symbolic dynamics and representations. Our exposition reaches from the general ideas up to recent developments, with special emphasis on its applications to biomedical recordings. The latter will be illustrated with epilepsy data.
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Affiliation(s)
- José M Amigó
- Centro de Investigación Operativa, Universidad Miguel Hernández, Avda. de la Universidad s/n, 03202 Elche, Spain
| | - Karsten Keller
- Institut für Mathematik, Universität zu Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
| | - Valentina A Unakafova
- Institut für Mathematik, Universität zu Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany Graduate School for Computing in Medicine and Life Science, Universität zu Lübeck, Ratzeburger Allee 160, 23562 Lübeck, Germany
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30
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Poincaré analysis of the electroencephalogram during sevoflurane anesthesia. Clin Neurophysiol 2015; 126:404-11. [DOI: 10.1016/j.clinph.2014.04.019] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 04/27/2014] [Accepted: 04/30/2014] [Indexed: 11/21/2022]
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Ibáñez-Molina AJ, Iglesias-Parro S, Soriano MF, Aznarte JI. Multiscale Lempel-Ziv complexity for EEG measures. Clin Neurophysiol 2014; 126:541-8. [PMID: 25127707 DOI: 10.1016/j.clinph.2014.07.012] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2014] [Revised: 07/04/2014] [Accepted: 07/08/2014] [Indexed: 11/18/2022]
Abstract
OBJECTIVE To demonstrate that the classical calculation of Lempel-Ziv complexity (LZC) has an important limitation when applied to EEGs with rapid rhythms, and to propose a multiscale approach that overcomes this limitation. METHODS We have evaluated, both with simulated and real EEGs, whether LZC calculation neglects functional characteristics of rapid EEG rhythms. In addition, we have proposed a procedure to obtain multiple binarization sequences that yield a spectrum of LZC, and we have explored whether complexity would be better captured using this computation. RESULTS In our simulated signals, classical LZC did not capture modulations of a rapid component when a slower component of more amplitude was included in the signal. In real EEGs from healthy participants with eyes closed and eyes open, classical LZC calculation failed to show any difference between these two conditions. However, a multiscale LZC showed that complexity was lower for eyes closed than for eyes open conditions. CONCLUSIONS As hypothesized, our new approximation captures the complexity of series with fast components masked by slower rhythms. SIGNIFICANCE The method we introduce significantly improves LZC calculation, and it allows a better characterization of complexity of EEG signals.
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Robust deep network with maximum correntropy criterion for seizure detection. BIOMED RESEARCH INTERNATIONAL 2014; 2014:703816. [PMID: 25105136 PMCID: PMC4106070 DOI: 10.1155/2014/703816] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 06/04/2014] [Indexed: 11/17/2022]
Abstract
Effective seizure detection from long-term EEG is highly important for seizure diagnosis. Existing methods usually design the feature and classifier individually, while little work has been done for the simultaneous optimization of the two parts. This work proposes a deep network to jointly learn a feature and a classifier so that they could help each other to make the whole system optimal. To deal with the challenge of the impulsive noises and outliers caused by EMG artifacts in EEG signals, we formulate a robust stacked autoencoder (R-SAE) as a part of the network to learn an effective feature. In R-SAE, the maximum correntropy criterion (MCC) is proposed to reduce the effect of noise/outliers. Unlike the mean square error (MSE), the output of the new kernel MCC increases more slowly than that of MSE when the input goes away from the center. Thus, the effect of those noises/outliers positioned far away from the center can be suppressed. The proposed method is evaluated on six patients of 33.6 hours of scalp EEG data. Our method achieves a sensitivity of 100% and a specificity of 99%, which is promising for clinical applications.
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Ge T, Qi Y, Wang Y, Chen W, Zheng X. A boosted cascade for efficient epileptic seizure detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:6309-12. [PMID: 24111183 DOI: 10.1109/embc.2013.6610996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Seizure detection from electroencephalogram (EEG) plays an important role for epilepsy therapy. Due to the diversity of seizure EEG patterns between different individuals, multiple features are necessary for high accuracy since a single feature could hardly encode all types of epileptiform discharges. However, a large feature set inevitably causes the increase of the computational cost. This paper proposes a boosted cascade chain to obtain both high detection performance and high computational efficiency. Sixteen features that are widely used in seizure detection are implemented. Considering the sequential characteristics of EEG signals, the features are extracted on each 1-second segment and its former three segments. Thus, a total of 64 features are used to construct a feature pool. Based on the feature pool, Real AdaBoost is used to select a group of effective features, on which weak classifiers are learned to assemble a strong classifier. The strong classifier is transformed to a cascade classifier by reordering the weak classifiers and learning a threshold for each weak classifier. The cascade classifier still has the similar classification strength to the original strong classifier. More importantly, it is able to reject easy non-seizure samples by the first a few weak classifiers in the cascade, thus high computational efficiency can be obtained. To evaluate our method, 90.6-hour EEG signals from four patients are tested. The experimental results show that our method can achieve an average accuracy of 95.31% and an average detection rate of 91.29% with the false positive rate of 4.68%. On average, only about 4 features are used. Compared with support vector machine (SVM), our method is much more efficient with the similar detection performance.
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Lee HW, Youngblood MW, Farooque P, Han X, Jhun S, Chen WC, Goncharova I, Vives K, Spencer DD, Zaveri H, Hirsch LJ, Blumenfeld H. Seizure localization using three-dimensional surface projections of intracranial EEG power. Neuroimage 2013; 83:616-26. [PMID: 23850575 DOI: 10.1016/j.neuroimage.2013.07.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2013] [Revised: 07/02/2013] [Accepted: 07/03/2013] [Indexed: 10/26/2022] Open
Abstract
Intracranial EEG (icEEG) provides a critical road map for epilepsy surgery but it has become increasingly difficult to interpret as technology has allowed the number of icEEG channels to grow. Borrowing methods from neuroimaging, we aimed to simplify data analysis and increase consistency between reviewers by using 3D surface projections of intracranial EEG poweR (3D-SPIER). We analyzed 139 seizures from 48 intractable epilepsy patients (28 temporal and 20 extratemporal) who had icEEG recordings, epilepsy surgery, and at least one year of post-surgical follow-up. We coregistered and plotted icEEG β frequency band signal power over time onto MRI-based surface renderings for each patient, to create color 3D-SPIER movies. Two independent reviewers interpreted the icEEG data using visual analysis vs. 3D-SPIER, blinded to any clinical information. Overall agreement rates between 3D-SPIER and icEEG visual analysis or surgery were about 90% for side of seizure onset, 80% for lobe, and just under 80% for sublobar localization. These agreement rates were improved when flexible thresholds or frequency ranges were allowed for 3D-SPIER, especially for sublobar localization. Interestingly, agreement was better for patients with good surgical outcome than for patients with poor outcome. Localization using 3D-SPIER was measurably faster and considered qualitatively easier to interpret than visual analysis. These findings suggest that 3D-SPIER could be an improved diagnostic method for presurgical seizure localization in patients with intractable epilepsy and may also be useful for mapping normal brain function.
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Affiliation(s)
- Hyang Woon Lee
- Department of Neurology, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA; Department of Neurology, Ewha Womans University School of Medicine and Ewha Medical Research Institute, 1071, Anyangcheon-ro, Yangcheon-gu, Seoul 158-710, South Korea
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Neurostimulation in the treatment of epilepsy. Exp Neurol 2013; 244:87-95. [DOI: 10.1016/j.expneurol.2013.04.004] [Citation(s) in RCA: 76] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Revised: 04/04/2013] [Accepted: 04/08/2013] [Indexed: 11/24/2022]
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