A new evolutionary approach for neural spike detection based on genetic algorithm
•Independently adaptive neural spike detection implementing genetic algorithm.•Improved spike detection offers lower transmission power consumption.•Potential for self-programming hardware implementation. Identification of the epileptic features in nervous signals is one of the main goals of neurosc...
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| Vydáno v: | Expert systems with applications Ročník 42; číslo 1; s. 462 - 467 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier Ltd
01.01.2015
Elsevier |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •Independently adaptive neural spike detection implementing genetic algorithm.•Improved spike detection offers lower transmission power consumption.•Potential for self-programming hardware implementation.
Identification of the epileptic features in nervous signals is one of the main goals of neuroscientists and biomedical engineers since it provides valuable information about the current and future health status of a patient. Implantable wireless neural signal recording is a powerful, newly emerging technique that has been suggested for neural signal tracking and recording. One of the main issues with this technique is the transmission of enormous amounts of data, which requires high bandwidth and high power consumption for the implanted device. Neural spike detection and spike sorting can be used to reduce the power consumption and the amount of data transmitted. Neural spike detection is a challenging technique because of the large amount of background noise that exists in the body known as low potential field signals (LPF). Existing signal processing methods make use of amplitude thresholding and artificial neural networks to recognize spike signals, but are very vulnerable to noise and require a large amount of pre-training before being useful. Nonlinear energy operators (NEO) are also used to filter spike signals from this background noise. This method requires precise selection of a particular coefficient that is currently chosen by human intervention, which is time consuming and open to human error. In this work a novel approach utilizing a genetic algorithm (GA) based on a nonlinear energy operator (NEO) is proposed. The proposed expert system uses a GA to automatically adjust the threshold level in the NEO technique to detect the spike within a noisy signal in real time. The method is able to recognize the number and the location of spike-events in a neural signal in real time. Preliminary simulations show that the genetic algorithm gives superior results to the manual selection method, and that the improvement is more pronounced in noisier signals. |
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| AbstractList | Identification of the epileptic features in nervous signals is one of the main goals of neuroscientists and biomedical engineers since it provides valuable information about the current and future health status of a patient. Implantable wireless neural signal recording is a powerful, newly emerging technique that has been suggested for neural signal tracking and recording. Neural spike detection and spike sorting can be used to reduce the power consumption and the amount of data transmitted. Neural spike detection is a challenging technique because of the large amount of background noise that exists in the body known as low potential field signals (LPF). In this work a novel approach utilizing a genetic algorithm (GA) based on a nonlinear energy operator (NEO) is proposed. Preliminary simulations show that the genetic algorithm gives superior results to the manual selection method, and that the improvement is more pronounced in noisier signals. •Independently adaptive neural spike detection implementing genetic algorithm.•Improved spike detection offers lower transmission power consumption.•Potential for self-programming hardware implementation. Identification of the epileptic features in nervous signals is one of the main goals of neuroscientists and biomedical engineers since it provides valuable information about the current and future health status of a patient. Implantable wireless neural signal recording is a powerful, newly emerging technique that has been suggested for neural signal tracking and recording. One of the main issues with this technique is the transmission of enormous amounts of data, which requires high bandwidth and high power consumption for the implanted device. Neural spike detection and spike sorting can be used to reduce the power consumption and the amount of data transmitted. Neural spike detection is a challenging technique because of the large amount of background noise that exists in the body known as low potential field signals (LPF). Existing signal processing methods make use of amplitude thresholding and artificial neural networks to recognize spike signals, but are very vulnerable to noise and require a large amount of pre-training before being useful. Nonlinear energy operators (NEO) are also used to filter spike signals from this background noise. This method requires precise selection of a particular coefficient that is currently chosen by human intervention, which is time consuming and open to human error. In this work a novel approach utilizing a genetic algorithm (GA) based on a nonlinear energy operator (NEO) is proposed. The proposed expert system uses a GA to automatically adjust the threshold level in the NEO technique to detect the spike within a noisy signal in real time. The method is able to recognize the number and the location of spike-events in a neural signal in real time. Preliminary simulations show that the genetic algorithm gives superior results to the manual selection method, and that the improvement is more pronounced in noisier signals. |
| Author | Zarifia, Mohammad Hossein Ghalehjogh, Negar Karimi Baradaran-nia, Mehdi |
| Author_xml | – sequence: 1 givenname: Mohammad Hossein surname: Zarifia fullname: Zarifia, Mohammad Hossein email: mohammad.h.zarifi@gmail.com organization: Department of Computer Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran – sequence: 2 givenname: Negar Karimi surname: Ghalehjogh fullname: Ghalehjogh, Negar Karimi organization: Department of Computer Engineering, East Azerbaijan Science and Research Branch, Islamic Azad University, Tabriz, Iran – sequence: 3 givenname: Mehdi surname: Baradaran-nia fullname: Baradaran-nia, Mehdi organization: Control Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran |
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| Cites_doi | 10.1016/j.jneumeth.2006.06.019 10.1109/ISCAS.2009.5118217 10.1016/j.jneumeth.2009.08.017 10.1109/ISDA.2009.140 10.1109/TBME.2011.2174992 10.1109/10.661266 10.1109/BIBMW.2011.6112443 10.1002/cta.643 10.1109/MECBME.2011.5752055 10.1016/j.compbiomed.2011.05.007 10.1109/TBME.2004.839800 10.1109/IEMBS.2003.1280856 10.1016/j.neucom.2011.10.016 10.1016/S1388-2457(02)00297-3 10.1109/10.568916 |
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| Keywords | Neuronal signal NEO Neural spike detection Genetic algorithm Current information Energy consumption Tracking Evolutionary algorithm Epilepsy Expert system Central nervous system Bandwidth Selection criterion Localization Neurological disorder Biomedical engineering Threshold Spike Implant Health Neural network Real time Filter Power device Tracking(movable target) Signal processing Field potential Wireless network Signal to noise ratio |
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| SubjectTerms | Applied sciences Background noise Biological and medical sciences Central nervous system Computer science; control theory; systems Computer simulation Computer systems and distributed systems. User interface Data processing. List processing. Character string processing Electrophysiology Evolutionary Exact sciences and technology Fundamental and applied biological sciences. Psychology Genetic algorithm Genetic algorithms Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy Manuals Medical sciences Memory organisation. Data processing NEO Nervous system (semeiology, syndromes) Neural spike detection Neurology Neuronal signal Patients Recording Software Spikes Vertebrates: nervous system and sense organs |
| Title | A new evolutionary approach for neural spike detection based on genetic algorithm |
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