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Automatic detection of A-phase onsets based on convolutional neural networks. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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2
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Mutti C, Angeli MC, Rausa F, Tontini V, Pizzarotti S, Soglia M, Pollara I, Rapina C, Azzi N, Zinno L, Parrino L. Sleep macro- and micro-structure in autoimmune encephalitis: single case report from the subacute phase of the disease to the follow-up. Neurocase 2022; 28:235-238. [PMID: 35531934 DOI: 10.1080/13554794.2022.2072228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Sleep disorders are frequently described in autoimmune encephalitis (AE); however, data on sleep texture are fragmentary. We analyzed the polysomnography of a woman affected by AE, and we performed cyclic alternating pattern (CAP) scoring during the subacute phase of the disease and at follow-up. The first polysomnography showed deviations both at macro and microstructure levels, with a marked reduction of CAP rate compare to healthy sleepers (20.8% vs 33%). After 6-months sleep macrostructure improved, whilst CAP parameters remained abnormal. This is the first polysomnographic analysis, comprehensive of microstructural data, performed in AE. We briefly discuss the results.
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Affiliation(s)
- Carlotta Mutti
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Marco Cesare Angeli
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Francesco Rausa
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Valentina Tontini
- Neurology Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Silvia Pizzarotti
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Margherita Soglia
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Irene Pollara
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Clara Rapina
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Nicoletta Azzi
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
| | - Lucia Zinno
- Neurology Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Liborio Parrino
- Sleep Disorders Center, Department of General and Specialized Medicine, University Hospital of Parma, Parma, Italy
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3
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Prognosis of automated sleep staging based on two-layer ensemble learning stacking model using single-channel EEG signal. Soft comput 2021. [DOI: 10.1007/s00500-021-06218-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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4
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Sharma M, Patel V, Tiwari J, Acharya UR. Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals. Diagnostics (Basel) 2021; 11:diagnostics11081380. [PMID: 34441314 PMCID: PMC8393617 DOI: 10.3390/diagnostics11081380] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 07/26/2021] [Accepted: 07/27/2021] [Indexed: 12/03/2022] Open
Abstract
Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.
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Affiliation(s)
- Manish Sharma
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India; (V.P.); (J.T.)
- Correspondence:
| | - Virendra Patel
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India; (V.P.); (J.T.)
| | - Jainendra Tiwari
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad 380026, India; (V.P.); (J.T.)
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- School of Management and Enterprise, University of Southern Queensland, Springfield 4300, Australia
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5
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Automated detection of cyclic alternating pattern and classification of sleep stages using deep neural network. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02597-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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6
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Satapathy S, Loganathan D, Kondaveeti HK, Rath R. Performance analysis of machine learning algorithms on automated sleep staging feature sets. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2021. [DOI: 10.1049/cit2.12042] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Santosh Satapathy
- Puducherry Research Scholar of Computer Science and Engineering Pondicherry Engineering College, Puducherry India
| | - D Loganathan
- Professor of Computer Science and Engineering Pondicherry Engineering College, Puducherry Puducherry India
| | - Hari Kishan Kondaveeti
- Assistant Professor of Computer Science and Engineering VIT University, Amaravati Andhra Pradesh India
| | - RamaKrushna Rath
- Research Scholar of Computer Science and Engineering, Anna University Chennai India
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7
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Mendonça F, Mostafa SS, Morgado-Dias F, Ravelo-García AG. On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes. J Neural Eng 2020; 18. [PMID: 33271524 DOI: 10.1088/1741-2552/abd047] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 12/03/2020] [Indexed: 11/12/2022]
Abstract
The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods). It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.
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Affiliation(s)
- Fábio Mendonça
- Universidade de Lisboa Instituto Superior Tecnico, Lisboa, PORTUGAL
| | | | | | - Antonio G Ravelo-García
- Señales y Comunicaciones, Universidad de Las Palmas de Gran Canaria - Campus de Tafira, Campus de Tafira, Las Palmas de Gran Canaria, 35017, SPAIN
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Hartmann S, Baumert M. Improved A-phase Detection of Cyclic Alternating Pattern Using Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:1842-1845. [PMID: 31946256 DOI: 10.1109/embc.2019.8857006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In recent years, machine learning algorithms have become increasingly popular for analyzing biomedical signals. This includes the detection of cyclic alternating pattern (CAP) in electroencephalography recordings. Here, we investigate the performance gain of a recurrent neural network (RNN) for CAP scoring in comparison to standard classification methods. We analyzed 15 recordings (n1-n15) from the publicly available CAP Sleep Database on Physionet to evaluate each machine learning method. A long short-term memory (LSTM) network increases the accuracy and F1-score by 0.5-3.5% and 3.5-8%, respectively, compared to commonly used classification algorithms such as linear discriminant analysis, k-nearest neighbour or feed-forward neural network. Our results show that by using a LSTM classifier the quantity of correctly detected CAP events can be increased and the number of wrongly classified periods reduced. RNNs significantly improve the precision in CAP scoring by taking advantage of available information from the past for deciding current classification.
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Dhok S, Pimpalkhute V, Chandurkar A, Bhurane AA, Sharma M, Acharya UR. Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville Distribution based features. Comput Biol Med 2020; 119:103691. [PMID: 32339125 DOI: 10.1016/j.compbiomed.2020.103691] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 02/21/2020] [Accepted: 02/29/2020] [Indexed: 10/24/2022]
Abstract
Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages. In this paper, we present an automated approach for the classification of CAP phases (A and B) using Wigner-Ville Distribution (WVD) and Rényi entropy (RE) features. The WVD provides a high-resolution time-frequency analysis of the signals whereas RE provides least time-frequency uncertainty with WVD. The classification is performed using medium Gaussian kernel-based support vector machine with 10-fold cross-validation technique. We have presented the results for randomly sampled balanced data sets. The proposed approach does not require any pre-processing or post-processing stages, making it simple as compared to the existing techniques. The proposed method is able to achieve an average classification accuracy of 72.35% and 87.45% for balanced and unbalanced data sets respectively. The proposed method can aid the medical experts to analyze the cerebral stability as well as the sleep quality of a person.
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Affiliation(s)
- Shivani Dhok
- Department of Electronics and Communication, Indian Institute of Information Technology, Nagpur (IIITN), India.
| | - Varad Pimpalkhute
- Department of Electronics and Communication, Indian Institute of Information Technology, Nagpur (IIITN), India.
| | - Ambarish Chandurkar
- Department of Electronics and Communication, Indian Institute of Information Technology, Nagpur (IIITN), India.
| | - Ankit A Bhurane
- Department of Electronics and Communication, Indian Institute of Information Technology, Nagpur (IIITN), India.
| | - Manish Sharma
- Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India.
| | - U Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST) Kumamoto University, Kumamoto, Japan.
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10
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Arce-Santana ER, Alba A, Mendez MO, Arce-Guevara V. A-phase classification using convolutional neural networks. Med Biol Eng Comput 2020; 58:1003-1014. [DOI: 10.1007/s11517-020-02144-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 02/12/2020] [Indexed: 12/27/2022]
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11
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Hartmann S, Baumert M. Automatic A-Phase Detection of Cyclic Alternating Patterns in Sleep Using Dynamic Temporal Information. IEEE Trans Neural Syst Rehabil Eng 2019; 27:1695-1703. [PMID: 31425039 DOI: 10.1109/tnsre.2019.2934828] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The identification of recurrent, transient perturbations in brain activity during sleep, so called cyclic alternating patterns (CAP), is of significant interest as they have been linked to neurological pathologies. CAP sequences comprise multiple, consecutive cycles of phasic activation (A-phases). Here, we propose a novel, automated system exploiting the dynamical, temporal information in electroencephalography (EEG) recordings for the classification of A-phases and their subtypes. Using recurrent neural networks (RNN), crucial information in the temporal behavior of the EEG is extracted. The automatic classification system is equipped to deal with the biasing issue of imbalanced data sets and uses state-of-the-art signal processing methods to reduce inter-subject variation. To evaluate our system, we applied recordings from the publicly available CAP Sleep Database on Physionet. Our results show that the RNN improved the detection accuracy by 3-5% and the F1-score by approximately 7% on two data sets compared to a normal feed-forward neural network. Our system achieves a sensitivity of approximately 76-78% and F1-score between 63-68%, significantly outperforming existing technologies. Moreover, its sensitivity for subtype classification of 60-63% (A1), 42-45% (A2), and 71-74% (A3) indicates superior multi-class classification performance for CAP detection. In conclusion, we have developed a fully automated high performance CAP scoring system that includes A-phase subtype classification. RNN classifiers yield a significant improvement in accuracy and sensitivity compared to previously proposed systems.
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12
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Marshansky S, Mayer P, Rizzo D, Baltzan M, Denis R, Lavigne GJ. Sleep, chronic pain, and opioid risk for apnea. Prog Neuropsychopharmacol Biol Psychiatry 2018; 87:234-244. [PMID: 28734941 DOI: 10.1016/j.pnpbp.2017.07.014] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 07/15/2017] [Accepted: 07/15/2017] [Indexed: 01/21/2023]
Abstract
Pain is an unwelcome sleep partner. Pain tends to erode sleep quality and alter the sleep restorative process in vulnerable patients. It can contribute to next-day sleepiness and fatigue, affecting cognitive function. Chronic pain and the use of opioid medications can also complicate the management of sleep disorders such as insomnia (difficulty falling and/or staying asleep) and sleep-disordered breathing (sleep apnea). Sleep problems can be related to various types of pain, including sleep headache (hypnic headache, cluster headache, migraine) and morning headache (transient tension type secondary to sleep apnea or to sleep bruxism or tooth grinding) as well as periodic limb movements (leg and arm dysesthesia with pain). Pain and sleep management strategies should be personalized to reflect the patient's history and ongoing complaints. Understanding the pain-sleep interaction requires assessments of: i) sleep quality, ii) potential contributions to fatigue, mood, and/or wake time functioning; iii) potential concomitant sleep-disordered breathing (SDB); and more importantly; iv) opioid use, as central apnea may occur in at-risk patients. Treatments include sleep hygiene advice, cognitive behavioral therapy, physical therapy, breathing devices (continuous positive airway pressure - CPAP, or oral appliance) and medications (sleep facilitators, e.g., zolpidem; or antidepressants, e.g., trazodone, duloxetine, or neuroleptics, e.g., pregabalin). In the presence of opioid-exacerbated SDB, if the dose cannot be reduced and normal breathing restored, servo-ventilation is a promising avenue that nevertheless requires close medical supervision.
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Affiliation(s)
- Serguei Marshansky
- CIUSSS du Nord de l'Île de Montréal, Hôpital Sacré-Cœur, Québec, Canada; Hôpital Hôtel-Dieu du Centre Hospitalier de l'Université de Montréal (CHUM), Faculté de Médecine, Université de Montréal, Québec, Canada
| | - Pierre Mayer
- Hôpital Hôtel-Dieu du Centre Hospitalier de l'Université de Montréal (CHUM), Faculté de Médecine, Université de Montréal, Québec, Canada
| | - Dorrie Rizzo
- Jewish General, Université de Montréal, Montréal, Québec, Canada
| | - Marc Baltzan
- Faculty of Medicine, McGill University, Mount Sinai Hospital, Montréal, Canada
| | - Ronald Denis
- CIUSSS du Nord de l'Île de Montréal, Hôpital Sacré-Cœur, Québec, Canada
| | - Gilles J Lavigne
- CIUSSS du Nord de l'Île de Montréal, Hôpital Sacré-Cœur, Québec, Canada; Faculty of Dental Medicine, Université de Montréal, Department of Stomatology, CHUM, Montréal, Québec, Canada.
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13
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14
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Dorantes-Méndez G, Mendez MO, Alba A, Parrino L, Milioli G. Time-varying analysis of the heart rate variability during A-phases of sleep: Healthy and pathologic conditions. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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Ma Y, Shi W, Peng CK, Yang AC. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. Sleep Med Rev 2018; 37:85-93. [DOI: 10.1016/j.smrv.2017.01.003] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 12/31/2016] [Accepted: 01/19/2017] [Indexed: 10/20/2022]
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16
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Cuesta-Frau D, Miró-Martínez P, Jordán Núñez J, Oltra-Crespo S, Molina Picó A. Noisy EEG signals classification based on entropy metrics. Performance assessment using first and second generation statistics. Comput Biol Med 2017; 87:141-151. [PMID: 28595129 DOI: 10.1016/j.compbiomed.2017.05.028] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 05/05/2017] [Accepted: 05/28/2017] [Indexed: 11/19/2022]
Abstract
This paper evaluates the performance of first generation entropy metrics, featured by the well known and widely used Approximate Entropy (ApEn) and Sample Entropy (SampEn) metrics, and what can be considered an evolution from these, Fuzzy Entropy (FuzzyEn), in the Electroencephalogram (EEG) signal classification context. The study uses the commonest artifacts found in real EEGs, such as white noise, and muscular, cardiac, and ocular artifacts. Using two different sets of publicly available EEG records, and a realistic range of amplitudes for interfering artifacts, this work optimises and assesses the robustness of these metrics against artifacts in class segmentation terms probability. The results show that the qualitative behaviour of the two datasets is similar, with SampEn and FuzzyEn performing the best, and the noise and muscular artifacts are the most confounding factors. On the contrary, there is a wide variability as regards initialization parameters. The poor performance achieved by ApEn suggests that this metric should not be used in these contexts.
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Affiliation(s)
- David Cuesta-Frau
- Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain.
| | - Pau Miró-Martínez
- Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Alcoi, Spain
| | - Jorge Jordán Núñez
- Department of Statistics, Polytechnic University of Valencia, Alcoi Campus, Alcoi, Spain
| | - Sandra Oltra-Crespo
- Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain
| | - Antonio Molina Picó
- Technological Institute of Informatics, Polytechnic University of Valencia, Alcoi Campus, Plaza Ferrandiz y Carbonell 2, Alcoi, Spain
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