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Qamar HGM, Qureshi MF, Mushtaq Z, Zubariah Z, Rehman MZU, Samee NA, Mahmoud NF, Gu YH, Al-Masni MA. EMG gesture signal analysis towards diagnosis of upper limb using dual-pathway convolutional neural network. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:5712-5734. [PMID: 38872555 DOI: 10.3934/mbe.2024252] [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: 06/15/2024]
Abstract
This research introduces a novel dual-pathway convolutional neural network (DP-CNN) architecture tailored for robust performance in Log-Mel spectrogram image analysis derived from raw multichannel electromyography signals. The primary objective is to assess the effectiveness of the proposed DP-CNN architecture across three datasets (NinaPro DB1, DB2, and DB3), encompassing both able-bodied and amputee subjects. Performance metrics, including accuracy, precision, recall, and F1-score, are employed for comprehensive evaluation. The DP-CNN demonstrates notable mean accuracies of 94.93 ± 1.71% and 94.00 ± 3.65% on NinaPro DB1 and DB2 for healthy subjects, respectively. Additionally, it achieves a robust mean classification accuracy of 85.36 ± 0.82% on amputee subjects in DB3, affirming its efficacy. Comparative analysis with previous methodologies on the same datasets reveals substantial improvements of 28.33%, 26.92%, and 39.09% over the baseline for DB1, DB2, and DB3, respectively. The DP-CNN's superior performance extends to comparisons with transfer learning models for image classification, reaffirming its efficacy. Across diverse datasets involving both able-bodied and amputee subjects, the DP-CNN exhibits enhanced capabilities, holding promise for advancing myoelectric control.
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Affiliation(s)
| | - Muhammad Farrukh Qureshi
- Department of Electrical Engineering, Riphah International University, Islamabad 44000, Pakistan
| | - Zohaib Mushtaq
- Department of Electrical, Electronics and Computer Systems, College of Engineering and Technology, University of Sargodha, Sargodha 40100, Pakistan
| | - Zubariah Zubariah
- Department of Physiotherapy, Isfandyar Bukhari Civil Hospital, District Headquarter Hospital, Attock 43600, Pakistan
| | - Muhammad Zia Ur Rehman
- Department of Biomedical Engineering, Riphah International University, Islamabad 44000, Pakistan
- Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark
| | - Nagwan Abdel Samee
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Noha F Mahmoud
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Yeong Hyeon Gu
- Department of Artificial Intelligence Data Science, College of Software & Convergence Technology, Sejong University, Seoul 05006, Republic of Korea
| | - Mohammed A Al-Masni
- Department of Artificial Intelligence Data Science, College of Software & Convergence Technology, Sejong University, Seoul 05006, Republic of Korea
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Samfira IMA, Galanopoulou AS, Nariai H, Gursky JM, Moshé SL, Bardakjian BL. EEG-based spatiotemporal dynamics of fast ripple networks and hubs in infantile epileptic spasms. Epilepsia Open 2024; 9:122-137. [PMID: 37743321 PMCID: PMC10839371 DOI: 10.1002/epi4.12831] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 09/17/2023] [Indexed: 09/26/2023] Open
Abstract
OBJECTIVE Infantile epileptic spasms (IS) are epileptic seizures that are associated with increased risk for developmental impairments, adult epilepsies, and mortality. Here, we investigated coherence-based network dynamics in scalp EEG of infants with IS to identify frequency-dependent networks associated with spasms. We hypothesized that there is a network of increased fast ripple connectivity during the electrographic onset of clinical spasms, which is distinct from controls. METHODS We retrospectively analyzed peri-ictal and interictal EEG recordings of 14 IS patients. The data was compared with 9 age-matched controls. Wavelet phase coherence (WPC) was computed between 0.2 and 400 Hz. Frequency- and time-dependent brain networks were constructed using this coherence as the strength of connection between two EEG channels, based on graph theory principles. Connectivity was evaluated through global efficiency (GE) and channel-based closeness centrality (CC), over frequency and time. RESULTS GE in the fast ripple band (251-400 Hz) was significantly greater following the onset of spasms in all patients (P < 0.05). Fast ripple networks during the first 10s from spasm onset show enhanced anteroposterior gradient in connectivity (posterior > central > anterior, Kruskal-Wallis P < 0.001), with maximum CC over the centroparietal channels in 10/14 patients. Additionally, this anteroposterior gradient in CC connectivity is observed during spasms but not during the interictal awake or asleep states of infants with IS. In controls, anteroposterior gradient in fast ripple CC was noted during arousals and wakefulness but not during sleep. There was also a simultaneous decrease in GE in the 5-8 Hz range after the onset of spasms (P < 0.05), of unclear biological significance. SIGNIFICANCE We identified an anteroposterior gradient in the CC connectivity of fast ripple hubs during spasms. This anteroposterior gradient observed during spasms is similar to the anteroposterior gradient in the CC connectivity observed in wakefulness or arousals in controls, suggesting that this state change is related to arousal networks.
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Affiliation(s)
- Ioana M. A. Samfira
- Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoOntarioCanada
| | - Aristea S. Galanopoulou
- Saul R. Korey Department of Neurology and Comprehensive Einstein/Montefiore Epilepsy CenterAlbert Einstein College of MedicineBronxNew YorkUSA
- Isabelle Rapin Division of Child NeurologyAlbert Einstein College of MedicineBronxNew YorkUSA
- Dominick P. Purpura Department of NeuroscienceAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Hiroki Nariai
- Department of PediatricsUCLA Mattel Children's HospitalLos AngelesCaliforniaUSA
| | - Jonathan M. Gursky
- Saul R. Korey Department of Neurology and Comprehensive Einstein/Montefiore Epilepsy CenterAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Solomon L. Moshé
- Saul R. Korey Department of Neurology and Comprehensive Einstein/Montefiore Epilepsy CenterAlbert Einstein College of MedicineBronxNew YorkUSA
- Isabelle Rapin Division of Child NeurologyAlbert Einstein College of MedicineBronxNew YorkUSA
- Dominick P. Purpura Department of NeuroscienceAlbert Einstein College of MedicineBronxNew YorkUSA
- Department of PediatricsEinstein College of MedicineBronxNew YorkUSA
| | - Berj L. Bardakjian
- Edward S. Rogers Sr. Department of Electrical and Computer EngineeringUniversity of TorontoTorontoOntarioCanada
- Institute of Biomedical EngineeringUniversity of TorontoTorontoOntarioCanada
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Schmoigl-Tonis M, Schranz C, Müller-Putz GR. Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review. Front Hum Neurosci 2023; 17:1251690. [PMID: 37920561 PMCID: PMC10619676 DOI: 10.3389/fnhum.2023.1251690] [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: 07/02/2023] [Accepted: 09/11/2023] [Indexed: 11/04/2023] Open
Abstract
Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.
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Affiliation(s)
- Mathias Schmoigl-Tonis
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
| | - Christoph Schranz
- Laboratory of Collaborative Robotics, Department of Human Motion Analytics, Salzburg Research GmbH, Salzburg, Austria
| | - Gernot R. Müller-Putz
- Institute of Neural Engineering, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Graz, Austria
- BioTechMed Graz, Graz, Austria
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Antony MJ, Sankaralingam BP, Khan S, Almjally A, Almujally NA, Mahendran RK. Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data. Diagnostics (Basel) 2023; 13:2852. [PMID: 37685390 PMCID: PMC10486696 DOI: 10.3390/diagnostics13172852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.
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Affiliation(s)
- Mary Judith Antony
- Department of Computer Science & Engineering, Panimalar College of Engineering, Chennai 600123, India
| | - Baghavathi Priya Sankaralingam
- Department of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai 601103, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.K.); (A.A.)
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India
| | - Abrar Almjally
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.K.); (A.A.)
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Rakesh Kumar Mahendran
- Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai 602105, India;
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Bhardwaj S, Ghosh D, Dutta D, Cheduluri G, Hansigida V, Nali AR, Acharyya A. Low Complex CORDIC-based Hand Movement Recognition Design Methodology for Rehabilitation and Prosthetic Applications. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-5. [PMID: 38083540 DOI: 10.1109/embc40787.2023.10340238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Hand movement recognition using Electromyography (EMG) signals have gained much significance lately and is extensively used for rehabilitation and prosthetic applications including stroke-driven disability and other neuromuscular disorders. Herein, quantitative analysis of EMG signals is very crucial. However, such applications are constrained by power consumption limitations due to the battery backup necessitating low-complex system design and the on-chip area requirement. Existing hand movement recognition methodologies using single-channel EMG signal involve computationally intensive stages, including Ensemble Empirical Mode Decomposition (EEMD), Fast Independent Component Analysis (FastICA), feature extraction, and Linear Discriminant Analysis (LDA) classification, which can not be mapped onto the low-complex architecture directly from the algorithmic level. The high computational complexity of LDA classification makes it difficult to be used for low-complex applications. In this paper, we introduce a low-complex CORDIC-based hand movement recognition design methodology targeting resource-constrained rehabilitation applications. This work explores replacing LDA classification with K-Means clustering due to its reduced complexity and efficient clustering algorithm. CORDIC-based K-Means clustering is used to further reduce the overall computational complexity of the system. The proposed low complex, K-Means clustering-based hand movement recognition for classifying seven hand movements using single-channel EMG data is found to be 99.77 % less complex and 1.28% more accurate than the conventional LDA-based classification.
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Daud SNSS, Sudirman R. Wavelet Based Filters for Artifact Elimination in Electroencephalography Signal: A Review. Ann Biomed Eng 2022; 50:1271-1291. [PMID: 35994164 DOI: 10.1007/s10439-022-03053-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/10/2022] [Indexed: 11/26/2022]
Abstract
Electroencephalography (EEG) is a diagnostic test that records and measures the electrical activity of the human brain. Research investigating human behaviors and conditions using EEG has increased from year to year. Therefore, an efficient approach is vital to process the EEG dataset to improve the output signal quality. The wavelet is one of the well-known approaches for processing the EEG signal in time-frequency domain analysis. The wavelet is better than the traditional Fourier Transform because it has good time-frequency localized properties and multi-resolution analysis where the transient information of an EEG signal can be extracted efficiently. Thus, this review article aims to comprehensively describe the application of the wavelet method in denoising the EEG signal based on recent research. This review begins with a brief overview of the basic theory and characteristics of EEG and the wavelet transform method. Then, several wavelet-based methods commonly applied in EEG dataset denoising are described and a considerable number of the latest published EEG research works with wavelet applications are reviewed. Besides, the challenges that exist in current EEG-based wavelet method research are discussed. Finally, alternative solutions to mitigate the issues are recommended.
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Affiliation(s)
| | - Rubita Sudirman
- School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, 81310, Johor, Malaysia
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Gorjan D, Gramann K, De Pauw K, Marusic U. Removal of movement-induced EEG artifacts: current state of the art and guidelines. J Neural Eng 2022; 19. [PMID: 35147512 DOI: 10.1088/1741-2552/ac542c] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/08/2022] [Indexed: 11/12/2022]
Abstract
Electroencephalography (EEG) is a non-invasive technique used to record cortical neurons' electrical activity using electrodes placed on the scalp. It has become a promising avenue for research beyond state-of-the-art EEG research that is conducted under static conditions. EEG signals are always contaminated by artifacts and other physiological signals. Artifact contamination increases with the intensity of movement. In the last decade (since 2010), researchers have started to implement EEG measurements in dynamic setups to increase the overall ecological validity of the studies. Many different methods are used to remove non-brain activity from the EEG signal, and there are no clear guidelines on which method should be used in dynamic setups and for specific movement intensities. Currently, the most common methods for removing artifacts in movement studies are methods based on independent component analysis (ICA). However, the choice of method for artifact removal depends on the type and intensity of movement, which affects the characteristics of the artifacts and the EEG parameters of interest. When dealing with EEG under non-static conditions, special care must be taken already in the designing period of an experiment. Software and hardware solutions must be combined to achieve sufficient removal of unwanted signals from EEG measurements. We have provided recommendations for the use of each method depending on the intensity of the movement and highlighted the advantages and disadvantages of the methods. However, due to the current gap in the literature, further development and evaluation of methods for artifact removal in EEG data during locomotion is needed.
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Affiliation(s)
- Dasa Gorjan
- Science and Research Centre Koper, Garibaldijeva 1, Koper, 6000, SLOVENIA
| | - Klaus Gramann
- Technische Universität Berlin, Fasanenstr. 1, Berlin, Berlin, 10623, GERMANY
| | - Kevin De Pauw
- Vrije Universiteit Brussel, Pleinlaan 2, Brussel, Brussel, 1050, BELGIUM
| | - Uros Marusic
- Science and Research Centre Koper, Garibaldijeva 1, Koper, 6000, SLOVENIA
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8
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Dora C, Patro R, Rout S, Biswal P, Biswal B. Adaptive SSA Based Muscle Artifact Removal from Single Channel EEG Using Neural Network Regressor. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2020.08.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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9
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[Potentials and challenges of social robots in relationships with older people: a rapid review of current debates]. Z Gerontol Geriatr 2021; 55:298-304. [PMID: 34228186 PMCID: PMC9213318 DOI: 10.1007/s00391-021-01932-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 06/01/2021] [Indexed: 11/29/2022]
Abstract
Hintergrund Soziale Beziehungen sind bedeutsame Ressourcen für psychisches Wohlbefinden und physische Gesundheit. Im höheren Lebensalter treffen zunehmende Vulnerabilität und Funktionsverluste häufig auf reduzierte soziale Netzwerke. Mangelnde soziale Kontakte und fehlende Netzwerke bergen dabei psychische und physische Risiken für die Betroffenen, die durch den Einsatz sozialer Roboter möglicherweise abgemildert werden können. Fragestellung Welche Potenziale und Herausforderungen ergeben sich für ältere Menschen aus ihrer Interaktion mit sozialen Robotern? Material und Methoden Die Forschungsfrage wird mittels eines „rapid review“ beantwortet. Eine systematische Literatursuche ergab 433 unikale Treffer, aus denen n = 11 Artikel in die Analysen eingingen. Ergebnisse Potenziale sozialer Roboter bestehen in der Reduktion von Einsamkeit, Stärkung der (zwischenmenschlichen) Kommunikation und Stimmungsaufhellung bei gleichzeitiger Stressreduktion. Herausforderungen bestehen in der sozialen Einbettung der Roboter. Diese sei durch Aspekte wie Wohltätigkeit, Autonomie und Privatheit als Grundsätze zu gestalten, an denen sich Design und Einsatz von sozialen Robotern orientieren können, um einem Verlust von sozialen Beziehungen vorzubeugen. Diskussion Die Ergebnisse zeigen einen Korridor auf, der die potenzialausschöpfende Anwendung sozialer Roboter für ältere Menschen ermöglicht. Im Vordergrund steht die Analyse der Herausforderungen für den Einzelfall, da soziale Beziehungen älterer Menschen positiv sowie negativ beeinflusst werden können. Dabei orientieren sich die eingeschlossenen Artikel größtenteils am Setting Pflege. Forschung zum Einsatz sozialer Roboter bei nicht oder wenig funktionseingeschränkten Personen sollte die bestehende Literatur ergänzen. Zusatzmaterial online Zusätzliche Informationen sind in der Online-Version dieses Artikels (10.1007/s00391-021-01932-5) enthalten.
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Kalantari M. Forecasting COVID-19 pandemic using optimal singular spectrum analysis. CHAOS, SOLITONS, AND FRACTALS 2021; 142:110547. [PMID: 33311861 PMCID: PMC7719007 DOI: 10.1016/j.chaos.2020.110547] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Revised: 11/12/2020] [Accepted: 12/04/2020] [Indexed: 05/17/2023]
Abstract
Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.
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Affiliation(s)
- Mahdi Kalantari
- Department of Statistics, Payame Noor University, 19395-4697, Tehran, Iran
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Duun-Henriksen J, Baud M, Richardson MP, Cook M, Kouvas G, Heasman JM, Friedman D, Peltola J, Zibrandtsen IC, Kjaer TW. A new era in electroencephalographic monitoring? Subscalp devices for ultra-long-term recordings. Epilepsia 2020; 61:1805-1817. [PMID: 32852091 DOI: 10.1111/epi.16630] [Citation(s) in RCA: 93] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 06/16/2020] [Accepted: 07/05/2020] [Indexed: 12/21/2022]
Abstract
Inaccurate subjective seizure counting poses treatment and diagnostic challenges and thus suboptimal quality in epilepsy management. The limitations of existing hospital- and home-based monitoring solutions are motivating the development of minimally invasive, subscalp, implantable electroencephalography (EEG) systems with accompanying cloud-based software. This new generation of ultra-long-term brain monitoring systems is setting expectations for a sea change in the field of clinical epilepsy. From definitive diagnoses and reliable seizure logs to treatment optimization and presurgical seizure foci localization, the clinical need for continuous monitoring of brain electrophysiological activity in epilepsy patients is evident. This paper presents the converging solutions developed independently by researchers and organizations working at the forefront of next generation EEG monitoring. The immediate value of these devices is discussed as well as the potential drivers and hurdles to adoption. Additionally, this paper discusses what the expected value of ultra-long-term EEG data might be in the future with respect to alarms for especially focal seizures, seizure forecasting, and treatment personalization.
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Affiliation(s)
- Jonas Duun-Henriksen
- Department of Basic & Clinical Neuroscience, King's College London, London, UK.,UNEEG medical, Lynge, Denmark
| | - Maxime Baud
- Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.,Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | - Mark P Richardson
- Department of Basic & Clinical Neuroscience, King's College London, London, UK
| | - Mark Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia.,Epi-Minder, Melbourne, Victoria, Australia
| | - George Kouvas
- Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
| | | | - Daniel Friedman
- NYU Langone Comprehensive Epilepsy Center, New York, New York, USA
| | - Jukka Peltola
- Department of Neurology, Tampere University and Tampere University Hospital, Tampere, Finland
| | - Ivan C Zibrandtsen
- Center of Neurophysiology, Department of Neurology, Zealand University Hospital, Roskilde, Denmark
| | - Troels W Kjaer
- Center of Neurophysiology, Department of Neurology, Zealand University Hospital, Roskilde, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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