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Debelo BS, Thamineni BL, Dasari HK, Dawud AA. Detection and Severity Identification of Neonatal Seizure Using Deep Convolutional Neural Networks from Multichannel EEG Signal. Pediatric Health Med Ther 2023; 14:405-417. [PMID: 37933303 PMCID: PMC10625745 DOI: 10.2147/phmt.s427773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 10/26/2023] [Indexed: 11/08/2023] Open
Abstract
Introduction One of the most frequent neurological conditions in newborns is neonatal seizures, which may indicate severe neurological dysfunction. These seizures may have very subtle or very modest clinical indications because patterns like oscillatory (spike) trains begin with relatively low amplitude and gradually increase over time. This becomes very challenging and erroneous if clinical observation is the primary basis for identifying newborn seizures. In this study, a diagnosis system using deep convolutional neural networks is proposed to determine and classify the severity level of neonatal seizures using multichannel neonatal EEG data. Methods Datasets from publicly accessible online sources were used to compile clinical multichannel EEG datasets. Various preprocessing steps were taken, including the conversion of 2D time series data to equivalent waveform pictures. The proposed models have undergone training, and evaluations of their performance were conducted. Results The proposed CNN was used to perform binary classification with an accuracy of 92.6%, F1-score of 92.7%, specificity of 92.8%, and precision of 92.6%. To detect newborn seizures, this model is utilized. Using the proposed CNN model, multiclassification was performed with accuracy rates of 88.6%, specificity rates of 92.18%, F1-score rates of 85.61%, and precision rates of 88.9%. The results demonstrated that the suggested strategy can assist medical professionals in making accurate diagnoses close to healthcare institutions. Conclusion The developed system was capable of detecting neonatal seizures and has the potential to be used as a decision-making tool in resource-limited areas with a scarcity of expert neurologists.
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Affiliation(s)
- Biniam Seifu Debelo
- Department of Biomedical Engineering, Nigist Eleni Mohamed Memorial Compressive Specialized Hospital, Wachamo University, Hosanna, Ethiopia
| | | | - Hanumesh Kumar Dasari
- Department of Electronics and Communication, Rayalaseema University, Kurnool, AP, India
| | - Ahmed Ali Dawud
- School of Biomedical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia
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2
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Torakis I, Antonakakis M, Bei ES, Gikas P, Sakkalis V, Zervakis M. Design of a Multi-Feature Classification Scheme for Infant Epileptic Seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083337 DOI: 10.1109/embc40787.2023.10341164] [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
Neonatal epileptic seizures take place in the early childhood years, accounting for a severe condition with several deaths and neurological problems in newborn neonates. Despite the early advancements on the diagnosis and/or treatment of this condition, as a major difficulty accounts the inability of the physicians to identify and characterize a seizure, as one a small percentage gets detected in neonatal intensive care units (NICU). An important step towards any kind of seizure classification is the detection and reduction of non-cerebral activity. Towards this direction, our multi-feature approach contains spectral and statistical characteristics of EEG signals of 79 infants with suspicion of seizure and assesses the performance of two classification algorithms iteratively. The trained models (Support Vector Machine (SVM) and Random Forest classifiers) yielded high classification performance (>80% and >85% respectively). A robust neonatal seizure classification scheme is thus proposed, along with nine high scoring spectrum and statistical features. The importance of embedding an artefact reduction approach is also discussed, since the complex artifacts spread throughout the signals have great impact on the accuracy of the algorithms. The nine extracted high scoring spectral and statistical features might be used as potential biomarkers for neonatal seizure prediction in a clinical setting.
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Abstract
Clinical Decision Support (CDS) tools help the healthcare team diagnose, monitor, and treat patients more efficiently and consistently by executing clinical practice guidelines and recommendations. As a result, CDS has a direct impact on the delivery and healthcare outcomes. This review covers the fundamental concepts, as well as the infrastructure needed to create a CDS tool and examples of its use in the neonatal setting. This article also serves as a primer on what to think about when proposing the development of a new CDS tool, or when upgrading an existing one. We also highlight important elements that influence CDS development, such as informatics methodologies, data and device interoperability, and regulation.
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Affiliation(s)
- Anoop Rao
- Stanford University School of Medicine, Center for Academic Medicine, # 434A, 453 Quarry Rd, Palo Alto, CA, 94304, USA.
| | - Jonathan Palma
- Orlando Health Winnie Palmer Hospital for Women and Babies, 83 W Miller St, Orlando, FL, 32806, USA.
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4
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Zhao IY, Ma YX, Yu MWC, Liu J, Dong WN, Pang Q, Lu XQ, Molassiotis A, Holroyd E, Wong CWW. Ethics, Integrity, and Retributions of Digital Detection Surveillance Systems for Infectious Diseases: Systematic Literature Review. J Med Internet Res 2021; 23:e32328. [PMID: 34543228 PMCID: PMC8530254 DOI: 10.2196/32328] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/14/2021] [Accepted: 09/14/2021] [Indexed: 01/27/2023] Open
Abstract
Background The COVID-19 pandemic has increased the importance of the deployment of digital detection surveillance systems to support early warning and monitoring of infectious diseases. These opportunities create a “double-edge sword,” as the ethical governance of such approaches often lags behind technological achievements. Objective The aim was to investigate ethical issues identified from utilizing artificial intelligence–augmented surveillance or early warning systems to monitor and detect common or novel infectious disease outbreaks. Methods In a number of databases, we searched relevant articles that addressed ethical issues of using artificial intelligence, digital surveillance systems, early warning systems, and/or big data analytics technology for detecting, monitoring, or tracing infectious diseases according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and further identified and analyzed them with a theoretical framework. Results This systematic review identified 29 articles presented in 6 major themes clustered under individual, organizational, and societal levels, including awareness of implementing digital surveillance, digital integrity, trust, privacy and confidentiality, civil rights, and governance. While these measures were understandable during a pandemic, the public had concerns about receiving inadequate information; unclear governance frameworks; and lack of privacy protection, data integrity, and autonomy when utilizing infectious disease digital surveillance. The barriers to engagement could widen existing health care disparities or digital divides by underrepresenting vulnerable and at-risk populations, and patients’ highly sensitive data, such as their movements and contacts, could be exposed to outside sources, impinging significantly upon basic human and civil rights. Conclusions Our findings inform ethical considerations for service delivery models for medical practitioners and policymakers involved in the use of digital surveillance for infectious disease spread, and provide a basis for a global governance structure. Trial Registration PROSPERO CRD42021259180; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=259180
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Affiliation(s)
- Ivy Y Zhao
- WHO Collaborating Centre for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Ye Xuan Ma
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Man Wai Cecilia Yu
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jia Liu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wei Nan Dong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Qin Pang
- Department of Information Technology, University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - Xiao Qin Lu
- School of General Practice and Continuing Education, Capital Medical University, Beijing, China
| | - Alex Molassiotis
- WHO Collaborating Centre for Community Health Services, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Eleanor Holroyd
- School of Clinical Sciences, Auckland University of Technology, Auckland, New Zealand
| | - Chi Wai William Wong
- Department of Family Medicine and Primary Care, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.,Department of Family Medicine and Primary Care, University of Hong Kong-Shenzhen Hospital, Shenzhen, China
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5
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Montazeri S, Pinchefsky E, Tse I, Marchi V, Kohonen J, Kauppila M, Airaksinen M, Tapani K, Nevalainen P, Hahn C, Tam EWY, Stevenson NJ, Vanhatalo S. Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization. Front Hum Neurosci 2021; 15:675154. [PMID: 34135744 PMCID: PMC8200402 DOI: 10.3389/fnhum.2021.675154] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/30/2021] [Indexed: 11/13/2022] Open
Abstract
Neonatal brain monitoring in the neonatal intensive care units (NICU) requires a continuous review of the spontaneous cortical activity, i.e., the electroencephalograph (EEG) background activity. This needs development of bedside methods for an automated assessment of the EEG background activity. In this paper, we present development of the key components of a neonatal EEG background classifier, starting from the visual background scoring to classifier design, and finally to possible bedside visualization of the classifier results. A dataset with 13,200 5-minute EEG epochs (8-16 channels) from 27 infants with birth asphyxia was used for classifier training after scoring by two independent experts. We tested three classifier designs based on 98 computational features, and their performance was assessed with respect to scoring system, pre- and post-processing of labels and outputs, choice of channels, and visualization in monitor displays. The optimal solution achieved an overall classification accuracy of 97% with a range across subjects of 81-100%. We identified a set of 23 features that make the classifier highly robust to the choice of channels and missing data due to artefact rejection. Our results showed that an automated bedside classifier of EEG background is achievable, and we publish the full classifier algorithm to allow further clinical replication and validation studies.
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Affiliation(s)
- Saeed Montazeri
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Elana Pinchefsky
- Division of Neurology, Department of Paediatrics, Sainte-Justine University Hospital Centre, University of Montreal, Montreal, QC, Canada
| | - Ilse Tse
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Viviana Marchi
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Developmental Neuroscience, Stella Maris Scientific Institute, IRCCS Fondazione Stella Maris Foundation, Pisa, Italy
| | - Jukka Kohonen
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Minna Kauppila
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Manu Airaksinen
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland
| | - Karoliina Tapani
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Päivi Nevalainen
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Cecil Hahn
- Department of Paediatrics (Neurology), The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Emily W. Y. Tam
- Department of Paediatrics (Neurology), The Hospital for Sick Children and University of Toronto, Toronto, ON, Canada
| | - Nathan J. Stevenson
- Brain Modelling Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Centre, Department of Clinical Neurophysiology, Children’s Hospital and HUS Diagnostic Center, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
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6
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Elakkiya R. Machine learning based intelligent automated neonatal epileptic seizure detection. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-200800] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Epilepsy is found to be the fourth most common chronic neurological disorder that tends to abnormal and unpredictable brain activity and seizure states. According to statistics, 70% of the epilepsy patients can be cured if identified and treated with anti-epileptic drugs or shock stimulations. Only about 7% to 8% need to be operated. Electroencephalogram (EEG) is a cheap and effective way to record the prolonged activities of the brain through electrical impulses between neural cells. Seizure is difficult to detect in neonates as the signal involves a lot of disturbances and the existing high accuracy system for adults can’t be used for neonates. In an attempt to build an impregnable system to detect seizure in early stages, EEG signals of neonates procured from Neonatal Intensive Care Unit (NICU) at the Helsinki University Hospital. These signals were processed and fed into three different robust algorithms –Support Vector Machine (SVM), Artificial Neural Network (ANN) and 1-Dimensional Convolutional Neural Network (1D-CNN). The experimental results were compared and the proposed CNN model with 95.99% accuracy outperforms all the state-of-art models for automated Epileptic Seizure prediction in Neonates. Deep CNN has been a powerful tool in extracting robust features from EEG signals. This generalized system can be used by medical experts for detecting Seizure in neonates with better accuracy and reliability.
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Affiliation(s)
- R. Elakkiya
- School of Computing, Center for Information Super Highways, SASTRA Deemed University, Thanjavur, Tamilnadu, India
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7
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An EEG-based methodology for the estimation of functional brain connectivity networks: Application to the analysis of newborn EEG seizure. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102229] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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8
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Isaev DY, Tchapyjnikov D, Cotten CM, Tanaka D, Martinez N, Bertran M, Sapiro G, Carlson D. Attention-Based Network for Weak Labels in Neonatal Seizure Detection. PROCEEDINGS OF MACHINE LEARNING RESEARCH 2020; 126:479-507. [PMID: 32995751 PMCID: PMC7521836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/22/2023]
Abstract
Seizures are a common emergency in the neonatal intesive care unit (NICU) among newborns receiving therapeutic hypothermia for hypoxic ischemic encephalopathy. The high incidence of seizures in this patient population necessitates continuous electroencephalographic (EEG) monitoring to detect and treat them. Due to EEG recordings being reviewed intermittently throughout the day, inevitable delays to seizure identification and treatment arise. In recent years, work on neonatal seizure detection using deep learning algorithms has started gaining momentum. These algorithms face numerous challenges: first, the training data for such algorithms comes from individual patients, each with varying levels of label imbalance since the seizure burden in NICU patients differs by several orders of magnitude. Second, seizures in neonates are usually localized in a subset of EEG channels, and performing annotations per channel is very time-consuming. Hence models which make use of labels only per time periods, and not per channels, are preferable. In this work we assess how different deep learning models and data balancing methods influence learning in neonatal seizure detection in EEGs. We propose a model which provides a level of importance to each of the EEG channels - a proxy to whether a channel exhibits seizure activity or not, and we provide a quantitative assessment of how well this mechanism works. The model is portable to EEG devices with differing layouts without retraining, facilitating its potential deployment across different medical centers. We also provide a first assessment of how a deep learning model for neonatal seizure detection agrees with human rater decisions - an important milestone for deployment to clinical practice. We show that high AUC values in a deep learning model do not necessarily correspond to agreement with a human expert, and there is still a need to further refine such algorithms for optimal seizure discrimination.
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Affiliation(s)
- Dmitry Yu Isaev
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Dmitry Tchapyjnikov
- Department of Pediatrics, Department of Neurology, Duke University, Durham, NC, USA
| | | | - David Tanaka
- Department of Pediatrics, Duke University, Durham, NC, USA
| | - Natalia Martinez
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Martin Bertran
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - Guillermo Sapiro
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA
| | - David Carlson
- Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA
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9
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O’Shea A, Lightbody G, Boylan G, Temko A. Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture. Neural Netw 2020; 123:12-25. [DOI: 10.1016/j.neunet.2019.11.023] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 09/26/2019] [Accepted: 11/25/2019] [Indexed: 10/25/2022]
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10
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Abbasi H, Unsworth CP. Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram. Neural Regen Res 2020; 15:222-231. [PMID: 31552887 PMCID: PMC6905345 DOI: 10.4103/1673-5374.265542] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 05/24/2019] [Indexed: 01/15/2023] Open
Abstract
Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.
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Affiliation(s)
- Hamid Abbasi
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
| | - Charles P. Unsworth
- Department of Engineering Science, The University of Auckland, Auckland, New Zealand
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11
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O'Sullivan M, Gomez S, O'Shea A, Salgado E, Huillca K, Mathieson S, Boylan G, Popovici E, Temko A. Neonatal EEG Interpretation and Decision Support Framework for Mobile Platforms. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:4881-4884. [PMID: 30441437 DOI: 10.1109/embc.2018.8513231] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper proposes and implements an intuitive and pervasive solution for neonatal EEG monitoring assisted by sonification and deep learning AI that provides information about neonatal brain health to all neonatal healthcare professionals, particularly those without EEG interpretation expertise. The system aims to increase the demographic of clinicians capable of diagnosing abnormalities in neonatal EEG. The proposed system uses a low-cost and low-power EEG acquisition system. An Android app provides single-channel EEG visualization, traffic-light indication of the presence of neonatal seizures provided by a trained, deep convolutional neural network and an algorithm for EEG sonification, designed to facilitate the perception of changes in EEG morphology specific to neonatal seizures. The multifaceted EEG interpretation framework is presented and the implemented mobile platform architecture is analyzed with respect to its power consumption and accuracy.
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12
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Frassineti L, Barba C, Melani F, Piras F, Guerrini R, Manfredi C. Automatic detection and sonification of nonmotor generalized onset epileptic seizures: Preliminary results. Brain Res 2019; 1721:146341. [DOI: 10.1016/j.brainres.2019.146341] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 06/04/2019] [Accepted: 07/17/2019] [Indexed: 10/26/2022]
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13
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Vellido A, Ribas V, Morales C, Ruiz Sanmartín A, Ruiz Rodríguez JC. Machine learning in critical care: state-of-the-art and a sepsis case study. Biomed Eng Online 2018; 17:135. [PMID: 30458795 PMCID: PMC6245501 DOI: 10.1186/s12938-018-0569-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments. RESULTS The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU. CONCLUSION We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.
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Affiliation(s)
- Alfredo Vellido
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya, C. Jordi Girona, 1-3, 08034, Barcelona, Spain. .,Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain.
| | - Vicent Ribas
- Data Analytics in Medicine, EureCat, Avinguda Diagonal, 177, 08018, Barcelona, Spain
| | - Carles Morales
- Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, Universitat Politècnica de Catalunya, C. Jordi Girona, 1-3, 08034, Barcelona, Spain
| | - Adolfo Ruiz Sanmartín
- Critical Care Deparment, Vall d'Hebron University Hospital. Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d' Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08035, Barcelona, Spain
| | - Juan Carlos Ruiz Rodríguez
- Critical Care Deparment, Vall d'Hebron University Hospital. Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d' Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08035, Barcelona, Spain
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Poveda J, O'Sullivan M, Popovici E, Temko A. Portable neonatal EEG monitoring and sonification on an Android device. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:2018-2021. [PMID: 29060292 DOI: 10.1109/embc.2017.8037248] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Clinical evaluation of electroencephalogram (EEG) is important for understanding and monitoring the electrical activity present in the brain. In collusion with engineering advances, the movement towards portable, rapid and low-cost EEG monitoring is growing. This will allow a greater availability of monitoring technologies for assessing brain function and health in disadvantaged communities. This paper presents an alternative method for interpreting neonatal brain health in real-time via the sonification of EEG on a smartphone. The paper discusses the implementation of the real-time EEG sonification using a phase vocoder and shows how the method is achievable using low-cost smartphone technologies with energy efficient algorithms.
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15
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Neonatal intensive care decision support systems using artificial intelligence techniques: a systematic review. Artif Intell Rev 2018. [DOI: 10.1007/s10462-018-9635-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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16
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Can a collaborative healthcare network improve the care of people with epilepsy? Epilepsy Behav 2018; 82:189-193. [PMID: 29573986 DOI: 10.1016/j.yebeh.2018.02.018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Accepted: 02/16/2018] [Indexed: 01/31/2023]
Abstract
New opportunities are now available to improve care in ways not possible previously. Information contained in electronic medical records can now be shared without identifying patients. With network collaboration, large numbers of medical records can be searched to identify patients most like the one whose complex medical situation challenges the physician. The clinical effectiveness of different treatment strategies can be assessed rapidly to help the clinician decide on the best treatment for this patient. Other capabilities from different components of the network can prompt the recognition of what is the best available option and encourage the sharing of information about programs and electronic tools. Difficulties related to privacy, harmonization, integration, and costs are expected, but these are currently being addressed successfully by groups of organizations led by those who recognize the benefits.
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Temko A. Estimation of heart rate from photoplethysmography during physical exercise using Wiener filtering and the phase vocoder. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:1500-3. [PMID: 26736555 DOI: 10.1109/embc.2015.7318655] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
A system for estimation of the heart rate (HR) from the photoplethysmographic (PPG) signal during intensive physical exercises is presented. The Wiener filter is used to attenuate the noise introduced by the motion artifacts in the PPG signals. The frequency with the highest magnitude estimated using Fourier transformation is selected from the resultant de-noised signal. The phase vocoder technique is exploited to refine the frequency estimate, from which the HR in beats per minute (BPM) is finally calculated. On a publically available database of twenty three PPG recordings, the proposed technique obtains an error of 2.28 BPM. A relative error rate reduction of 18% is obtained when comparing with the state-of-the art PPG-based HR estimation methods. The proposed system is shown to be robust to strong motion artifact, produces high accuracy results and has very few free parameters, in contrast to other available approaches. The algorithm has low computational cost and can be used for fitness tracking and health monitoring in wearable devices.
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Korshunova I, Kindermans PJ, Degrave J, Verhoeven T, Brinkmann BH, Dambre J. Towards Improved Design and Evaluation of Epileptic Seizure Predictors. IEEE Trans Biomed Eng 2018; 65:502-510. [DOI: 10.1109/tbme.2017.2700086] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17:279-288. [DOI: 10.1016/s1474-4422(18)30038-3] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 12/05/2017] [Accepted: 12/06/2017] [Indexed: 12/24/2022]
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Malak JS, Safdari R, Zeraati H, Nayeri FS, Mohammadzadeh N, Farajollah SSS. An agent based architecture for high-risk neonate management at neonatal intensive care unit. Electron Physician 2018; 10:6193-6200. [PMID: 29588819 PMCID: PMC5853993 DOI: 10.19082/6193] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 12/14/2017] [Indexed: 01/15/2023] Open
Abstract
Background In recent years, the use of new tools and technologies has decreased the neonatal mortality rate. Despite the positive effect of using these technologies, the decisions are complex and uncertain in critical conditions when the neonate is preterm or has a low birth weight or malformations. There is a need to automate the high-risk neonate management process by creating real-time and more precise decision support tools. Objective To create a collaborative and real-time environment to manage neonates with critical conditions at the NICU (Neonatal Intensive Care Unit) and to overcome high-risk neonate management weaknesses by applying a multi agent based analysis and design methodology as a new solution for NICU management. Methods This study was a basic research for medical informatics method development that was carried out in 2017. The requirement analysis was done by reviewing articles on NICU Decision Support Systems. PubMed, Science Direct, and IEEE databases were searched. Only English articles published after 1990 were included; also, a needs assessment was done by reviewing the extracted features and current processes at the NICU environment where the research was conducted. We analyzed the requirements and identified the main system roles (agents) and interactions by a comparative study of existing NICU decision support systems. The Universal Multi Agent Platform (UMAP) was applied to implement a prototype of our multi agent based high-risk neonate management architecture. Results Local environment agents interacted inside a container and each container interacted with external resources, including other NICU systems and consultation centers. In the NICU container, the main identified agents were reception, monitoring, NICU registry, and outcome prediction, which interacted with human agents including nurses and physicians. Conclusion Managing patients at the NICU units requires online data collection, real-time collaboration, and management of many components. Multi agent systems are applied as a well-known solution for management, coordination, modeling, and control of NICU processes. We are currently working on an outcome prediction module using artificial intelligence techniques for neonatal mortality risk prediction. The full implementation of the proposed architecture and evaluation is considered the future work.
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Affiliation(s)
- Jaleh Shoshtarian Malak
- Ph.D. Candidate of Medical Informatics, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran
| | - Reza Safdari
- Ph.D. of Health Information Management, Professor, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran
| | - Hojjat Zeraati
- Ph.D. of Biostatistics, Professor, Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Science, Tehran, Iran
| | - Fatemeh Sadat Nayeri
- Ph.D. of Neonatology, Professor, Maternal-Fetal & Neonatal Research Center, Tehran University of Medical Science, Tehran, Iran
| | - Niloofar Mohammadzadeh
- Ph.D. of Health Information Management, Assistant Professor, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran
| | - Seide Sedighe Seied Farajollah
- Ph.D. Candidate of Health Information Management, Department of Medical Information Management, Faculty of Allied Sciences, Tehran University of Medical Science, Tehran, Iran
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Temko A, Sarkar AK, Boylan GB, Mathieson S, Marnane WP, Lightbody G. Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2017; 5:2800414. [PMID: 29021923 PMCID: PMC5633333 DOI: 10.1109/jtehm.2017.2737992] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2016] [Revised: 05/19/2017] [Accepted: 07/30/2017] [Indexed: 11/09/2022]
Abstract
The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.
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Affiliation(s)
- Andriy Temko
- Department of Electrical and Electronic Engineering and Irish Centre for Fetal and Neonatal Translational ResearchUniversity College CorkT12 P2FYCorkIreland
| | | | - Geraldine B. Boylan
- Department of Paediatrics and Child Health and INFANT CenterUniversity College CorkT12 P2FYCorkIreland
| | - Sean Mathieson
- Academic Research Department of NeonatologyInstitute for Women’s Health, University College LondonLondonWC1E 6AUU.K.
| | - William P. Marnane
- Department of Electrical and Electronic Engineering and Irish Centre for Fetal and Neonatal Translational ResearchUniversity College CorkT12 P2FYCorkIreland
| | - Gordon Lightbody
- Department of Electrical and Electronic Engineering and Irish Centre for Fetal and Neonatal Translational ResearchUniversity College CorkT12 P2FYCorkIreland
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Abstract
OBJECTIVE The challenging task of heart rate (HR) estimation from the photoplethysmographic (PPG) signal, during intensive physical exercises, is tackled in this paper. METHODS The study presents a detailed analysis of a novel algorithm (WFPV) that exploits a Wiener filter to attenuate the motion artifacts, a phase vocoder to refine the HR estimate and user-adaptive post-processing to track the subject physiology. Additionally, an offline version of the HR estimation algorithm that uses Viterbi decoding is designed for scenarios that do not require online HR monitoring (WFPV+VD). The performance of the HR estimation systems is rigorously compared with existing algorithms on the publically available database of 23 PPG recordings. RESULTS On the whole dataset of 23 PPG recordings, the algorithms result in average absolute errors of 1.97 and 1.37 BPM in the online and offline modes, respectively. On the test dataset of 10 PPG recordings which were most corrupted with motion artifacts, WFPV has an error of 2.95 BPM on its own and 2.32 BPM in an ensemble with two existing algorithms. CONCLUSION The error rate is significantly reduced when compared with the state-of-the art PPG-based HR estimation methods. SIGNIFICANCE The proposed system is shown to be accurate in the presence of strong motion artifacts and in contrast to existing alternatives has very few free parameters to tune. The algorithm has a low computational cost and can be used for fitness tracking and health monitoring in wearable devices. The MATLAB implementation of the algorithm is provided online.
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Cichocki A. Epileptic EEG visualization and sonification based on linear discriminate analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:4466-9. [PMID: 26737286 DOI: 10.1109/embc.2015.7319386] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, we first presents a high accuracy epileptic electroencephalogram (EEG) classification algorithm. EEG data of epilepsy patients are preprocessed, segmented, and decomposed to intrinsic mode functions, from which features are extracted. Two classifiers are trained based on linear discriminant analysis (LDA) to classify EEG data into three types, i.e., normal, spike, and seizure. We further in-depth investigate the changes of the decision values in LDA on continuous EEG data. An epileptic EEG visualization and sonification algorithm is proposed to provide both temporal and spatial information of spike and seizure of epilepsy patients. In the experiment, EEG data of six subjects (two normal and four seizure patients) are included. The experiment result shows the proposed epileptic EEG classification algorithm achieves high accuracy. As well, the visualization and sonification algorithm exhibits a great help in nursing seizure patients and localizing the area of seizures.
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Ahmed R, Temko A, Marnane W, Lightbody G, Boylan G. Grading hypoxic–ischemic encephalopathy severity in neonatal EEG using GMM supervectors and the support vector machine. Clin Neurophysiol 2016; 127:297-309. [DOI: 10.1016/j.clinph.2015.05.024] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Revised: 04/17/2015] [Accepted: 05/20/2015] [Indexed: 10/23/2022]
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Shen CP, Lin JW, Lin FS, Lam AYY, Chen W, Zhou W, Sung HY, Kao YH, Chiu MJ, Leu FY, Lai F. GA-SVM modeling of multiclass seizure detector in epilepsy analysis system using cloud computing. Soft comput 2015. [DOI: 10.1007/s00500-015-1917-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Temko A, Doyle O, Murray D, Lightbody G, Boylan G, Marnane W. Multimodal predictor of neurodevelopmental outcome in newborns with hypoxic-ischaemic encephalopathy. Comput Biol Med 2015; 63:169-77. [DOI: 10.1016/j.compbiomed.2015.05.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 05/22/2015] [Accepted: 05/23/2015] [Indexed: 11/28/2022]
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