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Somaskandhan P, Leppänen T, Terrill PI, Sigurdardottir S, Arnardottir ES, Ólafsdóttir KA, Serwatko M, Sigurðardóttir SÞ, Clausen M, Töyräs J, Korkalainen H. Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls. Front Neurol 2023; 14:1162998. [PMID: 37122306 PMCID: PMC10140398 DOI: 10.3389/fneur.2023.1162998] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/23/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.
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Affiliation(s)
- Pranavan Somaskandhan
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Pranavan Somaskandhan,
| | - Timo Leppänen
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Philip I. Terrill
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
| | - Sigridur Sigurdardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Erna Sif Arnardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
- Internal Medicine Services, Landspitali–The National University Hospital of Iceland, Reykjavik, Iceland
| | - Kristín A. Ólafsdóttir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Marta Serwatko
- Department of Clinical Engineering, Landspitali University Hospital, Reykjavik, Iceland
| | - Sigurveig Þ. Sigurðardóttir
- Department of Immunology, Landspitali University Hospital, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Michael Clausen
- Department of Allergy, Landspitali University Hospital, Reykjavik, Iceland
- Children's Hospital Reykjavik, Reykjavik, Iceland
| | - Juha Töyräs
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Henri Korkalainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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Short-Term Forecasting Photovoltaic Solar Power for Home Energy Management Systems. INVENTIONS 2021. [DOI: 10.3390/inventions6010012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R2 of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.
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Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection. Biomed Eng Lett 2020; 10:333-344. [PMID: 32864172 DOI: 10.1007/s13534-020-00158-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 05/05/2020] [Accepted: 05/13/2020] [Indexed: 01/16/2023] Open
Abstract
Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.
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Duan W, Zhang J, Zhang L, Lin Z, Chen Y, Hao X, Wang Y, Zhang H. Evaluation of an artificial intelligent hydrocephalus diagnosis model based on transfer learning. Medicine (Baltimore) 2020; 99:e21229. [PMID: 32702895 PMCID: PMC7373556 DOI: 10.1097/md.0000000000021229] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
To design and develop artificial intelligence (AI) hydrocephalus (HYC) imaging diagnostic model using a transfer learning algorithm and evaluate its application in the diagnosis of HYC by non-contrast material-enhanced head computed tomographic (CT) images.A training and validation dataset of non-contrast material-enhanced head CT examinations that comprised of 1000 patients with HYC and 1000 normal people with no HYC accumulating to 28,500 images. Images were pre-processed, and the feature variables were labeled. The feature variables were extracted by the neural network for transfer learning. AI algorithm performance was tested on a separate dataset containing 250 examinations of HYC and 250 of normal. Resident, attending and consultant in the department of radiology were also tested with the test sets, their results were compared with the AI model.Final model performance for HYC showed 93.6% sensitivity (95% confidence interval: 77%, 97%) and 94.4% specificity (95% confidence interval: 79%, 98%), with area under the characteristic curve of 0.93. Accuracy rate of model, resident, attending, and consultant were 94.0%, 93.4%, 95.6%, and 97.0%.AI can effectively identify the characteristics of HYC from CT images of the brain and automatically analyze the images. In the future, AI can provide auxiliary diagnosis of image results and reduce the burden on junior doctors.
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Affiliation(s)
- Weike Duan
- Department of Neurosurgery, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang
| | - Jinsen Zhang
- Department of Neurosurgery, Huashan Hospital, Fudan University
| | - Liang Zhang
- Shanghai Nanoperception Information Technology Co. Ltd, Shanghai, P.R. China
| | - Zongsong Lin
- Shanghai Nanoperception Information Technology Co. Ltd, Shanghai, P.R. China
| | - Yuhang Chen
- Department of Neurosurgery, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang
| | - Xiaowei Hao
- Department of Neurosurgery, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang
| | - Yixin Wang
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - Hongri Zhang
- Department of Neurosurgery, the First Affiliated Hospital of Henan University of Science and Technology, Luoyang
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Computational Image Analysis of Nonenhanced Computed Tomography for Acute Ischaemic Stroke: A Systematic Review. J Stroke Cerebrovasc Dis 2020; 29:104715. [DOI: 10.1016/j.jstrokecerebrovasdis.2020.104715] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/16/2020] [Accepted: 01/28/2020] [Indexed: 11/23/2022] Open
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Lesot MJ, Vieira S, Reformat MZ, Carvalho JP, Wilbik A, Bouchon-Meunier B, Yager RR. Forecasting Electricity Consumption in Residential Buildings for Home Energy Management Systems. INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS 2020. [PMCID: PMC7274313 DOI: 10.1007/978-3-030-50146-4_24] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Prediction of the energy consumption is a key aspect of home energy management systems, whose aim is to increase the occupant’s comfort while reducing the energy consumption. This work, employing three years measured data, uses radial basis function neural networks, designed using a multi-objective genetic algorithm (MOGA) framework, for the prediction of total electric power consumption, HVAC demand and other loads demand. The prediction horizon desired is 12 h, using 15 min step ahead model, in a multi-step ahead fashion. To reduce the uncertainty, making use of the preferred set MOGA output, a model ensemble technique is proposed which achieves excellent forecast results, comparing additionally very favorably with existing approaches.
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Affiliation(s)
| | - Susana Vieira
- IDMEC, IST, Universidade de Lisboa, Lisbon, Portugal
| | | | | | - Anna Wilbik
- Eindhoven University of Technology, Eindhoven, The Netherlands
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Sarmento RM, Vasconcelos FFX, Filho PPR, Wu W, de Albuquerque VHC. Automatic Neuroimage Processing and Analysis in Stroke-A Systematic Review. IEEE Rev Biomed Eng 2019; 13:130-155. [PMID: 31449031 DOI: 10.1109/rbme.2019.2934500] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This article presents a systematic review of the current computational technologies applied to medical images for the detection, segmentation, and classification of strokes. Besides, analyzing and evaluating the technological advances, the challenges to be overcome and the future trends are discussed. The principal approaches make use of artificial intelligence, digital image processing and analysis, and various other technologies to develop computer-aided diagnosis (CAD) systems to improve the accuracy in the diagnostic process, as well as the interpretation consistency of medical images. However, there are some points that require greater attention such as low sensitivity, optimization of the algorithm, a reduction of false positives, and improvement in the identification and segmentation processes of different sizes and shapes. Also, there is a need to improve the classification steps of different stroke types and subtypes. Furthermore, there is an additional need for further research to improve the current techniques and develop new algorithms to overcome disadvantages identified here. The main focus of this research is to analyze the applied technologies for the development of CAD systems and verify how effective they are for stroke detection, segmentation, and classification. The main contributions of this review are that it analyzes only up-to-date studies, mainly from 2015 to 2018, as well as organizing the various studies in the area according to the research proposal, i.e., detection, segmentation, and classification of the types of stroke and the respective techniques used. Thus, the review has great relevance for future research, since it presents an ample comparison of the most recent works in the area, clearly showing the existing difficulties and the models that have been proposed to overcome such difficulties.
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