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Cucchi M, Gruener C, Petrauskas L, Steiner P, Tseng H, Fischer A, Penkovsky B, Matthus C, Birkholz P, Kleemann H, Leo K. Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification. SCIENCE ADVANCES 2021; 7:7/34/eabh0693. [PMID: 34407948 PMCID: PMC8373129 DOI: 10.1126/sciadv.abh0693] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Accepted: 06/28/2021] [Indexed: 05/12/2023]
Abstract
Early detection of malign patterns in patients' biological signals can save millions of lives. Despite the steady improvement of artificial intelligence-based techniques, the practical clinical application of these methods is mostly constrained to an offline evaluation of the patients' data. Previous studies have identified organic electrochemical devices as ideal candidates for biosignal monitoring. However, their use for pattern recognition in real time was never demonstrated. Here, we produce and characterize brain-inspired networks composed of organic electrochemical transistors and use them for time-series predictions and classification tasks using the reservoir computing approach. To show their potential use for biofluid monitoring and biosignal analysis, we classify four classes of arrhythmic heartbeats with an accuracy of 88%. The results of this study introduce a previously unexplored paradigm for biocompatible computational platforms and may enable development of ultralow-power consumption hardware-based artificial neural networks capable of interacting with body fluids and biological tissues.
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Affiliation(s)
- Matteo Cucchi
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany.
| | - Christopher Gruener
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Lautaro Petrauskas
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
- Chair for Circuit Design and Network Theory (CCN), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Peter Steiner
- Institute for Acoustics and Speech Communication (IAS), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Hsin Tseng
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Axel Fischer
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Bogdan Penkovsky
- National University of Kyiv-Mohyla Academy, Skovorody Str. 2, 04655 Kyiv, Ukraine
- Alysophil SAS, Bio Parc, 850 Boulevard Sebastien Brant, BP 30170 F, 67405, Illkirch CEDEX, France
| | - Christian Matthus
- Chair for Circuit Design and Network Theory (CCN), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Peter Birkholz
- Institute for Acoustics and Speech Communication (IAS), Technische Universität Dresden, Helmholtzstr. 18, 01069 Dresden, Germany
| | - Hans Kleemann
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
| | - Karl Leo
- Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Nöthnitzer Str. 61, 01187 Dresden, Germany
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da Silva Lourenço C, Tjepkema-Cloostermans MC, van Putten MJAM. Machine learning for detection of interictal epileptiform discharges. Clin Neurophysiol 2021; 132:1433-1443. [PMID: 34023625 DOI: 10.1016/j.clinph.2021.02.403] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/28/2021] [Accepted: 02/12/2021] [Indexed: 11/30/2022]
Abstract
The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG. Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Research towards automating IED detection started 45 years ago. Approaches range from mimetic methods to deep learning techniques. We review different approaches to IED detection, discussing their performance and limitations. Traditional machine learning and deep learning methods have yielded the best results so far and their application in the field is still growing. Standardization of datasets and outcome measures is necessary to compare models more objectively and decide which should be implemented in a clinical setting.
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Affiliation(s)
- Catarina da Silva Lourenço
- Department of Clinical Neurophysiology, Institute for Technical Medicine, University of Twente, Technical Medical Centre, Enschede, the Netherlands.
| | - Marleen C Tjepkema-Cloostermans
- Department of Clinical Neurophysiology, Institute for Technical Medicine, University of Twente, Technical Medical Centre, Enschede, the Netherlands; Neurocentrum, Medisch Spectrum Twente MST, Enschede, the Netherlands.
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, Institute for Technical Medicine, University of Twente, Technical Medical Centre, Enschede, the Netherlands; Neurocentrum, Medisch Spectrum Twente MST, Enschede, the Netherlands.
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Bagheri E, Jin J, Dauwels J, Cash S, Westover MB. A fast machine learning approach to facilitate the detection of interictal epileptiform discharges in the scalp electroencephalogram. J Neurosci Methods 2019; 326:108362. [PMID: 31310822 DOI: 10.1016/j.jneumeth.2019.108362] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 06/28/2019] [Accepted: 07/11/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND Finding interictal epileptiform discharges (IEDs) in the EEG is a part of diagnosing epilepsy. Automated software for annotating EEGs of patients with suspected epilepsy can therefore help with reaching a diagnosis. A large amount of data is required for training and evaluating an effective IED detection system. IEDs occur infrequently in the most patients' EEG, therefore, interictal EEG recordings contain mostly background waveforms. NEW METHOD As the first step to detect IEDs, we propose a machine learning technique eliminating most EEG background data using an ensemble of simple fast classifiers based on several EEG features. This could save computation time for an IED detection method, allowing the remaining waveforms to be classified by more computationally intensive methods. We consider several efficient features and reject background by applying thresholds on them in consecutive steps. RESULTS We applied the proposed algorithm on a dataset of 156 EEGs (93 and 63 with and without IEDs, respectively). We were able to eliminate 78% of background waveforms while retaining 97% of IEDs on our cross-validated dataset. COMPARISON WITH EXISTING METHODS We applied support vector machine, k-nearest neighbours, and random forest classifiers to detect IEDs with and without initial background rejection. Results show that rejecting background by our proposed method speeds up the overall classification by a factor ranging from 1.8 to 4.7 for the considered classifiers. CONCLUSIONS The proposed method successfully reduces computation time of an IED detection system. Therefore, it is beneficial in speeding up IED detection especially when utilizing large EEG datasets.
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Affiliation(s)
- Elham Bagheri
- Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore.
| | - Jing Jin
- Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore; Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Cambridge, MA, USA
| | - Justin Dauwels
- Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore 639798, Singapore
| | - Sydney Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Cambridge, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Cambridge, MA, USA
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KHOUMA O, DIOP I, FALL PA, NDIAYE ML, FARSSI SM, OUSSAMATOU AM, DIOUF B. Novel Classification Method of Spikes Morphology in EEG Signal Using Machine Learning. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.procs.2019.01.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Bagheri E, Dauwels J, Dean BC, Waters CG, Westover MB, Halford JJ. Interictal epileptiform discharge characteristics underlying expert interrater agreement. Clin Neurophysiol 2017; 128:1994-2005. [PMID: 28837905 PMCID: PMC5842710 DOI: 10.1016/j.clinph.2017.06.252] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2016] [Revised: 05/12/2017] [Accepted: 06/25/2017] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED. METHODS IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization. RESULTS Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11. CONCLUSIONS The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10). SIGNIFICANCE By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG.
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Affiliation(s)
- Elham Bagheri
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
| | - Justin Dauwels
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
| | - Brian C Dean
- School of Computing, Clemson University, Clemson, SC, USA.
| | - Chad G Waters
- School of Computing, Clemson University, Clemson, SC, USA.
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Jonathan J Halford
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
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Halford JJ, Schalkoff RJ, Zhou J, Benbadis SR, Tatum WO, Turner RP, Sinha SR, Fountain NB, Arain A, Pritchard PB, Kutluay E, Martz G, Edwards JC, Waters C, Dean BC. Standardized database development for EEG epileptiform transient detection: EEGnet scoring system and machine learning analysis. J Neurosci Methods 2012; 212:308-16. [PMID: 23174094 DOI: 10.1016/j.jneumeth.2012.11.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2011] [Revised: 11/06/2012] [Accepted: 11/08/2012] [Indexed: 10/27/2022]
Abstract
The routine scalp electroencephalogram (rsEEG) is the most common clinical neurophysiology procedure. The most important role of rsEEG is to detect evidence of epilepsy, in the form of epileptiform transients (ETs), also known as spike or sharp wave discharges. Due to the wide variety of morphologies of ETs and their similarity to artifacts and waves that are part of the normal background activity, the task of ET detection is difficult and mistakes are frequently made. The development of reliable computerized detection of ETs in the EEG could assist physicians in interpreting rsEEGs. We report progress in developing a standardized database for testing and training ET detection algorithms. We describe a new version of our EEGnet software system for collecting expert opinion on EEG datasets, a completely web-browser based system. We report results of EEG scoring from a group of 11 board-certified academic clinical neurophysiologists who annotated 30-s excepts from rsEEG recordings from 100 different patients. The scorers had moderate inter-scorer reliability and low to moderate intra-scorer reliability. In order to measure the optimal size of this standardized rsEEG database, we used machine learning models to classify paroxysmal EEG activity in our database into ET and non-ET classes. Based on our results, it appears that our database will need to be larger than its current size. Also, our non-parametric classifier, an artificial neural network, performed better than our parametric Bayesian classifier. Of our feature sets, the wavelet feature set proved most useful for classification.
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Affiliation(s)
- Jonathan J Halford
- Department of Neurosciences, Medical University of South Carolina, Charleston, SC, USA.
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Classification des stades de sommeil par des réseaux de neurones artificiels hiérarchiques. Ing Rech Biomed 2012. [DOI: 10.1016/j.irbm.2011.12.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Halford JJ. Computerized epileptiform transient detection in the scalp electroencephalogram: Obstacles to progress and the example of computerized ECG interpretation. Clin Neurophysiol 2009; 120:1909-1915. [PMID: 19836303 DOI: 10.1016/j.clinph.2009.08.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2009] [Revised: 08/05/2009] [Accepted: 08/09/2009] [Indexed: 11/19/2022]
Affiliation(s)
- Jonathan J Halford
- Division of Adult Neurology, Department of Neurosciences, Medical University of South Carolina, Charleston, SC 29425, USA.
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Pandey B, Mishra R. Knowledge and intelligent computing system in medicine. Comput Biol Med 2009; 39:215-30. [PMID: 19201398 DOI: 10.1016/j.compbiomed.2008.12.008] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2008] [Revised: 11/24/2008] [Accepted: 12/17/2008] [Indexed: 01/04/2023]
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10
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Evaluating Medical Decision Making Heuristics and Other Business Heuristics with Neural Networks. ACTA ACUST UNITED AC 2008. [DOI: 10.1007/978-3-540-76829-6_10] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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Exarchos TP, Tzallas AT, Fotiadis DI, Konitsiotis S, Giannopoulos S. EEG Transient Event Detection and Classification Using Association Rules. ACTA ACUST UNITED AC 2006; 10:451-7. [PMID: 16871711 DOI: 10.1109/titb.2006.872067] [Citation(s) in RCA: 51] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, a methodology for the automated detection and classification of transient events in electroencephalographic (EEG) recordings is presented. It is based on association rule mining and classifies transient events into four categories: epileptic spikes, muscle activity, eye blinking activity, and sharp alpha activity. The methodology involves four stages: 1) transient event detection; 2) clustering of transient events and feature extraction; 3) feature discretization and feature subset selection; and 4) association rule mining and classification of transient events. The methodology is evaluated using 25 EEG recordings, and the best obtained accuracy was 87.38%. The proposed approach combines high accuracy with the ability to provide interpretation for the decisions made, since it is based on a set of association rules.
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Affiliation(s)
- Themis P Exarchos
- Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Greece.
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Pellegrini A, Ubiali E, Orsato R, Schiff S, Gatta A, Castellaro A, Casagrande A, Amodio P. Electroencephalographic staging of hepatic encephalopathy by an artificial neural network and an expert system. Neurophysiol Clin 2006; 35:162-7. [PMID: 16530133 DOI: 10.1016/j.neucli.2005.12.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
AIM OF THE STUDY To provide an objective EEG assessment of hepatic encephalopathy (HE), we set up and tested an entirely automatic procedure based on an artificial neural network-expert system software (ANNESS). PATIENTS AND METHODS A training set sample of 50 EEG (group A) and a test sample of 50 EEG (group B) of 100 cirrhotic patients were considered. The EEGs had been visually classified by an expert electroencephalographer, using a modified five-degree Parsons-Simith classification of HE. The efficiency of the ANNESS, trained in group A, was tested in group B. RESULTS Both the ANNESS and the visually-based classifications were found to be correlated to liver insufficiency, as assessed by the Child-Pugh score (Spearman's coefficient rho=0.485, P<0.0001; rho=0.489, P<0.0001, respectively) and by the biochemical indexes of hepatic function (bilirubin: rho=0.31 vs. 0.27; albumin: rho=-0.13 vs. -0.18; prothrombin time rho=-0.35 vs. -0.52). The classifications were found to be correlated to each other (rho=0.84 P<0.0001, Cohen's kappa=0.55). However, the ANNESS overestimated grade 2 EEG alterations. CONCLUSION An ANNESS-based classification of EEG in HE provided data comparable with a visually-based classification, except for mild alterations (class 2) that tended to be overestimated. Further optimization of automatic EEG staging of HE is desirable, as well as a prospective clinical evaluation.
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Affiliation(s)
- A Pellegrini
- Clinica Medica 5, Università di Padova, Viale Giustiniani 2, 35128 Padova, Italy
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Pace F, Buscema M, Dominici P, Intraligi M, Baldi F, Cestari R, Passaretti S, Bianchi Porro G, Grossi E. Artificial neural networks are able to recognize gastro-oesophageal reflux disease patients solely on the basis of clinical data. Eur J Gastroenterol Hepatol 2005; 17:605-10. [PMID: 15879721 DOI: 10.1097/00042737-200506000-00003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Artificial neural networks (ANN) are modelling mechanisms that are highly flexible and adaptive to solve the non-linearity inherent in the relationship between symptoms and underlying pathology. OBJECTIVES To assess the efficacy of ANN in achieving a diagnosis of gastro-oesophageal reflux disease (GORD) using oesophagoscopy or pH-metry as a diagnostic gold standard and discriminant analysis as a statistical comparator technique in a group of patients with typical GORD symptoms and with or without GORD objective findings (e.g. a positive oesophagoscopy or a pathological oesophageal pH-metry). METHODS The sample of 159 cases (88 men, 71 women) presenting with typical symptoms of GORD, were subdivided on the basis of endoscopy and pH-metry results into two groups: GORD patients with or without oesophagitis, group 1 (N=103), and pH and endoscopy-negative patients in whom both examinations were negative, group 2 (N=56). A total of 101 different independent variables were collected: demographic information, medical history, generic health state and lifestyle, intensity and frequency of typical and atypical symptoms based on the Italian version of the Gastroesophageal Reflux Questionnaire (Mayo Clinic). The diagnosis was used as a dependent variable. Different ANN models were assessed. RESULTS Specific evolutionary algorithms selected 45 independent variables, concerning clinical and demographic features, as predictors of the diagnosis. The highest predictive performance was achieved by a 'back propagation' ANN, which was consistently 100% accurate in identifying the correct diagnosis compared with 78% obtained by traditional discriminant analysis. CONCLUSION On the basis of this preliminary work, the use of ANN seems to be a promising approach for predicting diagnosis without the need for invasive diagnostic methods in patients suffering from GORD symptoms.
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Affiliation(s)
- Fabio Pace
- Department of Gastroenterology, L. Sacco H., Milan, Italy.
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Tafaro L, Cicconetti P, Piccirillo G, Ettorre E, Marigliano V, Cacciafesta M. Is it possible to predict one-year survival in centenarians? A neural network study. Gerontology 2005; 51:199-205. [PMID: 15832048 DOI: 10.1159/000083994] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2004] [Accepted: 12/23/2004] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Human life expectancy is constantly increasing: the challenge for modern geriatric medicine is to identify the means to reach successfully extreme longevity. OBJECTIVE To determine which are the survival determinants in centenarians using a neural network. METHODS Sample of 110 centenarians living in Rome, mean age 101.6 years (SD=1.8) with a sex ratio males:females of 1:3. We administered an extensive health interview (lasting 1-2 h) to each subject. The questionnaire, carried out according to the Geriatric Multidimensional Assessment, is made up of 100 items including a comprehensive health and psychosocial assessment aimed at various topics of general health and well-being and some scales used in geriatric practice. We applied several three-layered feed-forward neural networks by mixing in different ways the most important of the 100 items. RESULTS The most predicting powered net is the one constructed with 23 variables regarding comorbidity, cardiovascular risk factors, cognitive status, mood, functional status and social interactions, which therefore are strictly related to survival in centenarians. CONCLUSION Survival in longevity is a complex biological phenomenon, which is an ideal field for using the neural network as a statistic method. The net shows us that the maintenance of social relationships even in presence of disability is of major importance for survival in the oldest old.
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Affiliation(s)
- Laura Tafaro
- Department of Aging Science, University La Sapienza, Rome, Italy.
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Guan P, Huang DS, Zhou BS. Forecasting model for the incidence of hepatitis A based on artificial neural network. World J Gastroenterol 2004; 10:3579-82. [PMID: 15534910 PMCID: PMC4611996 DOI: 10.3748/wjg.v10.i24.3579] [Citation(s) in RCA: 34] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
AIM: To study the application of artificial neural network (ANN) in forecasting the incidence of hepatitis A, which had an autoregression phenomenon.
METHODS: The data of the incidence of hepatitis A in Liaoning Province from 1981 to 2001 were obtained from Liaoning Disease Control and Prevention Center. We used the autoregressive integrated moving average (ARIMA) model of time series analysis to determine whether there was any autoregression phenomenon in the data. Then the data of the incidence were switched into [0,1] intervals as the network theoretical output. The data from 1981 to 1997 were used as the training and verifying sets and the data from 1998 to 2001 were made up into the test set. STATISTICA neural network (ST NN) was used to construct, train and simulate the artificial neural network.
RESULTS: Twenty-four networks were tested and seven were retained. The best network we found had excellent performance, its regression ratio was 0.73, and its correlation was 0.69. There were 2 input variables in the network, one was AR(1), and the other was time. The number of units in hidden layer was 3. In ARIMA time series analysis results, the best model was first order autoregression without difference and smoothness. The total sum square error of the ANN model was 9090.21, the sum square error of the training set and testing set was 8377.52 and 712.69, respectively, they were all less than that of ARIMA model. The corresponding value of ARIMA was 12291.79, 8944.95 and 3346.84, respectively. The correlation coefficient of nonlinear regression (RNL) of ANN was 0.71, while the RNL of ARIMA linear autoregression model was 0.66.
CONCLUSION: ANN is superior to conventional methods in forecasting the incidence of hepatitis A which has an autoregression phenomenon.
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Affiliation(s)
- Peng Guan
- Department of Epidemiology, School of Public Health, China Medical University, Shenyang 110001, Liaoning Province, China
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16
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Affiliation(s)
- D Hollander
- Broad Medical Research Program, The Eli and Edythe L. Broad Foundation, 10900 Wilshire Boulevard, 12th Floor, Los Angeles, CA 90024-6532, USA.
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