Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745523
M. Vizcardo, J. Jiménez, F. Moleiro, A. Marcano, A. Octavio, A. Rodriguez
According to the World wide Organization of the Health, the number of people infected with the Tri-panosoma Cruzi is considered between 16 and 18 million, causal agent of the Chagaspsila disease, and in 100 million the people exposed to the affectation risk. When concluding in 1983 a study longitudinal epidemiologist in patients with the disease evaluated every 3 years, the cardiac affectation: chronic Chagasic myocarditis (MCHC) increased from a 17% at the beginning of the study to a 49, 4% after 15 years. Previous studies of the variability of cardiac frequency in patients with the Chagaspsila disease, show alterations in the spectral indices of the HRV. We analyze the 24-hour heart rate by Holter recordings in 62 patients with ECG alterations (CH2), 32 patients without ECG alterations (CH1) who had positive serological findings for disease of Chagaspsila and 36 healthy subjects (control) matched for sex and age. We find a orthogonal base that is able to discriminate the groups from circadian profiles, control and CH2, and stratify the groups CH1.
{"title":"Analysis of the heart rate variability and stratification of the risk of cardiac patients with Chagas’ disease","authors":"M. Vizcardo, J. Jiménez, F. Moleiro, A. Marcano, A. Octavio, A. Rodriguez","doi":"10.1109/CIC.2007.4745523","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745523","url":null,"abstract":"According to the World wide Organization of the Health, the number of people infected with the Tri-panosoma Cruzi is considered between 16 and 18 million, causal agent of the Chagaspsila disease, and in 100 million the people exposed to the affectation risk. When concluding in 1983 a study longitudinal epidemiologist in patients with the disease evaluated every 3 years, the cardiac affectation: chronic Chagasic myocarditis (MCHC) increased from a 17% at the beginning of the study to a 49, 4% after 15 years. Previous studies of the variability of cardiac frequency in patients with the Chagaspsila disease, show alterations in the spectral indices of the HRV. We analyze the 24-hour heart rate by Holter recordings in 62 patients with ECG alterations (CH2), 32 patients without ECG alterations (CH1) who had positive serological findings for disease of Chagaspsila and 36 healthy subjects (control) matched for sex and age. We find a orthogonal base that is able to discriminate the groups from circadian profiles, control and CH2, and stratify the groups CH1.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128119370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745420
C. Bescos, M. Harris, R. Bover, R. Schmidt, J. Pérez-Villacastín
Chronic Heart Failure (CHF) is the major cause for hospitalization in adults in western societies, mainly due to decompensation of patients. Active prevention, diagnosis, and personalized treatment contribute to the stabilization of chronic patients and the reduction of events. The approach of the EU FP6 MyHeart project is to collect daily vital body signs on CHF patients in an easy and comfortable way. The data is processed via a decision support system (DSS) and the platform gives instant recommendations to the user. The system also sends the information to the professionals for a better follow-up. The designed DSS is based on Bayesian networks (BN) and combines the accepted standardized clinical guidelines with the most advanced monitoring data in daily routine, in order to provide individualized recommendations to the patient in a concrete situation.
{"title":"Decision support system for the practical implementation of the Chronic Heart Failure guidelines: The MyHeart approach","authors":"C. Bescos, M. Harris, R. Bover, R. Schmidt, J. Pérez-Villacastín","doi":"10.1109/CIC.2007.4745420","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745420","url":null,"abstract":"Chronic Heart Failure (CHF) is the major cause for hospitalization in adults in western societies, mainly due to decompensation of patients. Active prevention, diagnosis, and personalized treatment contribute to the stabilization of chronic patients and the reduction of events. The approach of the EU FP6 MyHeart project is to collect daily vital body signs on CHF patients in an easy and comfortable way. The data is processed via a decision support system (DSS) and the platform gives instant recommendations to the user. The system also sends the information to the professionals for a better follow-up. The designed DSS is based on Bayesian networks (BN) and combines the accepted standardized clinical guidelines with the most advanced monitoring data in daily routine, in order to provide individualized recommendations to the patient in a concrete situation.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128148150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745511
E. Aramendi, U. Irusta, S. Ruiz de Gauna, J. Ruiz
In this study pediatric and adult Ventricular Tachycardia (VT) are used to test the efficiency of an AED analysis algorithm. Statistical assessment of the four significant parameters that define the shock-noshock classification algorithm has been performed. The following parameters are considered: Pulse Rate (PR), Waveform Power Ratio (WPR), and two morphological parameters, Baseline Content (BC) and Probability Distribution Width (PDW). A set of 76 adult and 55 pediatric shockable VT episodes is considered to measure the sensitivity of the classification algorithm originally developed for adult patients (100% for rapid adult VT). The sensitivity for the whole pediatric set is 96.36 %, but increases to 100% for the 1-8 years of age subgroup.
{"title":"Comparative analysis of the parameters affecting AED rhythm analysis algorithm applied to pediatric and adult Ventricular Tachycardia","authors":"E. Aramendi, U. Irusta, S. Ruiz de Gauna, J. Ruiz","doi":"10.1109/CIC.2007.4745511","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745511","url":null,"abstract":"In this study pediatric and adult Ventricular Tachycardia (VT) are used to test the efficiency of an AED analysis algorithm. Statistical assessment of the four significant parameters that define the shock-noshock classification algorithm has been performed. The following parameters are considered: Pulse Rate (PR), Waveform Power Ratio (WPR), and two morphological parameters, Baseline Content (BC) and Probability Distribution Width (PDW). A set of 76 adult and 55 pediatric shockable VT episodes is considered to measure the sensitivity of the classification algorithm originally developed for adult patients (100% for rapid adult VT). The sensitivity for the whole pediatric set is 96.36 %, but increases to 100% for the 1-8 years of age subgroup.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"10 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134529281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745552
J. Rodríguez-Sotelo, D. Cuesta-Frau, G. Castellanos-Domínguez
Clustering is advisable technique for analysis and interpretation of long-term ECG Holter records. As a non-supervised method, several challenges are posed due to factors such as signal length (very long duration), noise presence, dynamic behavior and morphology variability (different patient physiology and/or pathology). This work describes an improved version of the k-means clustering algorithm (J-means) for this task. In order to reduce the number of heartbeats to process, a preclustering stage is also employed. Dissimilarity measure calculation is based on the Dynamic Time Warping approach. To assess the validity of the proposed method, a comparative study is carried out, using k-means, k-medians, hk-means, and J-means. Heartbeat features are extracted by means of WT coefficients and trace segmentation. Best results were achieved by the J-means algorithm, which reduces the clustering error down to 4.5% while the critical error tends to the minimal value.
{"title":"An improved method for unsupervised analysis of ECG beats based on WT features and J-means clustering","authors":"J. Rodríguez-Sotelo, D. Cuesta-Frau, G. Castellanos-Domínguez","doi":"10.1109/CIC.2007.4745552","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745552","url":null,"abstract":"Clustering is advisable technique for analysis and interpretation of long-term ECG Holter records. As a non-supervised method, several challenges are posed due to factors such as signal length (very long duration), noise presence, dynamic behavior and morphology variability (different patient physiology and/or pathology). This work describes an improved version of the k-means clustering algorithm (J-means) for this task. In order to reduce the number of heartbeats to process, a preclustering stage is also employed. Dissimilarity measure calculation is based on the Dynamic Time Warping approach. To assess the validity of the proposed method, a comparative study is carried out, using k-means, k-medians, hk-means, and J-means. Heartbeat features are extracted by means of WT coefficients and trace segmentation. Best results were achieved by the J-means algorithm, which reduces the clustering error down to 4.5% while the critical error tends to the minimal value.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134580140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745596
S. Schmidt, C. Holst-Hansen, C. Graff, E. Toft, J. Struijk
A noninvasive method for detection of coronary artery disease (CAD) with an electronic stethoscope is proposed. Heart sounds recorded in clinical settings are often contaminated with background noise and noise caused by friction between the skin and the stethoscope. A method was developed to reduce the influence of the noise artifacts. The diastolic parts of the heart sounds were divided into multiple sub-segments, where noisy sub-segments were indentified as sub-segments with a low degree of stationarity or with a high energy level. The sub-segments not identified as noisy were analyzed with an autoregressive (AR) model, where the pole-magnitude of the 1st pole was used as a discriminating parameter. A test on 50 subjects showed that removal of the noisy sub-segments before analyses improved the diagnostic performance of the AR-model considerably, thereby reducing the influence of noise related to the use of a handhold stethoscope.
{"title":"Detection of coronary artery disease with an electronic stethoscope","authors":"S. Schmidt, C. Holst-Hansen, C. Graff, E. Toft, J. Struijk","doi":"10.1109/CIC.2007.4745596","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745596","url":null,"abstract":"A noninvasive method for detection of coronary artery disease (CAD) with an electronic stethoscope is proposed. Heart sounds recorded in clinical settings are often contaminated with background noise and noise caused by friction between the skin and the stethoscope. A method was developed to reduce the influence of the noise artifacts. The diastolic parts of the heart sounds were divided into multiple sub-segments, where noisy sub-segments were indentified as sub-segments with a low degree of stationarity or with a high energy level. The sub-segments not identified as noisy were analyzed with an autoregressive (AR) model, where the pole-magnitude of the 1st pole was used as a discriminating parameter. A test on 50 subjects showed that removal of the noisy sub-segments before analyses improved the diagnostic performance of the AR-model considerably, thereby reducing the influence of noise related to the use of a handhold stethoscope.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115679020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745597
E. Bianchini, C. Giannarelli, F. Faita, K. Raimo, V. Gemignani, L. Ghiadoni, M. Demi
Local arterial stiffness of the superficial arteries can be evaluated by measuring the diameter change during the heart cycle from ultrasound data in conjunction with the local pulse pressure. In this work, such a system is introduced, and the obtained results on the common carotid artery are compared with those obtained by measuring the carotid to femoral pulse wave velocity (PWV) which can be considered to be the ldquogold standardrdquo technique for the evaluation of arterial stiffness. 14 healthy subjects and 14 hypertensive patients were involved in the study. Results show that a direct evaluation of local carotid stiffness, obtained by an appropriate video processing system, can discriminate between healthy and hypertensive patients as does the carotid-femoral PWV technique.
{"title":"The assessment of local arterial stiffness from ultrasound images","authors":"E. Bianchini, C. Giannarelli, F. Faita, K. Raimo, V. Gemignani, L. Ghiadoni, M. Demi","doi":"10.1109/CIC.2007.4745597","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745597","url":null,"abstract":"Local arterial stiffness of the superficial arteries can be evaluated by measuring the diameter change during the heart cycle from ultrasound data in conjunction with the local pulse pressure. In this work, such a system is introduced, and the obtained results on the common carotid artery are compared with those obtained by measuring the carotid to femoral pulse wave velocity (PWV) which can be considered to be the ldquogold standardrdquo technique for the evaluation of arterial stiffness. 14 healthy subjects and 14 hypertensive patients were involved in the study. Results show that a direct evaluation of local carotid stiffness, obtained by an appropriate video processing system, can discriminate between healthy and hypertensive patients as does the carotid-femoral PWV technique.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116749106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745525
S. Babaeizadeh, S.H. Zhou, X. Liu, W.Y. Hu, D. Feild, E. Helfenbein, R. Gregg, J. Lindauer
In this paper, we introduce a new index based on the frequency-domain analysis of heart rate variability, or more precisely, the power spectrum of the instant heart rate signal. This index, called VHFI, is defined as the very high frequency component of the power spectrum normalized to represent its relative value in proportion to the total power minus the very low frequency component. We tested VHFI on patients with known reduced left ventricular function and found that this index has the potential to be a useful tool for quick evaluation of left ventricular function.
{"title":"A novel heart rate variability index for evaluation of left ventricular function using five-minute electrocardiogram","authors":"S. Babaeizadeh, S.H. Zhou, X. Liu, W.Y. Hu, D. Feild, E. Helfenbein, R. Gregg, J. Lindauer","doi":"10.1109/CIC.2007.4745525","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745525","url":null,"abstract":"In this paper, we introduce a new index based on the frequency-domain analysis of heart rate variability, or more precisely, the power spectrum of the instant heart rate signal. This index, called VHFI, is defined as the very high frequency component of the power spectrum normalized to represent its relative value in proportion to the total power minus the very low frequency component. We tested VHFI on patients with known reduced left ventricular function and found that this index has the potential to be a useful tool for quick evaluation of left ventricular function.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124841757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745458
T. Syeda-Mahmood, F. Wang, D. Beymer, A. Amir, M. Richmond, S. Hashmi
Diagnostic decision support is still very much an art for physicians in their practices today due to lack of quantitative tools. AALIM is a decision support system for cardiology that exploits the consensus opinions of other physicians who have looked at similar patients, to present statistical reports summarizing possible diagnoses. The key idea behind our statistical decision support system is the search for similar patients based on the underlying multimodal data. In this paper, we describe the AALIM decision support system and the underlying multimodal similarity search used for cardiac data sets.
{"title":"AALIM: Multimodal mining for cardiac decision support","authors":"T. Syeda-Mahmood, F. Wang, D. Beymer, A. Amir, M. Richmond, S. Hashmi","doi":"10.1109/CIC.2007.4745458","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745458","url":null,"abstract":"Diagnostic decision support is still very much an art for physicians in their practices today due to lack of quantitative tools. AALIM is a decision support system for cardiology that exploits the consensus opinions of other physicians who have looked at similar patients, to present statistical reports summarizing possible diagnoses. The key idea behind our statistical decision support system is the search for similar patients based on the underlying multimodal data. In this paper, we describe the AALIM decision support system and the underlying multimodal similarity search used for cardiac data sets.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"355 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122043090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745562
S. Parvaneh, M. Pashna
In this paper modifications to an algorithm for electrocardiogram (ECG) synthesis based on a combination of Gaussians to fit real ECG data have been proposed. A method is proposed for fitting algorithm assuming that constituent Gaussian functions in GCM model are independent. Desired period(s) of ECG were selected and the number of Gaussians in the morphologic model was determined. For ECG synthesis, a Gaussian was fitted around each of the extrema and minimized local error that is defined as local difference of real ECG and our model. The range of Gaussian fitting (place to put independent Gaussian) was determined using two methods: zero crossing method and minimum bank method. Results were presented based on the efficiency of determining the Gaussian parameters in terms of time for fitting and accuracy of model.
{"title":"Electrocardiogram synthesis using a Gaussian combination model (GCM)","authors":"S. Parvaneh, M. Pashna","doi":"10.1109/CIC.2007.4745562","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745562","url":null,"abstract":"In this paper modifications to an algorithm for electrocardiogram (ECG) synthesis based on a combination of Gaussians to fit real ECG data have been proposed. A method is proposed for fitting algorithm assuming that constituent Gaussian functions in GCM model are independent. Desired period(s) of ECG were selected and the number of Gaussians in the morphologic model was determined. For ECG synthesis, a Gaussian was fitted around each of the extrema and minimized local error that is defined as local difference of real ECG and our model. The range of Gaussian fitting (place to put independent Gaussian) was determined using two methods: zero crossing method and minimum bank method. Results were presented based on the efficiency of determining the Gaussian parameters in terms of time for fitting and accuracy of model.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"112 23","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113969547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2007-09-01DOI: 10.1109/CIC.2007.4745540
J. Mateo, C. Sánchez, C. Vayá, R. Cervigón, J. J. Rieta
Nowadays, there exist different approaches to cancel out noise effect and baseline drift in biomedical signals. However, none of them can be considered as completely satisfactory. In this work, an artificial neural network (ANN) based approach to cancel out baseline drift in electrocardiogram signals is presented. The system is based on a grown ANN allowing to optimize both the hidden layer number of nodes and the coefficient matrixes. These matrixes are optimized following the Window-Hoff Delta algorithm, offering much lower computational cost that the traditional back propagation algorithm. The proposed methodology has been compared with traditional baseline reduction methods (FIR, Wavelet and Adaptive LMS filtering) making use of cross correlation, signal to interference ratio and signal to noise ratio indexes. Obtained results show that the ANN-based approach performs better, with respect to baseline drift reduction and signal distortion at filter output, than traditional methods.
{"title":"A new adaptive approach to remove baseline wander from ECG recordings using Madeline structure","authors":"J. Mateo, C. Sánchez, C. Vayá, R. Cervigón, J. J. Rieta","doi":"10.1109/CIC.2007.4745540","DOIUrl":"https://doi.org/10.1109/CIC.2007.4745540","url":null,"abstract":"Nowadays, there exist different approaches to cancel out noise effect and baseline drift in biomedical signals. However, none of them can be considered as completely satisfactory. In this work, an artificial neural network (ANN) based approach to cancel out baseline drift in electrocardiogram signals is presented. The system is based on a grown ANN allowing to optimize both the hidden layer number of nodes and the coefficient matrixes. These matrixes are optimized following the Window-Hoff Delta algorithm, offering much lower computational cost that the traditional back propagation algorithm. The proposed methodology has been compared with traditional baseline reduction methods (FIR, Wavelet and Adaptive LMS filtering) making use of cross correlation, signal to interference ratio and signal to noise ratio indexes. Obtained results show that the ANN-based approach performs better, with respect to baseline drift reduction and signal distortion at filter output, than traditional methods.","PeriodicalId":406683,"journal":{"name":"2007 Computers in Cardiology","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129871018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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