The Experts below are selected from a list of 117 Experts worldwide ranked by ideXlab platform
Abdulnasir Hossen - One of the best experts on this subject based on the ideXlab platform.
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discrimination of parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal
Bio-medical Materials and Engineering, 2013Co-Authors: Abdulnasir Hossen, Muthuraman Muthuraman, Jan Raethjen, Gunther Deuschl, Z Alhakim, Ulrich HeuteAbstract:A new technique for discrimination of Parkinson tremor from essential tremor is presented in this paper. This technique is based on Statistical Signal Characterization (SSC) of the spectrum of the accelerometer signal. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the Classification Factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. Three of twelve newly derived SSC parameters show good discrimination results. Specific results of those three parameters on training data and test data are shown in detail. A linear combination of the effects of those parameters on the discrimination results is also included. A total discrimination accuracy of 90% is obtained.
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discrimination of parkinsonian tremor from essential tremor by implementation of a wavelet based soft decision technique on emg and accelerometer signals
Biomedical Signal Processing and Control, 2010Co-Authors: Abdulnasir Hossen, Muthuraman Muthuraman, Jan Raethjen, Gunther Deuschl, Ulrich HeuteAbstract:Abstract A wavelet-decomposition with soft-decision algorithm is used to estimate an approximate power-spectral density (PSD) of both accelerometer and surface EMG signals for the purpose of discrimination of Parkinson tremor from essential tremor. A soft-decision wavelet-based PSD estimation is used with 256 bands for a signal sampled at 800 Hz. The sum of the entropy of the PSD in band 6 (7.8125–9.375 Hz) and band 11 (15.625–17.1875 Hz) is used as a Classification Factor. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the Classification Factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. A “voting” between three results obtained from accelerometer signal and two EMG signals is applied to obtain the final discrimination. A total accuracy of discrimination of 85% is obtained.
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A wavelet-based soft decision technique for screening of patients with congestive heart failure
Biomedical Signal Processing and Control, 2007Co-Authors: Abdulnasir Hossen, Bader Al-ghunaimiAbstract:Abstract A wavelet-decomposition with soft decision algorithm is used to estimate an approximate power spectral density (PSD) of R–R intervals (RRI) of ECG data for the purpose of screening of congestive heart failure (CHF) from normal subjects. The ratio of the power in the low-frequency (LF) band to the power in the high-frequency (HF) band of the RRI signal is used as the Classification Factor. The trial data used for estimating of the Classification Factor consist of 15 CHF (patient) subjects and 12 normal sinus rhythm (NSR) or simply normal subjects. The performance of the algorithm is then evaluated on test data set, which consists of 17 CHF subjects and 53 NSR subjects. Both trial and test data are drawn from MIT database. The receiver operating characteristics (ROC) is used to determine the threshold value of the Classification Factor. Results are shown for different wavelets filters. The new technique shows a Classification efficiency of 96.30% on trial data and 88.57% on test data. An FFT-based frequency domain screening technique is also implemented and included in this work for the purpose of comparison with the wavelet-based technique. The FFT-based technique shows an efficiency of Classification of 99.63% on trial data and 81.42% on test data. The comparison is also done on short-term (5-min) recordings. The wavelet-based soft-decision technique shows also better results than the FFT-based technique.
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Statistical Signal Characterization of Spectral Analysis of Heart Rate Variability for Screening of Patients with Obstructive Sleep Apnea
The Journal of Engineering Research [TJER], 2006Co-Authors: Abdulnasir Hossen, B. Al Ghunaimi, Mohammed O. HassanAbstract:A new screening technique for obstructive sleep apnea (OSA) is implemented. This technique is based on finding the statistical signal characterization (SSC) parameters of the spectrum of pre-processed R-R-interval (RRI) data. A single Classification Factor (CR) is selected to classify between patients with obstructive sleep apnea and normal controls. Both nonparametric spectral analysis techniques (with Welch method as an example) and parametric techniques (with Burg method as an example) are used to estimate the power spectral density. The data tested in this work are drawn from MIT database. Both trial and test (challenge) groups of data are used. Each of these two groups contains 20 OSA and 10 normal records. The trial data is used to set the threshold value of the Classification Factor, which is then used in identifying the challenge data. The total accuracy of the screening is about 93% and 90% using Burg and Welch algorithms respectively.
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A soft decision algorithm for obstructive sleep apnea patient Classification based on fast estimation of wavelet entropy of RRI data
Technology and health care : official journal of the European Society for Engineering and Medicine, 2005Co-Authors: Abdulnasir HossenAbstract:A soft decision algorithm for Obstructive Sleep Apnea (OSA) patient Classification using R-R interval (RRI) data is investigated. This algorithm is based on fast and approximate estimation of the entropy of the wavelet-decomposed bands of the RRI data. The Classification is done on the whole record as OSA patient or non-patient (normal). The ratio of the estimated entropy of the low-frequency (LF) band to that of the very-low frequency (VLF) band is used as a Classification Factor. RRI data used in this work are drawn from MIT database. The MIT trial records are used to set the threshold value of the Classification Factor usiug the Receiver Operating Characteristics (ROC). This threshold value is used then to classify the MIT challenge (test) records to obtain the efficiency of Classification. The new algorithm classifies correctly 30/30 of MIT-test data using different wavelet filters. Comparison of the results of different wavelet filters is done in terms of complexity and distance parameters. The method is also compared with other two techniques using wavelets in their analysis. The consistency of the results is examined using the leave-one-out technique.
Jacques D Marleau - One of the best experts on this subject based on the ideXlab platform.
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risk assessment and offender victim relationship in juvenile offenders
International Journal of Offender Therapy and Comparative Criminology, 2007Co-Authors: Richard Lusignan, Jacques D MarleauAbstract:The present study compares the Historical, Clinical, and Risk Management-20 (HCR-20) checklist in a male offender population of 108 adolescents using the relationship between the offender and the victim as a Classification Factor. Two types of relationship were retained for comparison purposes: family victim/known victim and unknown victim. All adolescents admitted to the Adolescent program of Montreal's Philippe-Pinel Institute from February 1998 to April 2003 were assessed and their families were met. The HCR-20 checklist was completed for each adolescent. Statistically significant differences were observed for the mean rank of the total score of the HCR-20 and two sub-scales, the historical subscale (H) and the risk management subscale (R). The results indicate that the adolescents who victimize strangers have more violent risk Factors compared to those who victimize family/known victims. These results have important implications regarding prevention and treatment.
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Risk Assessment and Offender–Victim Relationship in Juvenile Offenders
International journal of offender therapy and comparative criminology, 2007Co-Authors: Richard Lusignan, Jacques D MarleauAbstract:The present study compares the Historical, Clinical, and Risk Management-20 (HCR-20) checklist in a male offender population of 108 adolescents using the relationship between the offender and the victim as a Classification Factor. Two types of relationship were retained for comparison purposes: family victim/known victim and unknown victim. All adolescents admitted to the Adolescent program of Montreal's Philippe-Pinel Institute from February 1998 to April 2003 were assessed and their families were met. The HCR-20 checklist was completed for each adolescent. Statistically significant differences were observed for the mean rank of the total score of the HCR-20 and two sub-scales, the historical subscale (H) and the risk management subscale (R). The results indicate that the adolescents who victimize strangers have more violent risk Factors compared to those who victimize family/known victims. These results have important implications regarding prevention and treatment.
Ulrich Heute - One of the best experts on this subject based on the ideXlab platform.
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discrimination of parkinsonian tremor from essential tremor using statistical signal characterization of the spectrum of accelerometer signal
Bio-medical Materials and Engineering, 2013Co-Authors: Abdulnasir Hossen, Muthuraman Muthuraman, Jan Raethjen, Gunther Deuschl, Z Alhakim, Ulrich HeuteAbstract:A new technique for discrimination of Parkinson tremor from essential tremor is presented in this paper. This technique is based on Statistical Signal Characterization (SSC) of the spectrum of the accelerometer signal. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the Classification Factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. Three of twelve newly derived SSC parameters show good discrimination results. Specific results of those three parameters on training data and test data are shown in detail. A linear combination of the effects of those parameters on the discrimination results is also included. A total discrimination accuracy of 90% is obtained.
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discrimination of parkinsonian tremor from essential tremor by implementation of a wavelet based soft decision technique on emg and accelerometer signals
Biomedical Signal Processing and Control, 2010Co-Authors: Abdulnasir Hossen, Muthuraman Muthuraman, Jan Raethjen, Gunther Deuschl, Ulrich HeuteAbstract:Abstract A wavelet-decomposition with soft-decision algorithm is used to estimate an approximate power-spectral density (PSD) of both accelerometer and surface EMG signals for the purpose of discrimination of Parkinson tremor from essential tremor. A soft-decision wavelet-based PSD estimation is used with 256 bands for a signal sampled at 800 Hz. The sum of the entropy of the PSD in band 6 (7.8125–9.375 Hz) and band 11 (15.625–17.1875 Hz) is used as a Classification Factor. The data has been recorded for diagnostic purposes in the Department of Neurology of the University of Kiel, Germany. Two sets of data are used. The training set, which consists of 21 essential-tremor (ET) subjects and 19 Parkinson-disease (PD) subjects, is used to obtain the threshold value of the Classification Factor differentiating between the two subjects. The test data set, which consists of 20 ET and 20 PD subjects, is used to test the technique and evaluate its performance. A “voting” between three results obtained from accelerometer signal and two EMG signals is applied to obtain the final discrimination. A total accuracy of discrimination of 85% is obtained.
Gjalt Huppes - One of the best experts on this subject based on the ideXlab platform.
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quantitative life cycle assessment of products 2 Classification valuation and improvement analysis
Journal of Cleaner Production, 1993Co-Authors: Jeroen B Guinee, Reinout Heijungs, Helias Udo A De Haes, Gjalt HuppesAbstract:In a previous article about life cycle assessment (LCA), a methodological framework was proposed and two components of this framework were discussed in more detail: the goal definition and the inventory. In this second article, the other components of the framework are discussed in detail: the Classification, the valuation and the improvement analysis. In the Classification, resource extractions and emissions associated with the life cycle of a product are translated into contributions to a number of environmental problem types, such as resource depletion, global warming, ozone depletion, acidification, etc. For this, each extraction and emission is multiplied with a so-called Classification Factor and the multiplication results are aggregated per problem type. Classification Factors are proposed for a number of environmental problem types. The valuation includes both a valuation of the different environmental problem types and an assessment of the reliability and validity of the results. For the valuation of the environmental problem types, qualitative or quantitative multicriterion analysis could be applied. Given a standard list of weighting Factors the quantitative multicriterion analysis seems preferable, because of its low costs and its simplicity. The main problem, however, is to get a broadly supported standard list. In studies so far little attention is paid to the assessment of the reliability and the validity of the results. To improve this situation methods which could support this assessment are proposed. In the improvement analysis potential options to improve the product(s) studied are identified. Combined with expertise in other fields, such as costs and technological feasibility, the improvement analysis may yield a number of serious options for the redesign of a product. Two complementary techniques for the identification of the potential options are discussed. With these techniques and the active participation of process technologists and designers, LCA might become an analytic tool for eco-design supporting a continuous environmental improvement of products.
Mohammed O. Hassan - One of the best experts on this subject based on the ideXlab platform.
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Statistical Signal Characterization of Spectral Analysis of Heart Rate Variability for Screening of Patients with Obstructive Sleep Apnea
The Journal of Engineering Research [TJER], 2006Co-Authors: Abdulnasir Hossen, B. Al Ghunaimi, Mohammed O. HassanAbstract:A new screening technique for obstructive sleep apnea (OSA) is implemented. This technique is based on finding the statistical signal characterization (SSC) parameters of the spectrum of pre-processed R-R-interval (RRI) data. A single Classification Factor (CR) is selected to classify between patients with obstructive sleep apnea and normal controls. Both nonparametric spectral analysis techniques (with Welch method as an example) and parametric techniques (with Burg method as an example) are used to estimate the power spectral density. The data tested in this work are drawn from MIT database. Both trial and test (challenge) groups of data are used. Each of these two groups contains 20 OSA and 10 normal records. The trial data is used to set the threshold value of the Classification Factor, which is then used in identifying the challenge data. The total accuracy of the screening is about 93% and 90% using Burg and Welch algorithms respectively.