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Prediction of oral hepatotoxic dose of natural products derived from traditional Chinese medicines based on SVM classifier and PBPK modeling

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ARCHIVES OF TOXICOLOGY 2021年第5期95卷 1683-1701页

作者： Li, Size Yu, Yiqun Bian, Xiaolan Yao, Li Li, Min Lou, Yan-Ru Yuan, Jing Lin, Hai-shu Liu, Lucy Han, Bing Xiang, Xiaoqiang Fudan Univ Sch Pharm Dept Clin Pharm & Pharm Adm Shanghai 201203 Peoples R China Fudan Univ Minhang Hosp Dept Pharm Shanghai 201199 Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Dept Pharm Sch Med Shanghai 20025 Peoples R China Shenzhen Technol Univ Coll Pharm Shenzhen 518118 Guangdong Peoples R China Shanghai Qiangshi Informat Technol Co Ltd Shanghai Peoples R China

The risk of drug-induced liver injury (DILI) poses a major challenge for development of natural products derived from traditional Chinese medicines (NP-TCMs). It is urgent to find a new method for the safety assessment of the NP-TCMs. Recent study has reported an in vitro/in silico method to estimate the acceptable daily intake of hepatotoxic compounds using support vector machine (SVM) classifier and physiologically based pharmacokinetic (PBPK) modeling. However, this method is not suitable for estimating the dosing schedule of compounds which are administered in multiple daily doses. Thus, in this study, the method mentioned above was in particular optimized, and used to estimate the hepatotoxic plasma concentrations of 17 NP-TCMs. Additionally, the oral dosing schedules of the triptolide, emodin, matrine and oxymatrine were also predicted by the SVM classifier and PBPK modeling. The optimization included that: (1) in vitro cytotoxicity data of 28 training set compounds was optimized using benchmark concentrations (BMC) modeling;(2) AUC of the training set compound was used as the in vivo metric instead of C-max to better reflect the total daily exposure of compounds which are administered in multiple daily doses;(3) using the mean AUC in plasma as in vivo metric and BMC value as in vitro metric could achieve the better toxicity separation index (0.962 vs. 0.938);(4) The TSI for C-max and BMC values was 0.985 calculated in this study, and the results indicated that BMC modeling improved the separation performance. This optimized in vitro-in vivo extrapolation (IVIVE) workflow could extrapolate in vitro BMC to blood concentrations and the oral dosing schedule which are corresponding to certain risk of hepatotoxicity. The estimated safe dosing schedule of oxymatrine by this optimized method was close to the clinical recommended dosing regimen. The results indicate that the optimized method could be used to predict the dosing schedule of compounds administered in mul

关键词： support vector machine classifier Drug-induced liver injury Natural products derived from traditional Chinese medicines PBPK modeling In vitro to in vivo extrapolation

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WiFi-based passive sensing system for human presence and activity event classification

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IET WIRELESS SENSOR SYSTEMS 2018年第6期8卷 276-283页

作者： Li, Wenda Tan, Bo Piechocki, Robert J. Univ Bristol Dept Elect & Elect Engn Bristol Avon England Coventry Univ Sch Comp Elect & Math Coventry W Midlands England

Detection of human presence and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security, and low impact building. However, existing radio frequency identification tags, wearables, and passive infrared approaches require the user to carry dedicated electronic devices that suffer from problems of low detection accuracy and false alarms. This study proposes a novel system for non-invasive human sensing by analysing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to stationary objects, thus there is no need for any scenario-specific calibration which makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the support vector machine classifier towards activity event recognition. The proposed methodology is evaluated on a software defined radio system together with the experiment of five different events. The results indicate that the proposed system is sufficient for indoor context awareness, with 95.3% overall accuracy for event classification and 93.3% accuracy for human presence detection, which outperforms the traditional received signal strength approach where accuracy is 69.3% for event classification and 83.3% for human presence detection.

关键词： software radio wireless LAN ubiquitous computing support vector machines wireless sensor networks Doppler effect calibration time-frequency analysis signal classification vectors WiFi-based passive sensing system activity event classification passive infrared approaches noninvasive human sensing WiFi signal time-frequency domain feature vectors activity event recognition software defined radio system human presence detection radiofrequency identification tags e-healthcare electronic devices WiFi Doppler information analysis calibration support vector machine classifier indoor context-awareness applications received signal strength approach

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Analysis of support vector machine and maximum likelihood classifiers in land cover classification using Sentinel-2 images

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PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2022年第2期88卷 213-227页

作者： John, Susan Varghese, A. O. St Francis De Sales Coll Seminary Hills Nagpur 440006 Maharashtra India RRSC Cent Dept Space Amravati RdNRSC ISRO PB 003Univ Campus PO Nagpur 440033 Maharashtra India

Remote sensing data has been widely applied to classify the land cover more frequently and on a near real-time basis for updating as it is more economic, less time consuming compared to ground based survey. Accurate classification of the land use/cover classes such as water body, cropland, built-up area, scrub land, fallow land, forest etc., is one of the biggest challenges in natural resource inventory, management and monitoring. As accuracy of remote sensing data classification is affected by many parameters which include type of data, presence of heterogeneous landscapes in study area, classification approaches etc., as satellite imagery is complex in nature. Many classifiers have been developed and tested on remotely sensed data for better classification. Classification of remote sensing data is mainly divided into two categories such as supervised and unsupervised. In supervised classification, the decision boundaries in feature space are determined by training the samples. Two supervised classifiers namely maximum likelihood (ML) classifier, which is a parametric classifier that assumes data to be normally distributed, and support vector machine classifier (SVM) which is a non-parametric classifier are used in classification. In the present study, the accuracy of these two classifiers is studied on five different data sets of Sentinel-2 satellite image of different years and sessions to accommodate intra and inter annual variations of the datasets. Sentinel-2 satellite images covering part of Nagpur, located in Maharashtra, India were used for the classification. classifier accuracy has been calculated using overall accuracy and kappa statistics based on ground truth information. The result obtained were carefully examined by comparing classification accuracies and then by visual analysis. The result shows that SVM classifiers gives better overall accuracy and kappa coefficients and its average value for intra and inter annual classification outputs were 91.78

关键词： support vector machine classifier Maximum likelihood classifier Overall accuracy Kappa coefficient Sentinel-2

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Detection of focal electroencephalogram signals using higher-order moments in EMD-TKEO domain

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HEALTHCARE TECHNOLOGY LETTERS 2019年第3期6卷 64-69页

作者： Chatterjee, Soumya Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Detection of epileptogenic focus based on electroencephalogram (EEG) signal screening is an important pre-surgical step to remove affected regions inside the human brain. Considering the fact above, in this work, a novel technique for detection of focal EEG signals is proposed using a combination of empirical mode decomposition (EMD) and Teager-Kaiser energy operator (TKEO). EEG signals belonging to focal (Fo) and non-focal (NFo) groups were at first decomposed into a set of intrinsic mode functions (IMFs) using EMD. Next, TKEO was applied on each IMF and two higher-order statistical moments namely skewness and kurtosis were extracted as features from TKEO of each IMF. The statistical significance of the selected features was evaluated using student's t-test and based on the statistical test, features from first three IMFs which show very high discriminative capability were selected as inputs to a support vector machine classifier for discrimination of Fo and NFo signals. It was observed that the classification accuracy of 92.65% is obtained in classifying EEG signals using a radial basis kernel function, which demonstrates the efficacy of proposed EMD-TKEO based feature extraction method for computer-based treatment of patients suffering from focal seizures.

关键词： brain support vector machines electroencephalography feature extraction medical signal processing medical disorders medical signal detection signal classification focal electroencephalogram signals higher-order moments EMD-TKEO domain epileptogenic focus electroencephalogram signal screening important pre-surgical step human brain empirical mode decomposition Teager-Kaiser energy operator nonfocal groups intrinsic mode functions IMF higher-order statistical moments focal seizures proposed EMD-TKEO based feature extraction method radial basis kernel function support vector machine classifier high discriminative capability statistical test statistical significance kurtosis skewness

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Derivative component analysis for mass spectral serum proteomic profiles

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BMC MEDICAL GENOMICS 2014年第1-Sup期7卷 1-14页

作者： Han, Henry Fordham Univ Dept Comp & Informat Sci New York NY 10458 USA Columbia Univ Quantitat Prote Ctr New York NY 10027 USA

Background: As a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage. Methods: In this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers. Results: Our results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagno

关键词： Feature Selection Partial Less Square Discrete Wavelet Transform support vector machine classifier Feature Selection Method

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Vital sign normalisation for improving performance of multi-parameter patient monitors

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ELECTRONICS LETTERS 2015年第25期51卷 2089-2090页

作者： Kumar, C. S. Ramachandran, K. I. Kumar, A. A. Amrita Vishwa Vidyapeetham ECE Dept Machine Intelligence Res Lab Coimbatore Tamil Nadu India Amrita Inst Med Sci Dept Neurol Kochi and Kerala India

Using covariance normalisation (CVN) of vital signs is explored to improve the performance of multi-parameter patient monitors with heart rate, arterial blood pressure, respiration rate, and oxygen saturation (SpO(2)) as its input. The baseline system for the experiments is a support vector machine classifier with a radial basis function kernel. Although an improvement in the overall classification accuracy with the use of CVN is obtained, there was a deterioration in sensitivity. Furthermore, it is noted that the estimate of the covariance is often noisy, and therefore the covariance estimates is smoothed to obtain a performance improvement of 0.23% absolute for sensitivity, 1.34% absolute for specificity, and 1.08% absolute for the overall classification accuracy. Multi-parameter intelligent monitoring in intensive care II database for all the experiments is used.

关键词： biomedical electronics blood pressure measurement medical signal processing patient monitoring radial basis function networks signal classification support vector machines arterial blood pressure covariance normalisation heart rate multiparameter patient monitors oxygen saturation radial basis function kernel respiration rate support vector machine classifier vital sign normalisation

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machine learning-based edge-computing on a multi-level architecture of WSN and IoT for real-time fall detection

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IET WIRELESS SENSOR SYSTEMS 2020年第6期10卷 320-332页

作者： El Attaoui, Amina Largo, Salma Kaissari, Soufiane Benba, Achraf Jilbab, Abdelilah Bourouhou, Abdennaser Mohammed V Univ ENSET STIS Elect Sensors Syst & Nanobiotechnol Rabat Morocco INPT Rabat STRS Lab Smart Embedded Enterprise & Distributed Syst SEED Rabat Morocco

Health telemonitoring systems are constrained by the computational and data transmission load resulting from the large volumes of various measured signals, e.g. in the fall detection application. Nevertheless, the trend of movement and the implementation of computer intelligence in intelligent devices ensure an intelligent and convenient method for continuous real-time telemonitoring of health conditions. In this paper, fall detection is presented while leveraging edge computing integrated on a multi-level architecture combines the Wireless Sensors Network and the Internet of Things. Particularly, we present a complete study and implementation scenarios while investigating the performances of machine learning algorithms to distinguish between different fall patterns and activities of daily living using a set of significant extracted features from measured acceleration and angular velocity signals. For low computational requirements and to improve the classification performances, the Linear Discriminant Analysis is used to reduce the dimensionality of extracted features. The experimental results assess the performances of the proposed approach in fall detection that show the highest accuracy of 99.92% provided using the KNN classifier and accuracy of 97.5% for fall pattern recognition using the SVM classifier. Also, the online classification on the Fog device reached an accuracy of 94.42% using the SVM classifier.

关键词： geriatrics pattern recognition support vector machines feature extraction pattern classification telemedicine patient monitoring wireless sensor networks learning (artificial intelligence) machine learning-based edge-computing multilevel architecture WSN IoT real-time fall detection health telemonitoring systems computational data transmission burden fall detection application computer intelligence implementation smart devices real-time monitoring health conditions human fall detection leveraging edge wireless sensor network implementation scenarios machine-learning algorithms different fall patterns significant extracted features measured acceleration angular velocity signals low computational requirements online computing classification performances supervised learning fall pattern recognition support vector machine classifier run-time classification Fog device

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CURVELET FUSION ENHACEMENT BASED EVALUATION OF DIABETIC RETINOPATHY BY THE IDENTIFICATION OF EXUDATES IN OPTIC COLOR FUNDUS IMAGES

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BIOMEDICAL ENGINEERING-APPLICATIONS BASIS COMMUNICATIONS 2016年第6期28卷 1650046-1-1650046-10页

作者： Bhargavi, V. Ratna Senapati, Ranjan K. KL Univ Dept Elect & Commun Engn Guntur 522502 Andhra Pradesh India

Rapid growth of Diabetes mellitus in people causes damage to posterior part of eye vessel structures. Diabetic retinopathy (DR) is an important hurdle in diabetic people and it causes lesion formation in retina due to retinal vessel structures damage. Bright lesions (BLs) or exudates are initial clinical signs of DR. Early BLs detection can help avoiding vision loss. The severity can be recognized based on number of BLs formed in the color fundus image. Manually diagnosing a large amount of images is time consuming. So a computerized DR grading and BLs detection system is proposed. In this paper for BLs detection, curvelet fusion enhancement is done initially because bright objects maps to largest coeffcients in an image by utilizing the curvelet transform, so that BLs can be recognized in the retina easily. Then optic disk (OD) appearance is similar to BLs and vessel structures are barriers for lesion exact detection and moreover OD falsely classified as BLs and that increases false positives in classification. So these structures are segmented and eliminated by thresholding techniques. Various features were obtained from detected BLs. Publicly available databases are used for DR severity testing. 260 fundus images were used for the performance evaluation of proposed work. The support vector machine classifier (SVM) used to separate fundus images in various levels of DR based on feature set extracted. The proposed system that obtained the statistical measures were sensitivity 100%, specificity 95.4% and accuracy 97.74%. Compared to existing state-of-art techniques, the proposed work obtained better results in terms of sensitivity, specificity and accuracy.

关键词： Diabetic retinopathy Bright lesions Curvelet fusion Feature extraction Classification Segmentation support vector machine classifier

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Visual navigation method for indoor mobile robot based on extended BoW model

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2017年第4期2卷 142-147页

作者： Li, Xianghui Li, Xinde Khyam, Mohammad Omar Luo, Chaomin Tan, Yingzi Southeast Univ Sch Automat Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China Natl Univ Singapore Fac Engn Dept Elect & Comp Engn Singapore 117580 Singapore Univ Detroit Mercy Dept Elect & Comp Engn Detroit MI 48221 USA Southeast Univ Sch Cyber Sci & Engn Nanjing 211189 Peoples R China

This article proposes a new navigation method for mobile robots based on an extended bag of words (BoW) model for general object recognition in indoor environments. The scale-invariant feature transform (SIFT)detection algorithm with the graphic processing unit (GPU) acceleration technology is used to describe feature vectors in this model. First, in order to add some redundant image information, statistical information of the spatial relationships of all the feature points in an image, i.e. relative distances and angles, is used to extend the feature vectors in the original BoW model. Then, the support vector machine (SVM) classifier is used to classify objects. Also, in order to navigate conveniently in unknown and dynamic indoor environments, a type of human-robot interaction method based on a hand-drawn semantic map is considered. The experimental results show that this new navigation technology for indoor mobile robots is very robust and highly effective.

关键词： robot vision transforms statistical information mobile robots feature vectors navigation technology object recognition redundant image information feature extraction extended bag original BoW model graphic processing unit acceleration technology indoor mobile robot dynamic indoor environments words model extended BoW model unknown environments scale-invariant feature support vector machines human-robot interaction general object recognition feature points human–robot interaction method (SIFT)-detection algorithm relative distances angles visual navigation method support vector machine classifier

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A built-in active sensing system-based structural health monitoring technique using statistical pattern recognition

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2007年第6期21卷 896-902页

作者： Park, Seunghee Lee, Jong-Jae Yun, Chung-Bang Inman, Daniel J. Korea Adv Inst Sci & Technol Smart Infra Struct Technol Taejon 305701 South Korea Virginia Tech Ctr Intelligent Material Syst & Struct Blacksburg VA 24061 USA

A piezoelectric sensor-based health monitoring technique using a two-step support vector machine (SVM) classifier is developed for railroad track damage identification. A built-in active sensing system composed of two PZT patches was investigated in conjunction with both impedance and guided wave propagation methods to detect two kinds of damage in a railroad track (hole-damage 0.5cm in diameter at the web section and transverse cut damage 7.5cm in length and 0.5cm in depth at the head section). Two damage-sensitive features were separately extracted from each method: a) feature 1: root mean square deviations (RMSD) of impedance signatures, and b) feature II : sum of square of wavelet coefficients for maximum energy mode of guided waves. By defining damage indices from these two damagesensitive features, a two-dimensional damage feature (2-D DF) space was made. In order to enhance the damage identification capability of the current active sensing system, a two-step SVM classifier was applied to the 2-D DF space. As a result, optimal separable hyper-planes (OSH) were successfully established by the two-step SVM classifier: Damage detection was accomplished by the first step-SVM, and damage classification was carried out by the second step-SVM. Finally, the applicability of the proposed two-step SVM classifier has been verified by thirty test patterns prepared in advance from the intact state and two damage states.

关键词： structural health moniboring support vector machine classifier rail road track damage identification

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