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A new way of quantifying diagnostic information from multilead electrocardiogram for cardiac disease classification

HEALTHCARE TECHNOLOGY LETTERS 2014年第4期1卷 98-103页

作者： Tripathy, R. K. Sharma, L. N. Dandapat, S. Indian Inst Technol Dept Elect & Elect Engn Gauhati 781039 Assam India

A new measure for quantifying diagnostic information from a multilead electrocardiogram (MECG) is proposed. This diagnostic measure is based on principal component (PC) multivariate multiscale sample entropy (PMMSE). The PC analysis is used to reduce the dimension of the MECG data matrix. The multivariate multiscale sample entropy is evaluated over the PC matrix. The PMMSE values along each scale are used as a diagnostic feature vector. The performance of the proposed measure is evaluated using a least square support vector machine classifier for detection and classification of normal (healthy control) and different cardiovascular diseases such as cardiomyopathy, cardiac dysrhythmia, hypertrophy and myocardial infarction. The results show that the cardiac diseases are successfully detected and classified with an average accuracy of 90.34%. Comparison with some of the recently published methods shows improved performance of the proposed measure of cardiac disease classification.

关键词： MECG data matrix PMMSE cardiac disease classification cardiac dysrhythmia cardiovascular disease diagnostic feature vector diagnostic information diseases electrocardiography hypertrophy least square classifier medical diagnostic computing medical signal processing multilead electrocardiogram multivariate multiscale sample entropy myocardial infarction principal component principal component analysis support vector machine classifier support vector machines

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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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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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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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SVM-based prediction of linear B-cell epitopes using Bayes Feature Extraction

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BMC GENOMICS 2010年第4期11卷 1-9页

作者： Wee, Lawrence J. K. Simarmata, Diane Kam, Yiu-Wing Ng, Lisa F. P. Tong, Joo Chuan Inst Infocomm Res Data Min Dept Singapore 138632 Singapore Singapore Immunol Network Singapore 138648 Singapore Natl Univ Singapore Yong Loo Lin Sch Med Dept Biochem Singapore 117597 Singapore

Backgound: The identification of B-cell epitopes on antigens has been a subject of intense research as the knowledge of these markers has great implications for the development of peptide-based diagnostics, therapeutics and vaccines. As experimental approaches are often laborious and time consuming, in silico methods for prediction of these immunogenic regions are critical. Such efforts, however, have been significantly hindered by high variability in the length and composition of the epitope sequences, making naive modeling methods difficult to apply. Results: We analyzed two benchmark datasets and found that linear B-cell epitopes possess distinctive residue conservation and position-specific residue propensities which could be exploited for epitope discrimination in silico. We developed a support vector machines (SVM) prediction model employing Bayes Feature Extraction to predict linear B-cell epitopes of diverse lengths (12- to 20-mers). The best SVM classifier achieved an accuracy of 74.50% and A(ROC) of 0.84 on an independent test set and was shown to outperform existing linear B-cell epitope prediction algorithms. In addition, we applied our model to a dataset of antigenic proteins with experimentally-verified epitopes and found it to be generally effective for discriminating the epitopes from non-epitopes. Conclusion: We developed a SVM prediction model utilizing Bayes Feature Extraction and showed that it was effective in discriminating epitopes from non-epitopes in benchmark datasets and annotated antigenic proteins. A web server for predicting linear B-cell epitopes was developed and is available, together with supplementary materials, at http://***/bayesb/***.

关键词： support vector machine support vector machine classifier Amino Acid Pair support vector machine Training support vector machine Prediction

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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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Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing

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BMC BIOINFORMATICS 2012年第17期13卷 1-10页

作者： Su, Emily Chia-Yu Chang, Jia-Ming Cheng, Cheng-Wei Sung, Ting-Yi Hsu, Wen-Lian Taipei Med Univ Grad Inst Biomed Informat Taipei Taiwan Comparat Bioinformat Bioinformat & Genom Ctr Geno Barcelona 08003 Spain UPF Barcelona 08003 Spain Acad Sinica Inst Informat Sci Bioinformat Lab Taipei Taiwan

Background: Identification of subcellular localization in proteins is crucial to elucidate cellular processes and molecular functions in a cell. However, given a tremendous amount of sequence data generated in the postgenomic era, determining protein localization based on biological experiments can be expensive and timeconsuming. Therefore, developing prediction systems to analyze uncharacterised proteins efficiently has played an important role in high-throughput protein analyses. In a eukaryotic cell, many essential biological processes take place in the nucleus. Nuclear proteins shuttle between nucleus and cytoplasm based on recognition of nuclear translocation signals, including nuclear localization signals (NLSs) and nuclear export signals (NESs). Currently, only a few approaches have been developed specifically to predict nuclear localization using sequence features, such as putative NLSs. However, it has been shown that prediction coverage based on the NLSs is very low. In addition, most existing approaches only attained prediction accuracy and Matthew's correlation coefficient (MCC) around 54%similar to 70% and 0.250 similar to 0.380 on independent test set, respectively. Moreover, no predictor can generate sequence motifs to characterize features of potential NESs, in which biological properties are not well understood from existing experimental studies. Results: In this study, first we propose PSLNuc (Protein Subcellular Localization prediction for Nucleus) for predicting nuclear localization in proteins. First, for feature representation, a protein is represented by gapped-dipeptides and the feature values are weighted by homology information from a smoothed position-specific scoring matrix. After that, we incorporate probabilistic latent semantic indexing (PLSI) for feature reduction. Finally, the reduced features are used as input for a support vector machine (SVM) classifier. In addition to PSLNuc, we further identify gapped-dipeptide signatures for pu

关键词： Feature vector Nuclear Localization support vector machine classifier Feature Reduction Latent Semantic Indexing

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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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