Displaying publications 61 - 80 of 89 in total

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  1. Fulltext Automated cervical precancerous cells screening system based on Fourier transform infrared spectroscopy features
    Jusman Y, Mat Isa NA, Ng SC, Hasikin K, Abu Osman NA
    J Biomed Opt, 2016 07 01;21(7):75005.
    PMID: 27403606 DOI: 10.1117/1.JBO.21.7.075005
    Fourier transform infrared (FTIR) spectroscopy technique can detect the abnormality of a cervical cell that occurs before the morphological change could be observed under the light microscope as employed in conventional techniques. This paper presents developed features extraction for an automated screening system for cervical precancerous cell based on the FTIR spectroscopy as a second opinion to pathologists. The automated system generally consists of the developed features extraction and classification stages. Signal processing techniques are used in the features extraction stage. Then, discriminant analysis and principal component analysis are employed to select dominant features for the classification process. The datasets of the cervical precancerous cells obtained from the feature selection process are classified using a hybrid multilayered perceptron network. The proposed system achieved 92% accuracy.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  2. Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals
    Tan JH, Hagiwara Y, Pang W, Lim I, Oh SL, Adam M, et al.
    Comput Biol Med, 2018 03 01;94:19-26.
    PMID: 29358103 DOI: 10.1016/j.compbiomed.2017.12.023
    Coronary artery disease (CAD) is the most common cause of heart disease globally. This is because there is no symptom exhibited in its initial phase until the disease progresses to an advanced stage. The electrocardiogram (ECG) is a widely accessible diagnostic tool to diagnose CAD that captures abnormal activity of the heart. However, it lacks diagnostic sensitivity. One reason is that, it is very challenging to visually interpret the ECG signal due to its very low amplitude. Hence, identification of abnormal ECG morphology by clinicians may be prone to error. Thus, it is essential to develop a software which can provide an automated and objective interpretation of the ECG signal. This paper proposes the implementation of long short-term memory (LSTM) network with convolutional neural network (CNN) to automatically diagnose CAD ECG signals accurately. Our proposed deep learning model is able to detect CAD ECG signals with a diagnostic accuracy of 99.85% with blindfold strategy. The developed prototype model is ready to be tested with an appropriate huge database before the clinical usage.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  3. Ovarian tissue characterization in ultrasound: a review
    Acharya UR, Molinari F, Sree SV, Swapna G, Saba L, Guerriero S, et al.
    Technol Cancer Res Treat, 2015 Jun;14(3):251-61.
    PMID: 25230716 DOI: 10.1177/1533034614547445
    Ovarian cancer is the most common cause of death among gynecological malignancies. We discuss different types of clinical and nonclinical features that are used to study and analyze the differences between benign and malignant ovarian tumors. Computer-aided diagnostic (CAD) systems of high accuracy are being developed as an initial test for ovarian tumor classification instead of biopsy, which is the current gold standard diagnostic test. We also discuss different aspects of developing a reliable CAD system for the automated classification of ovarian cancer into benign and malignant types. A brief description of the commonly used classifiers in ultrasound-based CAD systems is also given.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods
  4. Automatic diagnosis of epileptic seizures using entropy-based features and multimodel deep learning approaches
    Al-Qazzaz NK, Alrahhal M, Jaafer SH, Ali SHBM, Ahmad SA
    Med Eng Phys, 2024 Aug;130:104206.
    PMID: 39160030 DOI: 10.1016/j.medengphy.2024.104206
    Epilepsy is one of the most common brain diseases, characterised by repeated seizures that occur on a regular basis. During a seizure, a patient's muscles flex uncontrollably, causing a loss of mobility and balance, which can be harmful or even fatal. Developing an automatic approach for warning patients of oncoming seizures necessitates substantial research. Analyzing the electroencephalogram (EEG) output from the human brain's scalp region can help predict seizures. EEG data were analyzed to extract time domain features such as Hurst exponent (Hur), Tsallis entropy (TsEn), enhanced permutation entropy (impe), and amplitude-aware permutation entropy (AAPE). In order to automatically diagnose epileptic seizure in children from normal children, this study conducted two sessions. In the first session, the extracted features from the EEG dataset were classified using three machine learning (ML)-based models, including support vector machine (SVM), K nearest neighbor (KNN), or decision tree (DT), and in the second session, the dataset was classified using three deep learning (DL)-based recurrent neural network (RNN) classifiers in The EEG dataset was obtained from the Neurology Clinic of the Ibn Rushd Training Hospital. In this regard, extensive explanations and research from the time domain and entropy characteristics demonstrate that employing GRU, LSTM, and BiLSTM RNN deep learning classifiers on the All-time-entropy fusion feature improves the final classification results.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods
  5. A new hybrid intelligent system for accurate detection of Parkinson's disease
    Hariharan M, Polat K, Sindhu R
    Comput Methods Programs Biomed, 2014 Mar;113(3):904-13.
    PMID: 24485390 DOI: 10.1016/j.cmpb.2014.01.004
    Elderly people are commonly affected by Parkinson's disease (PD) which is one of the most common neurodegenerative disorders due to the loss of dopamine-producing brain cells. People with PD's (PWP) may have difficulty in walking, talking or completing other simple tasks. Variety of medications is available to treat PD. Recently, researchers have found that voice signals recorded from the PWP is becoming a useful tool to differentiate them from healthy controls. Several dysphonia features, feature reduction/selection techniques and classification algorithms were proposed by researchers in the literature to detect PD. In this paper, hybrid intelligent system is proposed which includes feature pre-processing using Model-based clustering (Gaussian mixture model), feature reduction/selection using principal component analysis (PCA), linear discriminant analysis (LDA), sequential forward selection (SFS) and sequential backward selection (SBS), and classification using three supervised classifiers such as least-square support vector machine (LS-SVM), probabilistic neural network (PNN) and general regression neural network (GRNN). PD dataset was used from University of California-Irvine (UCI) machine learning database. The strength of the proposed method has been evaluated through several performance measures. The experimental results show that the combination of feature pre-processing, feature reduction/selection methods and classification gives a maximum classification accuracy of 100% for the Parkinson's dataset.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*; Diagnosis, Computer-Assisted/statistics & numerical data
  6. An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding
    Saleh MD, Eswaran C
    Comput Methods Biomech Biomed Engin, 2012;15(5):517-25.
    PMID: 21331960 DOI: 10.1080/10255842.2010.545949
    Retinal blood vessel detection and analysis play vital roles in early diagnosis and prevention of several diseases, such as hypertension, diabetes, arteriosclerosis, cardiovascular disease and stroke. This paper presents an automated algorithm for retinal blood vessel segmentation. The proposed algorithm takes advantage of powerful image processing techniques such as contrast enhancement, filtration and thresholding for more efficient segmentation. To evaluate the performance of the proposed algorithm, experiments were conducted on 40 images collected from DRIVE database. The results show that the proposed algorithm yields an accuracy rate of 96.5%, which is higher than the results achieved by other known algorithms.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*; Diagnosis, Computer-Assisted/statistics & numerical data
  7. Application of an optimal class of antisymmetric wavelet filter banks for obstructive sleep apnea diagnosis using ECG signals
    Sharma M, Agarwal S, Acharya UR
    Comput Biol Med, 2018 09 01;100:100-113.
    PMID: 29990643 DOI: 10.1016/j.compbiomed.2018.06.011
    Obstructive sleep apnea (OSA) is a sleep disorder caused due to interruption of breathing resulting in insufficient oxygen to the human body and brain. If the OSA is detected and treated at an early stage the possibility of severe health impairment can be mitigated. Therefore, an accurate automated OSA detection system is indispensable. Generally, OSA based computer-aided diagnosis (CAD) system employs multi-channel, multi-signal physiological signals. However, there is a great need for single-channel bio-signal based low-power, a portable OSA-CAD system which can be used at home. In this study, we propose single-channel electrocardiogram (ECG) based OSA-CAD system using a new class of optimal biorthogonal antisymmetric wavelet filter bank (BAWFB). In this class of filter bank, all filters are of even length. The filter bank design problem is transformed into a constrained optimization problem wherein the objective is to minimize either frequency-spread for the given time-spread or time-spread for the given frequency-spread. The optimization problem is formulated as a semi-definite programming (SDP) problem. In the SDP problem, the objective function (time-spread or frequency-spread), constraints of perfect reconstruction (PR) and zero moment (ZM) are incorporated in their time domain matrix formulations. The global solution for SDP is obtained using interior point algorithm. The newly designed BAWFB is used for the classification of OSA using ECG signals taken from the physionet's Apnea-ECG database. The ECG segments of 1 min duration are decomposed into six wavelet subbands (WSBs) by employing the proposed BAWFB. Then, the fuzzy entropy (FE) and log-energy (LE) features are computed from all six WSBs. The FE and LE features are classified into normal and OSA groups using least squares support vector machine (LS-SVM) with 35-fold cross-validation strategy. The proposed OSA detection model achieved the average classification accuracy, sensitivity, specificity and F-score of 90.11%, 90.87% 88.88% and 0.92, respectively. The performance of the model is found to be better than the existing works in detecting OSA using the same database. Thus, the proposed automated OSA detection system is accurate, cost-effective and ready to be tested with a huge database.
    Matched MeSH terms: Diagnosis, Computer-Assisted
  8. Fulltext Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions
    Loh BCS, Then PHH
    Mhealth, 2017;3:45.
    PMID: 29184897 DOI: 10.21037/mhealth.2017.09.01
    Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.
    Matched MeSH terms: Diagnosis, Computer-Assisted
  9. Fulltext Automated bone age assessment: motivation, taxonomies, and challenges
    Mansourvar M, Ismail MA, Herawan T, Raj RG, Kareem SA, Nasaruddin FH
    Comput Math Methods Med, 2013;2013:391626.
    PMID: 24454534 DOI: 10.1155/2013/391626
    Bone age assessment (BAA) of unknown people is one of the most important topics in clinical procedure for evaluation of biological maturity of children. BAA is performed usually by comparing an X-ray of left hand wrist with an atlas of known sample bones. Recently, BAA has gained remarkable ground from academia and medicine. Manual methods of BAA are time-consuming and prone to observer variability. This is a motivation for developing automated methods of BAA. However, there is considerable research on the automated assessment, much of which are still in the experimental stage. This survey provides taxonomy of automated BAA approaches and discusses the challenges. Finally, we present suggestions for future research.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  10. Improved biliary detection and diagnosis through intelligent machine analysis
    Logeswaran R
    Comput Methods Programs Biomed, 2012 Sep;107(3):404-12.
    PMID: 21194781 DOI: 10.1016/j.cmpb.2010.12.002
    This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods
  11. AMI screening using linguistic fuzzy rules
    Ainon RN, Bulgiba AM, Lahsasna A
    J Med Syst, 2012 Apr;36(2):463-73.
    PMID: 20703704 DOI: 10.1007/s10916-010-9491-2
    This paper aims at identifying the factors that would help to diagnose acute myocardial infarction (AMI) using data from an electronic medical record system (EMR) and then generating structure decisions in the form of linguistic fuzzy rules to help predict and understand the outcome of the diagnosis. Since there is a tradeoff in the fuzzy system between the accuracy which measures the capability of the system to predict the diagnosis of AMI and transparency which reflects its ability to describe the symptoms-diagnosis relation in an understandable way, the proposed fuzzy rules are designed in a such a way to find an appropriate balance between these two conflicting modeling objectives using multi-objective genetic algorithms. The main advantage of the generated linguistic fuzzy rules is their ability to describe the relation between the symptoms and the outcome of the diagnosis in an understandable way, close to human thinking and this feature may help doctors to understand the decision process of the fuzzy rules.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  12. A hybrid neural network system for pattern classification tasks with missing features
    Lim CP, Leong JH, Kuan MM
    IEEE Trans Pattern Anal Mach Intell, 2005 Apr;27(4):648-53.
    PMID: 15794170
    A hybrid neural network comprising Fuzzy ARTMAP and Fuzzy C-Means Clustering is proposed for pattern classification with incomplete training and test data. Two benchmark problems and a real medical pattern classification task are employed to evaluate the effectiveness of the hybrid network. The results are analyzed and compared with those from other methods.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  13. Fulltext Adaptive localization of focus point regions via random patch probabilistic density from whole-slide, Ki-67-stained brain tumor tissue
    Alomari YM, Sheikh Abdullah SN, MdZin RR, Omar K
    Comput Math Methods Med, 2015;2015:673658.
    PMID: 25793010 DOI: 10.1155/2015/673658
    Analysis of whole-slide tissue for digital pathology images has been clinically approved to provide a second opinion to pathologists. Localization of focus points from Ki-67-stained histopathology whole-slide tissue microscopic images is considered the first step in the process of proliferation rate estimation. Pathologists use eye pooling or eagle-view techniques to localize the highly stained cell-concentrated regions from the whole slide under microscope, which is called focus-point regions. This procedure leads to a high variety of interpersonal observations and time consuming, tedious work and causes inaccurate findings. The localization of focus-point regions can be addressed as a clustering problem. This paper aims to automate the localization of focus-point regions from whole-slide images using the random patch probabilistic density method. Unlike other clustering methods, random patch probabilistic density method can adaptively localize focus-point regions without predetermining the number of clusters. The proposed method was compared with the k-means and fuzzy c-means clustering methods. Our proposed method achieves a good performance, when the results were evaluated by three expert pathologists. The proposed method achieves an average false-positive rate of 0.84% for the focus-point region localization error. Moreover, regarding RPPD used to localize tissue from whole-slide images, 228 whole-slide images have been tested; 97.3% localization accuracy was achieved.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  14. Fulltext Modeling of hemoglobin in dengue fever and dengue hemorrhagic fever using bioelectrical impedance
    Ibrahim F, Ismail NA, Taib MN, Wan Abas WA
    Physiol Meas, 2004 Jun;25(3):607-15.
    PMID: 15253113 DOI: 10.1088/0967-3334/25/3/002
    This paper describes a model for predicting hemoglobin (Hb) by using bioelectrical impedance analysis (BIA) in dengue patients in the Hospital Universiti Kebangsaan Malaysia (HUKM). Bioelectrical impedance measurements were conducted on 83 (47 males and 36 females) serologically confirmed dengue fever (DF) and dengue hemorrhagic fever (DHF) patients during their hospitalization. The predictive equation for Hb was derived using multivariate analysis. We investigated all the parameters in BIA, patients' symptom and demographic data. In this developed model, four predictors (reactance (XC), sex, weight and vomiting) were found to be the best predictive factors for modeling Hb in dengue patients. However, the model can only explain approximately 42% of the variation in Hb status, thus single frequency bio-impedance stand-alone technique is insufficient to monitor Hb for the DF and DHF patients. Further investigation using multi-frequency BIA is recommended in modeling Hb to achieve the most parsimonious model.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  15. Automated diagnosis of congestive heart failure using dual tree complex wavelet transform and statistical features extracted from 2s of ECG signals
    Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al.
    Comput Biol Med, 2017 04 01;83:48-58.
    PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019
    Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  16. Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?
    Ali Z, Alsulaiman M, Muhammad G, Elamvazuthi I, Al-Nasheri A, Mesallam TA, et al.
    J Voice, 2017 May;31(3):386.e1-386.e8.
    PMID: 27745756 DOI: 10.1016/j.jvoice.2016.09.009
    A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  17. Fusion of spatial gray level dependency and fractal texture features for the characterization of thyroid lesions
    Raghavendra U, Rajendra Acharya U, Gudigar A, Hong Tan J, Fujita H, Hagiwara Y, et al.
    Ultrasonics, 2017 05;77:110-120.
    PMID: 28219805 DOI: 10.1016/j.ultras.2017.02.003
    Thyroid is a small gland situated at the anterior side of the neck and one of the largest glands of the endocrine system. The abrupt cell growth or malignancy in the thyroid gland may cause thyroid cancer. Ultrasound images distinctly represent benign and malignant lesions, but accuracy may be poor due to subjective interpretation. Computer Aided Diagnosis (CAD) can minimize the errors created due to subjective interpretation and assists to make fast accurate diagnosis. In this work, fusion of Spatial Gray Level Dependence Features (SGLDF) and fractal textures are used to decipher the intrinsic structure of benign and malignant thyroid lesions. These features are subjected to graph based Marginal Fisher Analysis (MFA) to reduce the number of features. The reduced features are subjected to various ranking methods and classifiers. We have achieved an average accuracy, sensitivity and specificity of 97.52%, 90.32% and 98.57% respectively using Support Vector Machine (SVM) classifier. The achieved maximum Area Under Curve (AUC) is 0.9445. Finally, Thyroid Clinical Risk Index (TCRI) a single number is developed using two MFA features to discriminate the two classes. This prototype system is ready to be tested with huge diverse database.
    Matched MeSH terms: Diagnosis, Computer-Assisted*
  18. Fulltext Multistage Optimization Using a Modified Gaussian Mixture Model in Sperm Motility Tracking
    Alameri M, Hasikin K, Kadri NA, Nasir NFM, Mohandas P, Anni JS, et al.
    Comput Math Methods Med, 2021;2021:6953593.
    PMID: 34497665 DOI: 10.1155/2021/6953593
    Infertility is a condition whereby pregnancy does not occur despite having unprotected sexual intercourse for at least one year. The main reason could originate from either the male or the female, and sometimes, both contribute to the fertility disorder. For the male, sperm disorder was found to be the most common reason for infertility. In this paper, we proposed male infertility analysis based on automated sperm motility tracking. The proposed method worked in multistages, where the first stage focused on the sperm detection process using an improved Gaussian Mixture Model. A new optimization protocol was proposed to accurately detect the motile sperms prior to the sperm tracking process. Since the optimization protocol was imposed in the proposed system, the sperm tracking and velocity estimation processes are improved. The proposed method attained the highest average accuracy, sensitivity, and specificity of 92.3%, 96.3%, and 72.4%, respectively, when tested on 10 different samples. Our proposed method depicted better sperm detection quality when qualitatively observed as compared to other state-of-the-art techniques.
    Matched MeSH terms: Diagnosis, Computer-Assisted/statistics & numerical data
  19. Fulltext Curvelet based automatic segmentation of supraspinatus tendon from ultrasound image: a focused assistive diagnostic method
    Gupta R, Elamvazuthi I, Dass SC, Faye I, Vasant P, George J, et al.
    Biomed Eng Online, 2014;13:157.
    PMID: 25471386 DOI: 10.1186/1475-925X-13-157
    Disorders of rotator cuff tendons results in acute pain limiting the normal range of motion for shoulder. Of all the tendons in rotator cuff, supraspinatus (SSP) tendon is affected first of any pathological changes. Diagnosis of SSP tendon using ultrasound is considered to be operator dependent with its accuracy being related to operator's level of experience.
    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
  20. An EEG-based functional connectivity measure for automatic detection of alcohol use disorder
    Mumtaz W, Saad MNBM, Kamel N, Ali SSA, Malik AS
    Artif Intell Med, 2018 01;84:79-89.
    PMID: 29169647 DOI: 10.1016/j.artmed.2017.11.002
    BACKGROUND: The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics.

    METHOD: In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used.

    RESULTS: The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95.

    CONCLUSION: The SL features could be utilized as objective markers to screen the AUD patients and healthy controls.

    Matched MeSH terms: Diagnosis, Computer-Assisted/methods*
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