Displaying publications 1 - 20 of 156 in total

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  1. Yasmin S, Pathan RK, Biswas M, Khandaker MU, Faruque MRI
    Sensors (Basel), 2020 Sep 21;20(18).
    PMID: 32967087 DOI: 10.3390/s20185391
    Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER's critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn-Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.
    Matched MeSH terms: Support Vector Machine*
  2. Abdulhussain SH, Mahmmod BM, Naser MA, Alsabah MQ, Ali R, Al-Haddad SAR
    Sensors (Basel), 2021 Mar 12;21(6).
    PMID: 33808986 DOI: 10.3390/s21061999
    Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential application in more realistic noise environments. Therefore, finding a feasible and accurate handwritten numeral recognition method that is accurate in the more practical noisy environment is crucial. To this end, this paper proposes a new scheme for handwritten numeral recognition using Hybrid orthogonal polynomials. Gradient and smoothed features are extracted using the hybrid orthogonal polynomial. To reduce the complexity of feature extraction, the embedded image kernel technique has been adopted. In addition, support vector machine is used to classify the extracted features for the different numerals. The proposed scheme is evaluated under three different numeral recognition datasets: Roman, Arabic, and Devanagari. We compare the accuracy of the proposed numeral recognition method with the accuracy achieved by the state-of-the-art recognition methods. In addition, we compare the proposed method with the most updated method of a convolutional neural network. The results show that the proposed method achieves almost the highest recognition accuracy in comparison with the existing recognition methods in all the scenarios considered. Importantly, the results demonstrate that the proposed method is robust against the noise distortion and outperforms the convolutional neural network considerably, which signifies the feasibility and the effectiveness of the proposed approach in comparison to the state-of-the-art recognition methods under both clean noise and more realistic noise environments.
    Matched MeSH terms: Support Vector Machine
  3. Abdulameer MH, Sheikh Abdullah SN, Othman ZA
    ScientificWorldJournal, 2014;2014:835607.
    PMID: 24790584 DOI: 10.1155/2014/835607
    Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.
    Matched MeSH terms: Support Vector Machine*
  4. Hussein AF, Hashim SJ, Rokhani FZ, Wan Adnan WA
    Sensors (Basel), 2021 Mar 26;21(7).
    PMID: 33810211 DOI: 10.3390/s21072311
    Cardiovascular Disease (CVD) is a primary cause of heart problems such as angina and myocardial ischemia. The detection of the stage of CVD is vital for the prevention of medical complications related to the heart, as they can lead to heart muscle death (known as myocardial infarction). The electrocardiogram (ECG) reflects these cardiac condition changes as electrical signals. However, an accurate interpretation of these waveforms still calls for the expertise of an experienced cardiologist. Several algorithms have been developed to overcome issues in this area. In this study, a new scheme for myocardial ischemia detection with multi-lead long-interval ECG is proposed. This scheme involves an observation of the changes in ischemic-related ECG components (ST segment and PR segment) by way of the Choi-Williams time-frequency distribution to extract ST and PR features. These extracted features are mapped to a multi-class SVM classifier for training in the detection of unknown conditions to determine if they are normal or ischemic. The use of multi-lead ECG for classification and 1 min intervals instead of beats or frames contributes to improved detection performance. The classification process uses the data of 92 normal and 266 patients from four different databases. The proposed scheme delivered an overall result with 99.09% accuracy, 99.49% sensitivity, and 98.44% specificity. The high degree of classification accuracy for the different and unknown data sources used in this study reflects the flexibility, validity, and reliability of this proposed scheme. Additionally, this scheme can assist cardiologists in detecting signal abnormality with robustness and precision, and can even be used for home screening systems to provide rapid evaluation in emergency cases.
    Matched MeSH terms: Support Vector Machine*
  5. Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah JM
    Sensors (Basel), 2020 Sep 14;20(18).
    PMID: 32937801 DOI: 10.3390/s20185234
    Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.
    Matched MeSH terms: Support Vector Machine*
  6. Hag A, Handayani D, Pillai T, Mantoro T, Kit MH, Al-Shargie F
    Sensors (Basel), 2021 Sep 20;21(18).
    PMID: 34577505 DOI: 10.3390/s21186300
    Exposure to mental stress for long period leads to serious accidents and health problems. To avoid negative consequences on health and safety, it is very important to detect mental stress at its early stages, i.e., when it is still limited to acute or episodic stress. In this study, we developed an experimental protocol to induce two different levels of stress by utilizing a mental arithmetic task with time pressure and negative feedback as the stressors. We assessed the levels of stress on 22 healthy subjects using frontal electroencephalogram (EEG) signals, salivary alpha-amylase level (AAL), and multiple machine learning (ML) classifiers. The EEG signals were analyzed using a fusion of functional connectivity networks estimated by the Phase Locking Value (PLV) and temporal and spectral domain features. A total of 210 different features were extracted from all domains. Only the optimum multi-domain features were used for classification. We then quantified stress levels using statistical analysis and seven ML classifiers. Our result showed that the AAL level was significantly increased (p < 0.01) under stress condition in all subjects. Likewise, the functional connectivity network demonstrated a significant decrease under stress, p < 0.05. Moreover, we achieved the highest stress classification accuracy of 93.2% using the Support Vector Machine (SVM) classifier. Other classifiers produced relatively similar results.
    Matched MeSH terms: Support Vector Machine*
  7. Sahran S, Albashish D, Abdullah A, Shukor NA, Hayati Md Pauzi S
    Artif Intell Med, 2018 05;87:78-90.
    PMID: 29680688 DOI: 10.1016/j.artmed.2018.04.002
    OBJECTIVE: Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components.

    METHODOLOGY: We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC.

    RESULTS: We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods.

    CONCLUSION: We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods.

    Matched MeSH terms: Support Vector Machine*
  8. Kasaraneni PP, Venkata Pavan Kumar Y, Moganti GLK, Kannan R
    Sensors (Basel), 2022 Nov 30;22(23).
    PMID: 36502025 DOI: 10.3390/s22239323
    Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes' energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers' performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier "RF+SVM+DT" has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.
    Matched MeSH terms: Support Vector Machine*
  9. Waqas S, Harun NY, Arshad U, Laziz AM, Sow Mun SL, Bilad MR, et al.
    Chemosphere, 2024 Feb;349:140830.
    PMID: 38056711 DOI: 10.1016/j.chemosphere.2023.140830
    Membrane fouling is a critical bottleneck to the widespread adoption of membrane separation processes. It diminishes the membrane permeability and results in high operational energy costs. The current study presents optimizing the operating parameters of a novel rotating biological contactor (RBC) integrated with an external membrane (RBC + ME) that combines membrane technology with an RBC. In the RBC + ME, the membrane panel is placed external to the bioreactor. Response surface methodology (RSM) is applied to optimize the membrane permeability through three operating parameters (hydraulic retention time (HRT), rotational disk speed, and sludge retention time (SRT)). The artificial neural networks (ANN) and support vector machine (SVM) are implemented to depict the statistical modelling approach using experimental data sets. The results showed that all three operating parameters contribute significantly to the performance of the bioreactor. RSM revealed an optimum value of 40.7 rpm disk rotational speed, 18 h HRT and 12.4 d SRT, respectively. An ANN model with ten hidden layers provides the highest R2 value, while the SVM model with the Bayesian optimizer provides the highest R2. RSM, ANN, and SVM models reveal the highest R-square values of 0.97, 0.99, and 0.99, respectively. Machine learning techniques help predict the model based on the experimental results and training data sets.
    Matched MeSH terms: Support Vector Machine*
  10. Kumar, Yogan Jaya, Naomie Salim, Ahmed Hamza Osman, Abuobieda, Albaraa
    MyJurnal
    Cross-document Structure Theory (CST) has recently been proposed to facilitate tasks related to multidocument analysis. Classifying and identifying the CST relationships between sentences across topically related documents have since been proven as necessary. However, there have not been sufficient studies presented in literature to automatically identify these CST relationships. In this study, a supervised machine learning technique, i.e. Support Vector Machines (SVMs), was applied to identify four types of CST relationships, namely “Identity”, “Overlap”, “Subsumption”, and “Description” on the datasets obtained from CSTBank corpus. The performance of the SVMs classification was measured using Precision, Recall and F-measure. In addition, the results obtained using SVMs were also compared with those from the previous literature using boosting classification algorithm. It was found that SVMs yielded better results in classifying the four CST relationships.
    Matched MeSH terms: Support Vector Machine
  11. Muhammad Aizuddin Ahmad, Kamaruddin, N.K., Muhamad Kamal Mohammed Amin
    MyJurnal
    Computer vision is applied in many software and devices. The detection and
    reconstruction of the human skeletal structure is one of area of interest, where the
    camera will identify the human parts and construct the joints of the person standing in
    front. Three-dimensional pose estimation is solved using various learning approaches,
    such as Support Vector Machines and Gaussian processes. However, difficulties in
    cluttered scenarios are encountered, and require additional input data, such as
    silhouettes, or controlled camera settings. The paper focused on estimating the threedimensional
    pose of a person without requiring background information, which is
    robust to camera variations. Each of the joint has three-dimensional space position and
    matrix orientation with respect to the sensor. Matlab Simulink was utilized to provide
    communication tools with depth camera using Kinect device for skeletal detection.
    Results on the skeletal detection using Kinect sensor is analysed in measuring the
    abilities to detect skeletal structure accurately, and it is shown that the system is able
    to detect human skeletal performing non-complex basic motions in daily life.
    Matched MeSH terms: Support Vector Machine
  12. Ashwaq Qasem, Siti Norul Huda Sheikh Abdullah, Shahnorbanun Sahran, Rizuana Iqbal Hussain, Fuad Ismail
    MyJurnal
    The false positive (FP) is an over-segment result where the noncancerous pixel is segmented as a cancer pixel. The FP rate is considered a challenge in localising masses in mammogram images. Hence, in this article, a rejection model is proposed by using a supervised learning method in mass classification such as support vector machine (SVM). The goal of the rejection model which is based on SVM is the reduction of FP rate in segmenting mammogram through the Chan-Vese method, which is initialised by the marker controller watershed (MCWS) algorithm. The MCWS algorithm is utilised for segmentation of a mammogram image. The segmentation is subsequently refined through the Chan-Vese method, followed by the development of the proposed SVM rejection model with different window size as well as its application in eliminating incorrect segmented nodules. The dataset comprised of 57 nodules and 113 non-nodules and the study successfully proved the effectiveness of the SVM rejection model to decrease the FP rate.
    Matched MeSH terms: Support Vector Machine
  13. Nurfadhlina Mohd Sharef, Rozilah Rosli
    MyJurnal
    Sentiment analysis classification has been typically performed by combining features that represent the dataset at hand. Existing works have employed various features individually such as the syntactical, lexical and machine learning, and some have hybridized to reach optimistic results. Since the debate on the best combination is still unresolved this paper addresses the empirical investigation of the combination of features for product review classification. Results indicate the Support Vector Machine classification model combined with any of the observed lexicon namely MPQA, BingLiu and General Inquirer and either the unigram or inte-gration of unigram and bigram features is the top performer.
    Matched MeSH terms: Support Vector Machine
  14. Nurbaity Sabri, Noor Hazira Yusof, Zaidah Ibrahim, Zolidah Kasiran, Nur Nabilah Abu Mangshor
    Scientific Research Journal, 2017;14(2):49-62.
    MyJurnal
    Text localisation determines the location of the text in an image. This process
    is performed prior to text recognition. Localising text on shop signage is
    a challenging task since the images of the shop signage consist of complex
    background, and the text occurs in various font types, sizes, and colours.
    Two popular texture features that have been applied to localise text in
    scene images are a histogram of oriented gradient (HOG) and speeded up
    robust features (SURF). A comparative study is conducted in this paper
    to determine which is better with support vector machine (SVM) classifier.
    The performance of SVM is influenced by its kernel function and another
    comparative study is conducted to identify the best kernel function. The
    experiments have been conducted using primary data collected by the
    authors. Results indicate that HOG with quadratic kernel function localises
    text for shop signage better than SURF.
    Matched MeSH terms: Support Vector Machine
  15. Saeed, Sana, Ong, Hong Choon
    MyJurnal
    Support vector machine (SVM) is one of the most popular algorithms in machine learning
    and data mining. However, its reduced efficiency is usually observed for imbalanced
    datasets. To improve the performance of SVM for binary imbalanced datasets, a new scheme
    based on oversampling and the hybrid algorithm were introduced. Besides the use of a
    single kernel function, SVM was applied with multiple kernel learning (MKL). A weighted
    linear combination was defined based on the linear kernel function, radial basis function
    (RBF kernel), and sigmoid kernel function for MKL. By generating the synthetic samples
    in the minority class, searching the best choices of the SVM parameters and identifying
    the weights of MKL by minimizing the objective function, the improved performance of
    SVM was observed. To prove the strength of the proposed scheme, an experimental study,
    including noisy borderline and real imbalanced datasets was conducted. SVM was applied
    with linear kernel function, RBF kernel, sigmoid kernel function and MKL on all datasets.
    The performance of SVM with all kernel functions was evaluated by using sensitivity,
    G Mean, and F measure. A significantly improved performance of SVM with MKL was
    observed by applying the proposed scheme.
    Matched MeSH terms: Support Vector Machine
  16. Razieh Shojanoori, Helmi Z.m. Shafri, Shattri Mansor, Mohd Hasmadi Ismail
    Sains Malaysiana, 2016;45:1025-1034.
    The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management
    issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling
    their growth and decline. This study focused on the detection of urban tree species by considering three types of tree
    species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these
    three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of
    maximum likelihood classification and support vector machines. These methods were then compared with object-based
    image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color
    attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated
    that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy.
    Matched MeSH terms: Support Vector Machine
  17. Singhania U, Tripathy B, Hasan MK, Anumbe NC, Alboaneen D, Ahmed FRA, et al.
    Front Public Health, 2021;9:751536.
    PMID: 34708019 DOI: 10.3389/fpubh.2021.751536
    Alzheimer's Disease (AD) is a neurodegenerative irreversible brain disorder that gradually wipes out the memory, thinking skills and eventually the ability to carry out day-to-day tasks. The amount of AD patients is rapidly increasing due to several lifestyle changes that affect biological functions. Detection of AD at its early stages helps in the treatment of patients. In this paper, a predictive and preventive model that uses biomarkers such as the amyloid-beta protein is proposed to detect, predict, and prevent AD onset. A Convolution Neural Network (CNN) based model is developed to predict AD at its early stages. The results obtained proved that the proposed model outperforms the traditional Machine Learning (ML) algorithms such as Logistic Regression, Support Vector Machine, Decision Tree Classifier, and K Nearest Neighbor algorithms.
    Matched MeSH terms: Support Vector Machine
  18. Khade S, Gite S, Thepade SD, Pradhan B, Alamri A
    Sensors (Basel), 2021 Nov 08;21(21).
    PMID: 34770715 DOI: 10.3390/s21217408
    Iris biometric detection provides contactless authentication, preventing the spread of COVID-19-like contagious diseases. However, these systems are prone to spoofing attacks attempted with the help of contact lenses, replayed video, and print attacks, making them vulnerable and unsafe. This paper proposes the iris liveness detection (ILD) method to mitigate spoofing attacks, taking global-level features of Thepade's sorted block truncation coding (TSBTC) and local-level features of the gray-level co-occurrence matrix (GLCM) of the iris image. Thepade's SBTC extracts global color texture content as features, and GLCM extracts local fine-texture details. The fusion of global and local content presentation may help distinguish between live and non-live iris samples. The fusion of Thepade's SBTC with GLCM features is considered in experimental validations of the proposed method. The features are used to train nine assorted machine learning classifiers, including naïve Bayes (NB), decision tree (J48), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), and ensembles (SVM + RF + NB, SVM + RF + RT, RF + SVM + MLP, J48 + RF + MLP) for ILD. Accuracy, precision, recall, and F-measure are used to evaluate the performance of the projected ILD variants. The experimentation was carried out on four standard benchmark datasets, and our proposed model showed improved results with the feature fusion approach. The proposed fusion approach gave 99.68% accuracy using the RF + J48 + MLP ensemble of classifiers, immediately followed by the RF algorithm, which gave 95.57%. The better capability of iris liveness detection will improve human-computer interaction and security in the cyber-physical space by improving person validation.
    Matched MeSH terms: Support Vector Machine
  19. Hui KH, Ooi CS, Lim MH, Leong MS, Al-Obaidi SM
    PLoS One, 2017;12(12):e0189143.
    PMID: 29261689 DOI: 10.1371/journal.pone.0189143
    A major issue of machinery fault diagnosis using vibration signals is that it is over-reliant on personnel knowledge and experience in interpreting the signal. Thus, machine learning has been adapted for machinery fault diagnosis. The quantity and quality of the input features, however, influence the fault classification performance. Feature selection plays a vital role in selecting the most representative feature subset for the machine learning algorithm. In contrast, the trade-off relationship between capability when selecting the best feature subset and computational effort is inevitable in the wrapper-based feature selection (WFS) method. This paper proposes an improved WFS technique before integration with a support vector machine (SVM) model classifier as a complete fault diagnosis system for a rolling element bearing case study. The bearing vibration dataset made available by the Case Western Reserve University Bearing Data Centre was executed using the proposed WFS and its performance has been analysed and discussed. The results reveal that the proposed WFS secures the best feature subset with a lower computational effort by eliminating the redundancy of re-evaluation. The proposed WFS has therefore been found to be capable and efficient to carry out feature selection tasks.
    Matched MeSH terms: Support Vector Machine
  20. Gul MU, Kamarul Azman MH, Kadir KA, Shah JA, Hussen S
    Comput Intell Neurosci, 2023;2023:8162325.
    PMID: 36909967 DOI: 10.1155/2023/8162325
    Atrial flutter (AFL) is a common arrhythmia with two significant mechanisms, namely, focal (FAFL) and macroreentry (MAFL). Discrimination of the AFL mechanism through noninvasive techniques can improve radiofrequency ablation efficacy. This study aims to differentiate the AFL mechanism using a 12-lead surface electrocardiogram. P-P interval series variability is hypothesized to be different in FAFL and MAFL and may be useful for discrimination. 12-lead ECG signals were collected from 46 patients with known AFL mechanisms. Features for a proposed classifier are extracted through descriptive statistics of the interval series. On the other hand, the class ratio of MAFL and FAFL was 41 : 5, respectively, which was highly imbalanced. To resolve this, different data augmentation techniques (SMOTE, modified-SMOTE, and smoothed-bootstrap) have been applied on the interval series to generate synthetic interval series and minimize imbalance. Modification is introduced in the classic SMOTE technique (modified-SMOTE) to properly produce data samples from the original distribution. The characteristics of modified-SMOTE are found closer to the original dataset than the other two techniques based on the four validation criteria. The performance of the proposed model has been evaluated by three linear classifiers, namely, linear discriminant analysis (LDA), logistic regression (LOG), and support vector machine (SVM). Filter and wrapper methods have been used for selecting relevant features. The best average performance was achieved at 400% augmentation of the FAFL interval series (90.24% sensitivity, 49.50% specificity, and 76.88% accuracy) in the LOG classifier. The variation of consecutive P-wave intervals has been shown as an effective concept that differentiates FAFL from MAFL through the 12-lead surface ECG.
    Matched MeSH terms: Support Vector Machine
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