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Background:
Epilepsy (Ep) is a chronic neural disease. The diagnosis of epilepsy depends on detailed seizure history and scalp electroencephalogram (EEG) examinations. The automatic recognition of epileptic EEG is an artificial intelligence application developed from machine learning (ML).
Purpose:
This study compares the classification effects...
Context in source publication
Context 1
... we classified the EEG data by two classifiers: SVM and GBDT. In order to evaluate the classifiers, we calculated sensitivity, specificity, accuracy, precision, F1_score, and AUC value, and obtained the results shown in Table 2 and Figure 4. The result of the SVM classifier showed a sensitivity of 92.86%, specificity of 23.33%, accuracy of 72.00%, precision of 73.98%, F1_score of 82.28%, and AUC of 0.7500. ...Similar publications
One of the most prevalent brain diseases, epilepsy is characterized by recurring seizures that happen quite frequently. During seizures, a patient suffers uncontrollable muscle contractions that cause loss of motion and balance, which could lead to harm or even death. Establishing an automatic method for warning patients about impending seizures re...
Citations
... SVM is one of the machine learning methods that is easy to implement [21]. SVM is a binary classification model [26], [6]. ...
The vocalization of infants, commonly known as baby crying, represents one of the primary means by which infants effectively communicate their needs and emotional states to adults. While the act of crying can yield crucial insights into the well-being and comfort of a baby, there exists a dearth of research specifically investigating the influence of the audio range within a baby cry on research outcomes. The core problem of research is the lack of research on the influence of audio range on baby cry classification on machine learning. The purpose of this study is to ascertain the impact of the duration of an infant’s cry on the outcomes of machine learning classification and to gain knowledge regarding the accuracy of results F1 score obtained through the utilization of the machine learning method. The contribution is to enrich an understanding of the application of classification and feature selection in audio datasets, particulary in the context of baby cry audio. The utilized dataset, known as donate-a-cry-corpus, encompasses five distinct data classes and possesses a duration of seven seconds. The employed methodology consists of the spectrogram technique, cross-validation for data partitioning, MFCC feature extraction with 10, 20, and 30 coefficients, as well as machine learning models including Support Vector Machine, Random Forest, and Naïve Bayes. The findings of this study reveal that the Random Forest model achieved an accuracy of 0.844 and an F1 score of 0.773 when 10 MFCC coefficients were utilized and the optimal audio range was set at six seconds. Furthermore, the Support Vector Machine model with an RBF kernel yielded an accuracy of 0.836 and an F1 score of 0.761, while the Naïve Bayes model achieved an accuracy 0.538 and F1 score of 0.539. Notably, no discernible differences were observed when evaluating the Support Vector Machine and Naïve Bayes methods across the 1-7 second time trial. The implication of this research is to establish a foundation for the advancement of premature illness identification techniques grounded in the vocalizations of infants, thereby facilitating swifter diagnostic processes for pediatric practitioners.
... Both RMS and Line length have been employed in EEG [31] and EGG studies [14,32], attesting to their relevance and applicability in the analysis of such signals. Fractal dimensions, including PFD [26], have been extensively employed in EEG and ECG literature [52][53][54][55], indicating that they may offer valuable insights into the complexity and self-similarity of physiological signals. We direct readers with an interest in comparing various fractal dimension methodologies to consult reference [27] for a detailed examination and comparative analysis. ...
Vagus nerve stimulation (VNS) is a potential treatment option for gastrointestinal (GI) diseases. The present study aimed to understand the physiological effects of VNS on gastrointestinal (GI) function, which is crucial for developing more effective adaptive closed-loop VNS therapies for GI diseases. Electrogastrography (EGG), which measures gastric electrical activities (GEAs) as a proxy to quantify GI functions, was employed in our investigation. We introduced a recording schema that allowed us to simultaneously induce electrical VNS and record EGG. While this setup created a unique model for studying the effects of VNS on the GI function and provided an excellent testbed for designing advanced neuromodulation therapies, the resulting data was noisy, heterogeneous, and required specialized analysis tools. The current study aimed at formulating a systematic and interpretable approach to quantify the physiological effects of electrical VNS on GEAs in ferrets by using signal processing and machine learning techniques. Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. Our results indicated that the electrophysiological changes induced by VNS were optimally characterized by a distinct set of features for each classification scenario. Additionally, our findings demonstrated that the process of feature selection enhanced classification performance and facilitated representation learning.
... Furthermore, this particular algorithm utilizes a gradient approach, thereby mitigating the issue of overfitting commonly encountered in conventional decision trees. Consequently, it yields more precise and accurate classifications [5]. According to a study conducted by [6], a comparison was made among various machine learning models, including GBDT, Random Forest, Logic Regression, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbor (KNN), Neural Network, and XGBoost. ...
... The results indicated that GBDT demonstrated superior performance compared to the other models, achieving an AUC value of 0.946 and a precision of 0.778. Moreover, another study conducted by [5] compared GBDT with SVM in the context of recognizing Electroencephalography (EEG) epilepsy. The performance results demonstrated that GBDT surpassed SVM with an accuracy difference of 8%. ...
Coronary artery disease, a prevalent type of cardiovascular disease, is a significant contributor to premature mortality globally. Employing the classification of coronary artery disease as an early detection measure can have a substantial impact on reducing death rates caused by this ailment. To investigate this, the Z-Alizadeh dataset, consisting of clinical data from patients afflicted with coronary artery disease, was utilized, encompassing a total of 303 data points that comprise 55 predictive attribute features and 1 target attribute feature. For the purpose of classification, the Gradient Boosting Decision Tree (GBDT) algorithm was chosen, and in addition, a metaheuristic algorithm called monarch butterfly optimization (MBO) was implemented to diminish the number of features. The objective of this study is to compare the performance of GBDT before and after the application of MBO for feature selection. The evaluation of the study's findings involved the utilization of a confusion matrix and the calculation of the area under the curve (AUC). The outcomes demonstrated that GBDT initially attained an accuracy rate of 87.46%, a precision of 83.85%, a recall of 70.37%, and an AUC of 82.09%. Subsequent to the implementation of MBO, the performance of GBDT improved to an accuracy of 90.26%, a precision of 86.82%, a recall of 80.79%, and an AUC of 87.33% with the selection of 31 features. This improvement in performance leads to the conclusion that MBO effectively addresses the feature selection issue within this particular context.
... The Gradient Boosting classifier is a prevalent classification algorithm based on decision tree. This classifier has been widely utilized in EEG signal recognition [7,22] because of the advantages it has in reducing the over-fitting problem and has high prediction accuracy. The workflow of the Gradient Boosting classifier is shown in Figure 6. ...
Electroencephalogram (EEG) signals are the gold standard tool for detecting epileptic seizures. Long-term EEG signal monitoring is a promising method to realize real-time and automatic epilepsy detection with the assistance of computer-aided techniques and the Internet of Medical Things (IoMT) devices. Machine learning (ML) algorithms combined with advanced feature extraction methods have been widely explored to precisely recognize EEG signals, while among which, little attention has been paid to high computing costs and severe information losses. The lack of model interpretability also impedes the wider application and deeper understanding of ML methods in epilepsy detection. In this research, a novel feature extraction method based on an autoencoder (AE) is proposed in the time domain. The architecture and mechanism are elaborated. In this method, specified features are defined and calculated on the basis of signal reconstruction quantification of the AE. The EEG recognition is performed to validate the effectiveness of the proposed detection method, and the prediction accuracy reached 97%. To further investigate the superiority of the proposed AE-based feature extraction method, a widely used feature extraction method, PCA, is allocated for comparison. In order to understand the underlying working mechanism, permutation importance and SHapley Additive exPlanations (SHAP) are conducted for model interpretability, and the results further confirm the reasonability and effectiveness of the extracted features by AE reconstruction. With high computing efficiency in the time domain and an extensively satisfactory accuracy, the proposed epilepsy detection method exhibits great superiority and potential in almost real-time and automatic epilepsy monitoring.
