Design of a Fast Learning Classifier for Sleep Apnea Database based on Fuzzy SVM

Lee, SooYong; Urtnasan, Erdenebayar; Lee, Kyoung-Joung

doi:10.5391/ijfis.2017.17.3.187

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…SVM has been shown to provide higher performance than traditional learning machines and has been introduced as a powerful tool for solving classification problems. However, there are some limitations, including outlier handling, long learning times, and an increased number of support vectors (Lee et al, 2017). With the assistance of SVMs, one can perform both linear as well as non-linear classification (Goudjil et al, 2018;Liu et al, 2012).…”

Section: Methodsmentioning

confidence: 99%

Intelligent Control System of a Real Industrial Process

ZERMANE¹,

Hassina²

2022

EPSTEM

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Industrial systems are difficult to control and supervise efficiently because of the complexity of the production process. The aim is to automatically control in real-time as an alternative for operators as possible and highlight the importance of machine learning in the field of industry. Integrating SVM into the industrial supervision system in the cement factory (SCIMAT) permits the classification of different measurements coming from sensors to the Programmable Logic Controller (PLC) that indicates when the process is in good functioning or bad indicating that a default has occurred. These measurements are classified after training in three classes of level (low, medium, and high) that are classified in their turn into two classes (good and bad functioning). The three classes present the inputs of the fuzzy controllers. Based on this classification, the PLC makes orders for industrial equipment. Then a regression of variation of measurements in real-time is carried out to predict the good or the bad functioning of the production line. In conclusion, the proposed approach innovates the complex supervision system to learn how to control and preserve the habitual linguistic language used by operators, react in the right way, and prevent critical situations.

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Section: Methodsmentioning

confidence: 99%

Intelligent Control System of a Real Industrial Process

ZERMANE¹,

Hassina²

2022

EPSTEM

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“…This allows Catboost to better utilize the information from categorical features and improve model accuracy. In addition to handling categorical features, Catboost also introduces a technique called Ordered Target Statistics to address overfitting [10]. This technique sorts the training data based on the values of the objective function and computes cumulative statistical information of the sorted objective function values.…”

Section: Analysis Of Catboost Algorithm Principlesmentioning

confidence: 99%

Exploring the influence of lifestyle on sleep health based on deep learning

Duan,

He,

Wang

2024

ACE

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Sleep plays a crucial role in maintaining overall health. However, various lifestyle factors significantly influence sleep quality and duration. Understanding the relationship between lifestyle choices and sleep health is crucial for individuals seeking to improve their sleep patterns. The purpose of this study is to provide valuable insights into the causes and effects of sleep disorders in order to help individuals make informed decisions to optimize their sleep health. This article implements the CatBoost gradient algorithm for predictive modeling. Among various models including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Xtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Deep Neural Network (DNN), CatBoost shows better overall performance with an accuracy of 0.93, an Fl-score of 0.925, and a recall of 0.95. Through data analysis, Blood-pressure-Systolic, Blood-Pressure-Diastolic, and Stress Level are found to have the greatest impact on the model's output.

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“…And some other research focus on detecting the number of the apnea or hypopnea and providing a vague position of them by classify signal slices. Annotation is done on epoch of a fixed time, and a machine learning method is used to classify whether this epoch is normal, apnea or hypopnea, there are multiple classification methods that have been test in previous study such as support vector machine(SVM) 8,9 , neural network 10,11 , fuzzy logic 12,13 , linear discriminant analysis(LDA) 14 .…”

Section: Introductionmentioning

confidence: 99%

An one-dimensional signal based object detection network for apnea and hypopnea locating

Tang

Zhao²,

Shuai³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Sleep-disordered breathing (SDB), a common sleep disorder, shows symptoms of shallow breathing or paused breathing during sleep called respiratory events. SDB was conventionally diagnosed based on overnight multi-channel polysomnography (PSG) in clinical treatment. However, this process requires experienced sleep technicians to annotate and is quite labour-intensive. In this study, a novel one-dimensional signal based object detection network was proposed for automatic, high efficiency detection and classification of different kinds of respiratory events from continuous PSG signals. Our method can locate respiratory events in PSG signal data and classify them into four categories for further clinical treatment. The method was further validated on a PSG clinical dataset collected from Beijing Tongren Hospital. Precision, recall and F1-score of 84.9%, 85.1%, 85.0% were achieved for events detection with total accuracy rate reaching 74.9% in classification of detected events. The result shows that one-dimensional signal object detection is a promising method to locate the characteristic waveform and extract signal features. Such method can be applied in other signal feature detection field.

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Design of a Fast Learning Classifier for Sleep Apnea Database based on Fuzzy SVM

Cited by 8 publications

References 9 publications

Intelligent Control System of a Real Industrial Process

Intelligent Control System of a Real Industrial Process

Exploring the influence of lifestyle on sleep health based on deep learning

An one-dimensional signal based object detection network for apnea and hypopnea locating

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