RETRACTED ARTICLE: Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, Kemal

doi:10.1007/s00521-016-2617-9

Cited by 55 publications

(23 citation statements)

References 38 publications

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“…Speed and success are the most critical factors in the selection of machine methods. These choices were made according to our studies and the information given in the literature [41], [42].…”

Section: Artificial Intelligence Classification Methodsmentioning

confidence: 99%

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Topal¹,

Uçar

2019

IEEE Access

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Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as ''Turkey Tourism Year'' in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.76. Conclusions: It was observed that it is possible to find the tourists who will prefer Turkey by using the developed system. In other words, the study revealed that the countries can reach the individual instead of masses in their promotional activities.

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Section: Artificial Intelligence Classification Methodsmentioning

confidence: 99%

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Topal¹,

Uçar

2019

IEEE Access

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“…e F-score is one of the feature selection algorithms that helps distinguish classes from each other [25]. To select the feature, an F-score value (F i ) is calculated for each feature (equation 1).…”

Section: F-score Feature Selection Algorithmmentioning

confidence: 99%

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Noor

Uçar

Polat

et al. 2020

Mathematical Problems in Engineering

Self Cite

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In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

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“…In recent years, an increasing number of studies have used the characteristics of a single signal to carry out an automated diagnosis of sleep apnea events 11 . Using respiratory airflow, oxygen saturation (SpO 2 ), and photoplethysmography (PPG) alone can help identify apnea events, which have achieved many research results 12 , 13 . Although these signals have the advantage of being easily acquired, they have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Classification of sleep apnea based on EEG sub-band signal characteristics

Zhao

Wang

Yang

et al. 2021

Sci Rep

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Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

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RETRACTED ARTICLE: Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques

Cited by 55 publications

References 38 publications

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Classification of sleep apnea based on EEG sub-band signal characteristics

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