Journal article
DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals
Knowledge-Based Systems, Vol.300, pp.1-13
2024
Abstract
Hunger is a physiological state that arises from complex interactions of multiple factors, including higher brain center control. We purposed to develop an accurate and efficient machine-learning model for the automated detection of hunger using EEG signals. We prospectively acquired 14-channel EEG (sampling frequency 128 Hz) from 43 and 48 fasted and post-prandial healthy subjects (hungry vs. control groups, respectively) using the EMOTIV EPOC+ mobile brain cap system. To augment the hunger response, fasted subjects were also shown video images of food during EEG recording. The EEG signals were divided into 15-second segments. 877 and 852 participants/subjects were in the hungry and control groups. We created a novel handcrafted architecture—decomposed shifted window (DSWIN)—that combined swin patch division with tunable Q-factor wavelet transform-based signal decomposition for multilevel feature extraction of EEG signals. Textural and statistical features were extracted from the multiple patches and decomposed signals using a novel penta pattern-based extractor and statistical moments, respectively, and then merged. Iterative neighborhood component analysis (INCA) and iterative ReliefF (IRF) were applied. Twenty-eight selected feature vectors were generated, which were then fed to a shallow k-nearest neighbors (kNN) classifier to calculate channel-wise prediction vectors. From the 28 channel-wise prediction vectors, another 26 modes of function-based voted results were calculated using iterative hard majority voting, and the best overall model result was selected using a greedy algorithm. Our model attained 99.54% and 82.71% binary classification accuracies of hungry status vs. control using 10-fold and leave-one-subject-out cross-validations, respectively.
Details
- Title
- DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals
- Authors
- Serkan Kirik - Elazığ Eğitim ve Araştırma HastanesiIrem Tasci - Fırat UniversityPrabal D Barua - University of Southern QueenslandArif Metehan Yildiz - Fırat UniversityTugce Keles - Fırat UniversityMehmet Baygin - Erzurum Technical UniversityIlknur TuncerSengul Dogan (Corresponding Author)Turker Tuncer - Fırat UniversityAruna Devi (Author) - University of the Sunshine Coast, Queensland, School of Education and Tertiary AccessRu-San Tan - National Heart Centre SingaporeU. Rajendra Acharya - University of Southern Queensland
- Publication details
- Knowledge-Based Systems, Vol.300, pp.1-13
- Publisher
- Elsevier BV
- Date published
- 2024
- DOI
- 10.1016/j.knosys.2024.112150
- ISSN
- 1872-7409
- Data Availability
- The data is not publicly available due to restrictions regarding the Ethical Committee Institution.
- Organisation Unit
- Indigenous and Transcultural Research Centre; School of Education and Tertiary Access
- Language
- English
- Record Identifier
- 991048598302621
- Output Type
- Journal article
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- Computer Science, Artificial Intelligence
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