DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals
Article
Kirik, Serkan, Tasci, Irem, Barua, Prabal D., Yildiz, Arif Metehan, Keles, Tugce, Baygin, Mehmet, Tuncer, Ilknur, Dogan, Sengul, Tuncer, Turker, Devi, Aruna, Tan, Ru-San and Acharya, U.R.. 2024. "DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals." Knowledge-Based Systems. 300. https://doi.org/10.1016/j.knosys.2024.112150
| Article Title | DSWIN: Automated hunger detection model based on hand-crafted decomposed shifted windows architecture using EEG signals |
|---|---|
| ERA Journal ID | 18062 |
| Article Category | Article |
| Authors | Kirik, Serkan, Tasci, Irem, Barua, Prabal D., Yildiz, Arif Metehan, Keles, Tugce, Baygin, Mehmet, Tuncer, Ilknur, Dogan, Sengul, Tuncer, Turker, Devi, Aruna, Tan, Ru-San and Acharya, U.R. |
| Journal Title | Knowledge-Based Systems |
| Journal Citation | 300 |
| Article Number | 112150 |
| Number of Pages | 13 |
| Year | 2024 |
| Publisher | Elsevier |
| ISSN | 0950-7051 |
| 1872-7409 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2024.112150 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0950705124007846 |
| 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. |
| Keywords | DSWIN architecture; EEG; Hunger detection ; Penta pattern ; Neuroscience |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkey |
| Firat University, Turkey | |
| School of Business | |
| Erzurum Technical University, Turkey | |
| Interior Ministry, Turkiye | |
| University of the Sunshine Coast | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical Centre, Singapore | |
| School of Mathematics, Physics and Computing | |
| Centre for Health Research |
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