التفاصيل البيبلوغرافية
| العنوان: |
A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network Architecture. |
| المؤلفون: |
Niknazar H, Mednick SC |
| المصدر: |
IEEE transactions on pattern analysis and machine intelligence [IEEE Trans Pattern Anal Mach Intell] 2024 Jul; Vol. 46 (7), pp. 5044-5061. Date of Electronic Publication: 2024 Jun 05. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: IEEE Computer Society Country of Publication: United States NLM ID: 9885960 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1939-3539 (Electronic) Linking ISSN: 00985589 NLM ISO Abbreviation: IEEE Trans Pattern Anal Mach Intell Subsets: MEDLINE |
| أسماء مطبوعة: |
Original Publication: [New York] IEEE Computer Society. |
| مواضيع طبية MeSH: |
Electroencephalography* , Deep Learning* , Sleep Stages*/physiology , Polysomnography* , Signal Processing, Computer-Assisted*, Humans ; Algorithms ; Adult ; Male ; Female ; Neural Networks, Computer ; Young Adult |
| مستخلص: |
In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer guided by a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from microstructure of EEG signals, such as trained kernels and effect of each kernel on the detected stages, to macrostructures, such as transitions between stages. The proposed system demonstrated greater performance than prior studies and the system learned information consistent with expert knowledge. |
| تواريخ الأحداث: |
Date Created: 20240215 Date Completed: 20240605 Latest Revision: 20240606 |
| رمز التحديث: |
20240606 |
| DOI: |
10.1109/TPAMI.2024.3366170 |
| PMID: |
38358869 |
| قاعدة البيانات: |
MEDLINE |
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