Implementing deep learning on edge devices for snoring detection and reduction.
| Title: | Implementing deep learning on edge devices for snoring detection and reduction. |
|---|---|
| Authors: | Dinh NN; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam.; Bach NC; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam.; Bach TV; Phan Boi Chau High School for the Gifted, Nghean City, 460000, Viet Nam.; Nguyet Chi DT; VNU-HUS, High School for the Gifted, Hanoi, 100000, Viet Nam.; Cuong DD; Vietnam National University, Hanoi, 100000, Viet Nam; Thai Nguyen University of Technology, Thainguyen City, 250000, Viet Nam.; Dat NT; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam.; Kien DT; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam.; Phuong NT; Vietnam National University, Hanoi, 100000, Viet Nam.; Thao LQ; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam. Electronic address: thaolq@hus.edu.vn.; Thien ND; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam.; Thuy DTT; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam.; Minh Thuy LT; Faculty of Physics, VNU University of Science, Hanoi, 100000, Viet Nam; Vietnam National University, Hanoi, 100000, Viet Nam. |
| Source: | Computers in biology and medicine [Comput Biol Med] 2025 Jan; Vol. 184, pp. 109458. Date of Electronic Publication: 2024 Nov 22. |
| Publication Type: | Journal Article |
| Language: | English |
| Journal Info: | Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE |
| Imprint Name(s): | Publication: New York : Elsevier; Original Publication: New York, Pergamon Press. |
| MeSH Terms: | Snoring*/physiopathology ; Deep Learning*; Humans ; Neural Networks, Computer ; Male |
| Abstract: | This study introduces MinSnore, a novel deep learning model tailored for real-time snoring detection and reduction, specifically designed for deployment on low-configuration edge devices. By integrating MobileViTV3 blocks into the Dynamic MobileNetV3 backbone model architecture, MinSnore leverages both Convolutional Neural Networks (CNNs) and transformers to deliver enhanced feature representations with minimal computational overhead. The model was pre-trained on a diverse dataset of 46,349 audio files using the Self-Supervised Learning with Barlow Twins (SSL-BT) method, followed by fine-tuning on 17,355 segmented clips extracted from this dataset. MinSnore represents a significant breakthrough in snoring detection, achieving an accuracy of 96.37 %, precision of 96.31 %, recall of 94.12 %, and an F1-score of 95.02 %. When deployed on a single-board computer like a Raspberry Pi, the system demonstrated a reduction in snoring duration during real-world experiments. These results underscore the importance of this work in addressing sleep-related health issues through an efficient, low-cost, and highly accurate snoring mitigation solution.; (Copyright © 2024 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Dynamic convolutions; Edge device; MinSnore; Self-supervise learning; Snoring prevention |
| Entry Date(s): | Date Created: 20241123 Date Completed: 20241221 Latest Revision: 20241221 |
| Update Code: | 20260130 |
| DOI: | 10.1016/j.compbiomed.2024.109458 |
| PMID: | 39579667 |
| Database: | MEDLINE |
Journal Article