Multimodal apnea detection: advancements through convolutional neural networks and STFT analysis of EEG, ECG, and nasal signals to tackle key challenges in innovation.
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| Title: | Multimodal apnea detection: advancements through convolutional neural networks and STFT analysis of EEG, ECG, and nasal signals to tackle key challenges in innovation. |
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| Authors: | Ghafourian MS; Electrical Engineering Department, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran., Noori A; Faculty of Electrical and Biomedical Engineering, Sadjad University, Mashhad, Iran., Taghipour S; Faculty of Electrical and Biomedical Engineering, Sadjad University, Mashhad, Iran., Ramezani A; Control Engineering Department, School of Electrical and Computer Engineering, Tarbiat Modares University (TMU), Tehran, Iran.; Postdoctoral Research Fellow, Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. |
| Source: | Computer methods in biomechanics and biomedical engineering [Comput Methods Biomech Biomed Engin] 2026 Oct; Vol. 29 (13), pp. 3225-3235. Date of Electronic Publication: 2025 Jun 08. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Informa Healthcare Country of Publication: England NLM ID: 9802899 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1476-8259 (Electronic) Linking ISSN: 10255842 NLM ISO Abbreviation: Comput Methods Biomech Biomed Engin Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1476-8259 |
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| DOI: | 10.1080/10255842.2025.2510367 |
