ArticleIEEE journal of biomedical and health informatics2019
Multimodal Ambulatory Sleep Detection Using LSTM Recurrent Neural Networks.
Article in IEEE journal of biomedical and health informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Predicting Sleep and Sleep Stage in Children Using Actigraphy and Heartrate via a Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation.Journal of sleep research · 2026Article
- Comparison and Validation of Actigraphy Algorithms Using a Large Community Dataset: Algorithm Validation Study.JMIR formative research · 2025Article
- Recent applications of EEG-based brain-computer-interface in the medical field.Military Medical Research · 2025Review
- Modeling of whole brain sleep electroencephalogram using deep oscillatory neural network.Frontiers in neuroinformatics · 2025Article
- [Artificial intelligence in wearable electrocardiogram monitoring].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Sputum deposition classification for mechanically ventilated patients using LSTM method based on airflow signals.Heliyon · 2022Article
- Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods.International journal of environmental research and public health · 2022Article
- A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets.The Visual computer · 2022Article
- Sensor-Based Estimation of Dim Light Melatonin Onset (DLMO) Using Features of Two Time Scales.ACM transactions on computing for healthcare · 2021Article
- Potential Applications of Mobile and Wearable Devices for Psychological Support During the COVID-19 Pandemic: A Review.IEEE sensors journal · 2021Article
- Upper Esophageal Sphincter Opening Segmentation With Convolutional Recurrent Neural Networks in High Resolution Cervical Auscultation.IEEE journal of biomedical and health informatics · 2021Article
- The Real-Time and Patient-Specific Prediction for Duration and Recovery Profile of Cisatracurium Based on Deep Learning Models.Frontiers in pharmacology · 2021Article
- Sleep stage classification from heart-rate variability using long short-term memory neural networks.Scientific reports · 2019Article
- Categorizing Sleep in Older Adults with Wireless Activity Monitors Using LSTM Neural Networks.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Unobtrusive and accurate ambulatory methods are needed to monitor long-term sleep patterns for improving health. Previously developed ambulatory sleep detection methods rely either in whole or in part on self-reported diary data as ground truth, which is a problem, since people often do not fill them out accurately. This paper presents an algorithm that uses multimodal data from smartphones and wearable technologies to detect sleep/wake state and sleep onset/offset using a type of recurrent neural network with long-short-term memory (LSTM) cells for synthesizing temporal information. We collected 5580 days of multimodal data from 186 participants and compared the new method for sleep/wake classification and sleep onset/offset detection to, first, nontemporal machine learning methods and, second, a state-of-the-art actigraphy software. The new LSTM method achieved a sleep/wake classification accuracy of 96.5%, and sleep onset/offset detection F
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.