ArticleScientific reports2021
Time-frequency time-space LSTM for robust classification of physiological signals.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- LSTM-based safety-oriented prediction of big toe skin temperature in extreme cold conditions for mountaineering footwear evaluation.Scientific reports · 2026Article
- Emotion recognition in cross-linguistic legal context: a comparison between human-based and computational approaches.Frontiers in psychology · 2026Article
- Synthetic-data-driven LSTM framework for tracing cardiac pulsation in optical signals.Biomedical optics express · 2025Article
- Time Series Forecasting Model Based on the Adapted Transformer Neural Network and FFT-Based Features Extraction.Sensors (Basel, Switzerland) · 2025Article
- Intelligent monitoring and individualized strategies for preventing altitude sickness during altitude training.Frontiers in physiology · 2025Review
- ByteTrack: a deep learning approach for bite count and bite rate detection using meal videos in children.Frontiers in nutrition · 2025Article
- Machine and Deep Learning Models for Hypoxemia Severity Triage in CBRNE Emergencies.Diagnostics (Basel, Switzerland) · 2024Article
- Discrete Time Series Forecasting of Hive Weight, In-Hive Temperature, and Hive Entrance Traffic in Non-Invasive Monitoring of Managed Honey Bee Colonies: Part I.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Driven Prognosis of Respiratory Mechanics: Forecasting Tissue Hysteresivity Using Long Short-Term Memory and Continuous Sensor Data.Sensors (Basel, Switzerland) · 2024Article
- Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample.Psychiatry research · 2024Article
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- IoT-Based Solution for Detecting and Monitoring Upper Crossed Syndrome.Sensors (Basel, Switzerland) · 2023Article
- Frontal-occipital phase synchronization predicts occipital alpha power in perceptual decision-making.Cognitive neurodynamics · 2023Article
- Classification of Low Earth Orbit (LEO) Resident Space Objects' (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM).Sensors (Basel, Switzerland) · 2023Article
- Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients.IEEE journal of translational engineering in health and medicine · 2023Article
- A consensus statement on detection of hippocampal sharp wave ripples and differentiation from other fast oscillations.Nature communications · 2022Review
- Deep learning-based predictions of older adults' adherence to cognitive training to support training efficacy.Frontiers in psychology · 2022Article
- Latent Network Construction for Univariate Time Series Based on Variational Auto-Encode.Entropy (Basel, Switzerland) · 2021Article
- Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time-frequency and time-space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.
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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.