ReviewSensors (Basel, Switzerland)2020
Deep Learning in Physiological Signal Data: A Survey.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
70 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of Artificial Intelligence in the Classification of Upper-Limb Motion Using EEG and EMG Signals: A Review.Sensors (Basel, Switzerland) · 2026Pooled it
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Dual-Stream Fusion of Eye-Tracking and ECG Signals for Fatigue Detection in Remote Tower Air Traffic Controllers.Bioengineering (Basel, Switzerland) · 2026Article
- Real-Time Pain Assessment from Electrodermal Activity Using Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Hierarchical Deep Learning Framework for Skin Disease and Cancer Classification Performance Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Remote Tower Air Traffic Controller Multimodal Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Improving Clinical Diagnostics and Patient Care through Artificial Intelligence and Biosensor Technologies.ACS omega · 2026Review
- Physiological sensing systems and AI-based signal analysis in immersive virtual reality learning and training: A scoping review.PloS one · 2026Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.PloS one · 2026Article
- Foundation Models Meet Medical Image Interpretation.Research (Washington, D.C.) · 2026Review
- Generalizable Hybrid Wavelet-Deep Learning Architecture for Robust Arrhythmia Detection in Wearable ECG Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.bioRxiv : the preprint server for biology · 2025Article
- Printed sensing human-machine interface with individualized adaptive machine learning.Science advances · 2025Article
- BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques.Sensors (Basel, Switzerland) · 2025Article
- Distilling the knowledge from large-language model for health event prediction.Scientific reports · 2024Article
- Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy.Sensors (Basel, Switzerland) · 2024Article
- Deep learning model using continuous skin temperature data predicts labor onset.BMC pregnancy and childbirth · 2024Article
- Multiclass motor imagery classification with Riemannian geometry and temporal-spectral selection.Medical & biological engineering & computing · 2024Article
- A lightweight xAI approach to cervical cancer classification.Medical & biological engineering & computing · 2024Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.
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What OpenQuestion holds
Registered trials
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.