ArticleSensors (Basel, Switzerland)2020
CNN and LSTM-Based Emotion Charting Using Physiological Signals.
Article 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 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- ECG-Based Emotion Recognition Using Beat-Level Complementary Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- Improving Fall Classification Accuracy of Multi-Input Models Using Three-Axis Accelerometer and Heart Rate Variability Data.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12-Lead ECGs Based on Left Atrial Remodeling.Journal of the American Heart Association · 2024Article
- DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.Sensors (Basel, Switzerland) · 2024Article
- STAFNet: an adaptive multi-feature learning network via spatiotemporal fusion for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
- ECG Multi-Emotion Recognition Based on Heart Rate Variability Signal Features Mining.Sensors (Basel, Switzerland) · 2023Article
- Personality-Based Emotion Recognition Using EEG Signals with a CNN-LSTM Network.Brain sciences · 2023Article
- Hybrid Deep Learning and Discrete Wavelet Transform-Based ECG Biometric Recognition for Arrhythmic Patients and Healthy Controls.Sensors (Basel, Switzerland) · 2023Article
- An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.Sensors (Basel, Switzerland) · 2022Article
- CNN-XGBoost fusion-based affective state recognition using EEG spectrogram image analysis.Scientific reports · 2022Article
- Decoding the neural signatures of valence and arousal from portable EEG headset.Frontiers in human neuroscience · 2022Article
- Emotion Recognition Based on Skin Potential Signals with a Portable Wireless Device.Sensors (Basel, Switzerland) · 2021Article
- Classification of Mental Stress Using CNN-LSTM Algorithms with Electrocardiogram Signals.Journal of healthcare engineering · 2021Article
- In the Flow of Life: Capturing Affective Socializing Dynamics Using a Wearable Sensor and Intensive Daily Diaries.Socius : sociological research for a dynamic worldArticle
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
No grant is acknowledged in the PubMed record.
Abstract
Novel trends in affective computing are based on reliable sources of physiological signals such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR). The use of these signals provides challenges of performance improvement within a broader set of emotion classes in a less constrained real-world environment. To overcome these challenges, we propose a computational framework of 2D Convolutional Neural Network (CNN) architecture for the arrangement of 14 channels of EEG, and a combination of Long Short-Term Memory (LSTM) and 1D-CNN architecture for ECG and GSR. Our approach is subject-independent and incorporates two publicly available datasets of DREAMER and AMIGOS with low-cost, wearable sensors to extract physiological signals suitable for real-world environments. The results outperform state-of-the-art approaches for classification into four classes, namely High Valence-High Arousal, High Valence-Low Arousal, Low Valence-High Arousal, and Low Valence-Low Arousal. Emotion elicitation average accuracy of 98.73% is achieved with ECG right-channel modality, 76.65% with EEG modality, and 63.67% with GSR modality for AMIGOS. The overall highest accuracy of 99.0% for the AMIGOS dataset and 90.8% for the DREAMER dataset is achieved with multi-modal fusion. A strong correlation between spectral- and hidden-layer feature analysis with classification performance suggests the efficacy of the proposed method for significant feature extraction and higher emotion elicitation performance to a broader context for less constrained environments.
Indexed as
Identifiers
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.