# A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion from Heartbeat

arXiv: https://arxiv.org/abs/1902.03043 · HTML: https://mlsanity.com/paper/1902.03043/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [Transformer-Based Self-Supervised Learning for Emotion Recognition](https://mlsanity.com/paper/2204.05103/) (2022)
- [Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring](https://mlsanity.com/paper/2508.09085/) (2025)
- [Supervised Contrastive Learning for Affect Modelling](https://mlsanity.com/paper/2208.12238/) (2022)
- [Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review](https://mlsanity.com/paper/2312.09454/) (2023)
- [Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals](https://mlsanity.com/paper/2212.13885/) (2022)
- [Conformal Depression Prediction](https://mlsanity.com/paper/2405.18723/) (2024)
- [Multi-scale Transformer-based Network for Emotion Recognition from Multi Physiological Signals](https://mlsanity.com/paper/2305.00769/) (2023)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)
- [Fine-Grained Uncertainty Quantification via Collisions](https://mlsanity.com/paper/2411.12127/) (2024)
- [Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection](https://mlsanity.com/paper/2608.14675/) (2026)
- [Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models](https://mlsanity.com/paper/2609.15055/) (2026)

## Abstract (arXiv)

Automatic prediction of emotion promises to revolutionise human-computer interaction. Recent trends involve fusion of multiple data modalities - audio, visual, and physiological - to classify emotional state. However, in practice, collection of physiological data `in the wild' is currently limited to heartbeat time series of the kind generated by affordable wearable heart monitors. Furthermore, real-world applications of emotion prediction often require some measure of uncertainty over model output, in order to inform downstream decision-making. We present here an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat time series. We further propose a Bayesian framework for modelling uncertainty over these valence predictions, and describe a probabilistic procedure for choosing to accept or reject model output according to the intended application. We benchmarked our framework against two established datasets and achieved peak classification accuracy of 90%. These results lay the foundation for applications of affective computing in real-world domains such as healthcare, where a high premium is placed on non-invasive collection of data, and predictive certainty.