# ECG-TCN: Wearable Cardiac Arrhythmia Detection with a Temporal Convolutional Network

arXiv: https://arxiv.org/abs/2103.13740 · HTML: https://mlsanity.com/paper/2103.13740/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [LSTM Fully Convolutional Networks for Time Series Classification](https://mlsanity.com/paper/1709.05206/) (2017)
- [EEG-TCNet: An Accurate Temporal Convolutional Network for Embedded Motor-Imagery Brain-Machine Interfaces](https://mlsanity.com/paper/2006.00622/) (2020)
- [DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs](https://mlsanity.com/paper/2008.07127/) (2020)
- [PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors](https://mlsanity.com/paper/1908.11263/) (2019)
- [Comparison of Batch Normalization and Weight Normalization Algorithms for the Large-scale Image Classification](https://mlsanity.com/paper/1709.08145/) (2017)
- [Technical Report: NEMO DNN Quantization for Deployment Model](https://mlsanity.com/paper/2004.05930/) (2020)
- [Convolutional-Recurrent Neural Networks on Low-Power Wearable Platforms for Cardiac Arrhythmia Detection](https://mlsanity.com/paper/2001.03538/) (2020)
- [Optimizing Temporal Convolutional Network inference on FPGA-based accelerators](https://mlsanity.com/paper/2005.03775/) (2020)
- [InfiniWolf: Energy Efficient Smart Bracelet for Edge Computing with Dual Source Energy Harvesting](https://mlsanity.com/paper/2003.00041/) (2020)

## Built on by (most cited first, 14 of 14)

- [Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge](https://mlsanity.com/paper/2301.10281/) (2023)
- [Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices](https://mlsanity.com/paper/2106.08008/) (2021)
- [Global Attention-based Encoder-Decoder LSTM Model for Temperature Prediction of Permanent Magnet Synchronous Motors](https://mlsanity.com/paper/2208.00293/) (2022)
- [EpiDeNet: An Energy-Efficient Approach to Seizure Detection for Embedded Systems](https://mlsanity.com/paper/2309.07135/) (2023)
- [FEMBA on the Edge: Physiologically-Aware Pre-Training, Quantization, and Deployment of a Bidirectional Mamba EEG Foundation Model on an Ultra-low Power Microcontroller](https://mlsanity.com/paper/2603.26716/) (2026)
- [DeepArrhythmia: Segment-Contextualized ECG Arrhythmia Classification via Selective Evidence Acquisition](https://mlsanity.com/paper/2605.16441/) (2026)
- [OpenGlass: Ultra-Low-Power On-Device AI Eyewear with Event-based Vision](https://mlsanity.com/paper/2606.07431/) (2026)
- [Deep Temporal Modeling and Ensemble Fusion for Multimodal Emotion Recognition from Physiological Signals](https://mlsanity.com/paper/2606.15026/) (2026)
- [FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation](https://mlsanity.com/paper/2303.16322/) (2023)
- [Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification](https://mlsanity.com/paper/2308.02416/) (2023)
- [Skilog: A Smart Sensor System for Performance Analysis and Biofeedback in Ski Jumping](https://mlsanity.com/paper/2309.14455/) (2023)
- [Wavelet Probabilistic Recurrent Convolutional Network for Multivariate Time Series Classification](https://mlsanity.com/paper/2505.17307/) (2025)
- [EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming](https://mlsanity.com/paper/2604.25499/) (2026)
- [From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities](https://mlsanity.com/paper/2609.20991/) (2026)

## Abstract (arXiv)

Personalized ubiquitous healthcare solutions require energy-efficient wearable platforms that provide an accurate classification of bio-signals while consuming low average power for long-term battery-operated use. Single lead electrocardiogram (ECG) signals provide the ability to detect, classify, and even predict cardiac arrhythmia. In this paper, we propose a novel temporal convolutional network (TCN) that achieves high accuracy while still being feasible for wearable platform use. Experimental results on the ECG5000 dataset show that the TCN has a similar accuracy (94.2%) score as the state-of-the-art (SoA) network while achieving an improvement of 16.5% in the balanced accuracy score. This accurate classification is done with 27 times fewer parameters and 37 times less multiply-accumulate operations. We test our implementation on two publicly available platforms, the STM32L475, which is based on ARM Cortex M4F, and the GreenWaves Technologies GAP8 on the GAPuino board, based on 1+8 RISC-V CV32E40P cores. Measurements show that the GAP8 implementation respects the real-time constraints while consuming 0.10 mJ per inference. With 9.91 GMAC/s/W, it is 23.0 times more energy-efficient and 46.85 times faster than an implementation on the ARM Cortex M4F (0.43 GMAC/s/W). Overall, we obtain 8.1% higher accuracy while consuming 19.6 times less energy and being 35.1 times faster compared to a previous SoA embedded implementation.