# LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/litenet-lightweight-neural-network-for-detecting-arrhythmias-at-resource/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Ziyang He,Xiaoqing Zhang,Yangjie Cao,Zhi Liu,Bo Zhang,Xiaoyan Wang |
| Citations | 56 |
| DOI | 10.3390/s18041229 |
| Fields | Engineering,Medicine,Neuroscience |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.mdpi.com/1424-8220/18/4/1229/pdf?version=1525348828 |
| OpenAlex ID | https://openalex.org/W2800428890 |
| PMID | 29673171 |
| Type | article |
| Year | 2018 |

## Paper authors

- [Yangjie Cao](https://scholariq.org/researchers/yangjie-cao/)

## Paper journal

- [Sensors](https://scholariq.org/journals/sensors/)

## Paper primary topic

- [ECG Monitoring and Analysis](https://scholariq.org/topics/ecg-monitoring-and-analysis/)

## Paper topics

- [ECG Monitoring and Analysis](https://scholariq.org/topics/ecg-monitoring-and-analysis/)
- [EEG and Brain-Computer Interfaces](https://scholariq.org/topics/eeg-and-brain-computer-interfaces/)
- [Non-Invasive Vital Sign Monitoring](https://scholariq.org/topics/non-invasive-vital-sign-monitoring/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.