Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening

Chi Ho Tseng; Chen Lin; Hsiang Chih Chang; Cyuan Cin Liu; Bess Ma F. Serafico; Li Ching Wu; Chih Ting Lin; Tien Hsu; Chun Yao Huang; Men Tzung Lo

doi:10.1109/MC.2019.2933195

Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening

Chi Ho Tseng, Chen Lin, Hsiang Chih Chang, Cyuan Cin Liu, Bess Ma F. Serafico, Li Ching Wu, Chih Ting Lin, Tien Hsu, Chun Yao Huang, Men Tzung Lo

Research output: Contribution to specialist publication › Article

7 Scopus citations

Abstract

Atrial fibrillation (AFib) is the most common arrhythmia, and patients with AFib have a five times higher risk for stroke. To develop an efficient and sustainable strategy for detecting undiagnosed AFib, we propose a cloud-based artificial intelligence system for arrhythmia screening, especially for AFib.

Original language	English
Pages	40-51
Number of pages	12
Volume	52
No	11
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2019.2933195
State	Published - Nov 2019

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10.1109/MC.2019.2933195

A Multi-Signal Based Automated Monitoring System for Pediatric Sleep Disorders(1/2)
Lin, C. (PI)
1/08/19 → 31/07/20
Project: Research
Realization of Real-Time Ultra-High Density Substrate Mapping for Automatic Detection of Hidden Af Drivers beneath Highly Fractionated Electrograms(1/3)
Lo, M.-T. (PI)
1/08/19 → 31/07/20
Project: Research
The Hunt for Rotors and Focal Sources as a New Approach to Improving the Outcome of Catheter Ablation of Persistent and Longstanding Persistent Atrial Fibrillation and an Efficient Heterogeneous Structure Weathering the Computation(2/2)
Lo, M.-T. (PI)
1/08/18 → 31/07/19
Project: Research

Cite this

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title = "Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening",

abstract = "Atrial fibrillation (AFib) is the most common arrhythmia, and patients with AFib have a five times higher risk for stroke. To develop an efficient and sustainable strategy for detecting undiagnosed AFib, we propose a cloud-based artificial intelligence system for arrhythmia screening, especially for AFib.",

author = "Tseng, {Chi Ho} and Chen Lin and Chang, {Hsiang Chih} and Liu, {Cyuan Cin} and Serafico, {Bess Ma F.} and Wu, {Li Ching} and Lin, {Chih Ting} and Tien Hsu and Huang, {Chun Yao} and Lo, {Men Tzung}",

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AU - Tseng, Chi Ho

AU - Lin, Chen

AU - Chang, Hsiang Chih

AU - Liu, Cyuan Cin

AU - Serafico, Bess Ma F.

AU - Wu, Li Ching

AU - Lin, Chih Ting

AU - Hsu, Tien

AU - Huang, Chun Yao

AU - Lo, Men Tzung

PY - 2019/11

Y1 - 2019/11

N2 - Atrial fibrillation (AFib) is the most common arrhythmia, and patients with AFib have a five times higher risk for stroke. To develop an efficient and sustainable strategy for detecting undiagnosed AFib, we propose a cloud-based artificial intelligence system for arrhythmia screening, especially for AFib.

AB - Atrial fibrillation (AFib) is the most common arrhythmia, and patients with AFib have a five times higher risk for stroke. To develop an efficient and sustainable strategy for detecting undiagnosed AFib, we propose a cloud-based artificial intelligence system for arrhythmia screening, especially for AFib.

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JO - Computer

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Cloud-Based Artificial Intelligence System for Large-Scale Arrhythmia Screening

Abstract

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Projects

A Multi-Signal Based Automated Monitoring System for Pediatric Sleep Disorders(1/2)

Realization of Real-Time Ultra-High Density Substrate Mapping for Automatic Detection of Hidden Af Drivers beneath Highly Fractionated Electrograms(1/3)

The Hunt for Rotors and Focal Sources as a New Approach to Improving the Outcome of Catheter Ablation of Persistent and Longstanding Persistent Atrial Fibrillation and an Efficient Heterogeneous Structure Weathering the Computation(2/2)

Cite this