An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection

An Arbitrarily Reconfigurable Extreme Learning Machine Inference Engine for Robust ECG Anomaly Detection

Extreme learning machine (ELM) has shown to be an effective and low-power approach for real-time electrocardiography (ECG) anomaly detection. However, prior ELM inference chips are noise-prone and lacking in reconfigurability. In this article, we present an arbitrarily reconfigurable ELM inference e...

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Bibliographic Details
Main Authors: Yu-Chuan Chuang, Yi-Ta Chen, Huai-Ting Li, An-Yeu Andy Wu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Open Journal of Circuits and Systems
Subjects:
Adaptive boosting (Adaboost)
eigenspace denoising
electrocardiography (ECG) anomaly detection
extreme learning machine (ELM)
reconfigurable chip design
Online Access:https://ieeexplore.ieee.org/document/9335310/

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