A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution–Pooling Method

A Multi-Class ECG Signal Classifier Using a Binarized Depthwise Separable CNN with the Merged Convolution–Pooling Method

Binarized convolutional neural networks (bCNNs) are favored for the design of low-storage, low-power cardiac arrhythmia classifiers owing to their high weight compression rate. However, multi-class classification of ECG signals based on bCNNs is challenging due to the accuracy loss introduced by the...

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Bibliographic Details
Main Authors: Rui Zhang, Ranran Zhou, Zuting Zhong, Haifeng Qi, Yong Wang
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Sensors
Subjects:
binarized depthwise separable convolutional neural network (bDSCNN)
ECG
blockwise incremental calculation
merged convolution–pooling method
multi-classifier
FPGA
Online Access:https://www.mdpi.com/1424-8220/24/22/7207
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