ECG heartbeat classification using Wavelet transform and different Neural network Architectures

被引：1

作者：

Datta, Aniruddha ^{[1
]}

Kolwadkar, Bhakti ^{[1
]}

Rauta, Ankita ^{[1
]}

Handal, Siddhi ^{[1
]}

Ingale, V. V. ^{[1
]}

机构：

[1] Coll Engn, Pune, Maharashtra, India

来源：

2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT) | 2021年

关键词：

ECG; Arrhythmia; Convolutional Neural Networks; Continuous Wavelet Transform; Spatiotemporal; Convolutional LSElls;

D O I：

10.1109/I2CT51068.2021.9418101

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Individual Heartbeats of five different classes were extracted from the MIT BIH Arrhythmia Database, Continuous wavelet transform was performed for feature extraction of the ECG recordings, very powerful Convolutional Neural networks were used for the classification process in which many wellknown architectures such as ResNet Inception and Xception were used alongside more recent EfficientNet, and lastly a spatiotemporal method involving convolutional LSTMs was investigated owing to the joint time frequency nature of the wavelet Transform.

引用

页数：7

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