1D CNN Framework on ECG Signals

Jasvitha, Bommisetty Durga; Kanagaraj, Kanaganandini; Murali, Keerthana; Singh, Tripty; Duraisamy, Prakash

doi:10.1109/inocon60754.2024.10512284

2024 3rd International Conference for Innovation in Technology (INOCON) 2024

DOI: 10.1109/inocon60754.2024.10512284

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1D CNN Framework on ECG Signals

Bommisetty Durga Jasvitha,

Kanaganandini Kanagaraj,

Keerthana Murali

et al.

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Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users

Cacciari,

Ranfagni

2024

Applied Sciences

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In recent years, deep learning (DL) has garnered significant attention for its successful applications across various domains in solving complex problems. This interest has spurred the development of numerous neural network architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and the more recently introduced Transformers. The choice of architecture depends on the data characteristics and the specific task at hand. In the 1D domain, one-dimensional CNNs (1D CNNs) are widely used, particularly for tasks involving the classification and recognition of 1D signals. While there are many applications of 1D CNNs in the literature, the technical details of their training are often not thoroughly explained, posing challenges for those developing new libraries in languages other than those supported by available open-source solutions. This paper offers a comprehensive, step-by-step tutorial on deriving feedforward and backpropagation equations for 1D CNNs, applicable to both regression and classification tasks. By linking neural networks with linear algebra, statistics, and optimization, this tutorial aims to clarify concepts related to 1D CNNs, making it a valuable resource for those interested in developing new libraries beyond existing ones.

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Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users

Cacciari,

Ranfagni

2024

Applied Sciences

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1D CNN Framework on ECG Signals

Cited by 1 publication

References 17 publications

Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users

Hands-On Fundamentals of 1D Convolutional Neural Networks—A Tutorial for Beginner Users

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