Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping

Ding, Z.; Ho, Eric S.

Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping

Abstract

Stroke is the second leading cause of death globally after ischemic heart disease, also a risk factor of cardioembolic stroke. Thus, we postulate that heartbeats encapsulate vital signals related to stroke. With the rapid advancement of deep neural networks (DNNs), it emerges as a powerful tool to decipher intriguing heartbeat patterns associated with post-stroke patients. In this study, we propose the use of a one-dimensional convolutional network (1D-CNN) architecture to build a binary classifier that distinguishes electrocardiograms (ECGs) between the post-stroke and the stroke-free. We have built two 1D-CNNs that were used to identify distinct patterns from an openly accessible ECG dataset collected from elderly post-stroke patients. In addition to prediction accuracy, which is the primary focus of existing ECG deep neural network methods, we have utilized Gradient-weighted Class Activation Mapping (GRAD-CAM) to facilitate model interpretation by uncovering subtle ECG patterns captured by our model. Our stroke model has achieved ~90 % accuracy and 0.95 area under the Receiver Operating Characteristic curve. Findings suggest that the core PQRST complex alone is important but not sufficient to differentiate the post-stroke and the stroke-free. In conclusion, we have developed an accurate stroke model using the latest DNN method. Importantly, our work has illustrated an approach to enhance model interpretation, overcoming the black-box issue confronting DNNs, fostering higher user confidence and adoption of DNNs in medicine.

Title	Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping
Creator	Ding, Z.
Creator	Ho, Eric S.
Publisher	Artificial Intelligence in Medicine
Academic Department	Biology
Academic Department	Computer Science
Division	Natural Sciences
Organization	Lafayette College
Date Issued	August 2022
Date Available	2022-07-06
Type	Article
Language	English
Keyword	Convolutional neural network
	Deep neural network
	Stroke
	Electrocardiogram
	Cardioembolism
	GRAD-CAM
Bibliographic Citation	Ho, E. S. and Z. Ding (2022 Aug) "Electrocardiogram analysis of post-stroke elderly people using one-dimensional convolutional neural network model with gradient-weighted class activation mapping." Artificial Intelligence in Medicine 130: 102342.
Standard Identifier	DOI 10.1016/j.artmed.2022.102342
Standard Identifier	Handle 10385/fb494986x
Permalink	http://hdl.handle.net/10385/fb494986x
Rights Statement	In Copyright
Rights Holders	Elsevier B.V.