Abstract
Background and purposes 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 electrocardiogram s (ECGs) between the post-stroke and the stroke-free.
Methods 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 ease model interpretation by uncovering ECG patterns captured by our model.
Results 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.
Conclusions 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 facing DNN, fostering higher user confidence and adoption of DNN in medicine.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study was supported by Lafayette College internal funds.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
The details of the IRB/oversight body that provided approval or exemption for the research described are given below:
N.A.
All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.
Yes
I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).
Yes
I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
Yes
Data Availability
Dataset was obtained from another research group: https://physionet.org/content/cves/1.0.0/