Artifact: end-to-end multi-modal tiny-cnn for cardiovascular monitoring on sensor patches

Start Page
5
End Page
6
Citation
IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2024
Contribution to Conference
IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2024  
Publisher DOI
10.1109/PerComWorkshops59983.2024.10502566
Scopus ID
2-s2.0-85192502336
Publisher
IEEE
ISBN
979-8-3503-0436-7
979-8-3503-0437-4
This document describes the content and usage of the code artifact files of the original paper “End-to-End Multi-Modal Tiny-CNN for Cardiovascular Monitoring on Sensor Patches”. In that work, we show the feasibility of applying deep learning for the classification of synchronized electrocardiogram and phonocardiogram recordings under very tight resource constraints. Our model employs an early fusion of data and uses convolutional layers to solve the problem of binary classification of anomalies. We use the “training-a” dataset of the Physionet Challenge 2016 database for evaluation. Further, we demonstrate the applicability of our model on edge devices, such as sensor patches, by estimating processor performance, power consumption, and silicon area.
Subjects
MLE@TUHH
DDC Class
005: Computer Programming, Programs, Data and Security