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  4. End-to-end multi-modal tiny-CNN for cardiovascular monitoring on sensor patches
 
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End-to-end multi-modal tiny-CNN for cardiovascular monitoring on sensor patches

Publikationstyp
Conference Paper
Date Issued
2024-03
Sprache
English
Author(s)
Ibrahim, Mustafa Fuad Rifet  
NXP Semiconductors, Eindhoven, The Netherlands
Alkanat, Tunc  
NXP Semiconductors, Eindhoven, The Netherlands
Meijer, Maurice  
NXP Semiconductors, Eindhoven, The Netherlands
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
Stelldinger, Peer  
Hochschule für Angewandte Wissenschaften Hamburg  
TORE-URI
https://hdl.handle.net/11420/47438
Start Page
18
End Page
24
Citation
IEEE International Conference on Pervasive Computing and Communications, PerCom 2024
Contribution to Conference
IEEE International Conference on Pervasive Computing and Communications, PerCom 2024  
Publisher DOI
10.1109/PerCom59722.2024.10494450
Scopus ID
2-s2.0-85191236547
Publisher
IEEE
ISBN
9798350326031
The vast majority of cardiovascular diseases are avoidable or treatable by preventive measures and early de-tection. To efficiently detect early signs and risk factors, car-diovascular parameters can be monitored continuously with small sensor patches, which improve the comfort of patients. However, processing the sensor data is a challenging task with the demanding needs of robustness, reliability, performance and efficiency. The field of deep learning has tremendous potential to provide a way to analyze cardiovascular sensor data to detect anomalies which alleviates the workload of doctors for more effective data interpretation. In this 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. Our experiments show that our model matches the accuracy of the current state-of-the-art model on the 'training-a' dataset of the Physionet Challenge 2016 database while being more than two orders of magnitude more efficient in memory footprint and compute cost. 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
edge computing
patient monitoring
sensor patches
smart sensing
tiny-ml
MLE@TUHH
DDC Class
610: Medicine, Health
620: Engineering
TUHH
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