ACM IoT 2023

Exploring CNN and XAI-based Approaches for Accountable MI Detection in the Context of IoT-enabled Emergency Communication Systems

Abstract:

The ageing European population and the expected increasing number 
of medical emergencies put pressure on the medical sector and 
existing emergency infrastructures, which calls for new innovative 
digital solutions. In parallel, the increasing utilization of the 
Internet of Things (IoT) has enabled the collection of real-time 
data, allowing for the autonomous detection of acute medical 
emergencies. In this context, this paper presents two distinct 
machine learning (ML) models that leverage sensor data to 
autonomously detect emergencies. These models are intended to be 
integrated into an IoT-enabled next-generation emergency 
communications system (NG112) capable of detecting emergencies, 
initiating emergency calls (eCalls), and providing relevant 
information to emergency call takers, which reduces response time. 
Thereby, this paper focuses on the accountable detection of 
myocardial infarctions (MIs), commonly known as heart attacks, 
based on electrocardiogram (ECG) data. To realize this, two 
disparate models working on fundamentally different data 
structures are proposed and compared: A one-dimensional 
convolutional neural network (CNN) operating on the raw ECG 
signals and a GoogLeNet-based model trained on ECG images. The 
PTB-XL dataset is used to evaluate the proposed models, and the 
results indicate the 1D CNN exhibits a favourable trade-off 
between precision and recall for the eCall use case. Finally, the 
paper also discusses applying eXplainable AI (XAI) methods to 
achieve explainability for the ML models, paving the way for 
an accountable and reliable implementation in safety-critical systems. 


Pre-camera PDF 

ACM Library

@inproceedings{Knof:IoT2023,
 author = {Knof, Helen and Bagave, Prachi and Boerger, Michell and Tcholtchev, Nikolay and Ding, Aaron},
 title = {Exploring CNN and XAI-based Approaches for Accountable MI Detection in the Context of IoT-enabled Emergency Communication Systems},
 booktitle = {Proceedings of the 2023 International Conference on the Internet of Things},
 series = {IoT '23},
 year = {2023},
 publisher = {ACM}
}

Helene Knof, Prachi Bagave, Michell Boerger, Nikolay Tcholtchev, Aaron Ding. 2023. "Exploring CNN and XAI-based Approaches for Accountable MI Detection in the Context of IoT-enabled Emergency Communication Systems". In Proceedings of the 2023 International Conference on the Internet of Things (IoT '23).