Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches
This work addresses the need for efficient, on-device deep learning for cardiovascular sensor patches, which could aid in early disease detection, though it is incremental as it builds on existing methods for edge deployment.
The authors tackled the problem of classifying synchronized ECG and PCG recordings for cardiovascular monitoring on resource-constrained edge devices, achieving a three orders of magnitude reduction in memory and compute costs while maintaining competitive accuracy.
The vast majority of cardiovascular diseases may be preventable if early signs and risk factors are detected. Cardiovascular monitoring with body-worn sensor devices like sensor patches allows for the detection of such signs while preserving the freedom and comfort of patients. However, the analysis of the sensor data must be robust, reliable, efficient, and highly accurate. Deep learning methods can automate data interpretation, reducing the workload of clinicians. In this work, we analyze the feasibility of applying deep learning models to the classification of synchronized electrocardiogram (ECG) and phonocardiogram (PCG) recordings on resource-constrained medical edge devices. We propose a convolutional neural network with early fusion of data to solve a binary classification problem. We train and validate our model on the synchronized ECG and PCG recordings from the Physionet Challenge 2016 dataset. Our approach reduces memory footprint and compute cost by three orders of magnitude compared to the state-of-the-art while maintaining competitive accuracy. We demonstrate the applicability of our proposed model on medical edge devices by analyzing energy consumption on a microcontroller and an experimental sensor device setup, confirming that on-device inference can be more energy-efficient than continuous data streaming.