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.

翻译：绝大多数心血管疾病若能在早期体征和风险因素出现时被检测到，则可能得以预防。采用传感器贴片等可穿戴式体感设备进行心血管监测，可在保障患者自由与舒适度的同时，实现对相关体征的检测。然而，传感器数据的分析必须兼具鲁棒性、可靠性、高效性与高精度。深度学习方法能够实现数据解读的自动化，从而减轻临床医生的工作负担。本研究旨在探讨将深度学习模型应用于资源受限的医疗边缘设备上，对同步采集的心电图（ECG）与心音图（PCG）记录进行分类的可行性。我们提出了一种采用数据早期融合策略的卷积神经网络，以解决二元分类问题。我们基于Physionet Challenge 2016数据集中的同步ECG与PCG记录对模型进行训练与验证。与现有最优方法相比，我们的方法在保持具有竞争力的准确率的同时，将内存占用与计算成本降低了三个数量级。通过分析微控制器及实验性传感器设备设置上的能耗，我们验证了所提模型在医疗边缘设备上的适用性，并证实了设备端推理相较于持续数据流传输可能具有更高的能效。