论文标题
基于tensorflow Lite模型基于PTB-XL数据集，基于深度学习的ECG分类
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset
论文作者
论文摘要
由于19 COVID-19大流行以来的需求增加，医疗保健中的IoT设备数量预计将急剧增加。深度学习和IoT设备正在用于监测身体生命力并在临床和非临床环境中自动化异常检测。当前的大多数技术都需要将原始数据传输到远程服务器，这对于资源约束的IoT设备和嵌入式系统不有效。此外，由于缺乏广泛的公共数据库，开发用于ECG分类的机器学习模型是一项挑战。在某种程度上，为了克服这一挑战，已经使用了PTB-XL数据集。在这项工作中，我们开发了将在Raspberry Pi上部署的机器学习模型。我们通过两个分类类对张量流模型进行评估。我们还介绍了对相应的Tensorflow Lite Flatbuffers的评估，以证明其最小的运行时间要求，同时保持可接受的准确性。
The number of IoT devices in healthcare is expected to rise sharply due to increased demand since the COVID-19 pandemic. Deep learning and IoT devices are being employed to monitor body vitals and automate anomaly detection in clinical and non-clinical settings. Most of the current technology requires the transmission of raw data to a remote server, which is not efficient for resource-constrained IoT devices and embedded systems. Additionally, it is challenging to develop a machine learning model for ECG classification due to the lack of an extensive open public database. To an extent, to overcome this challenge PTB-XL dataset has been used. In this work, we have developed machine learning models to be deployed on Raspberry Pi. We present an evaluation of our TensorFlow Model with two classification classes. We also present the evaluation of the corresponding TensorFlow Lite FlatBuffers to demonstrate their minimal run-time requirements while maintaining acceptable accuracy.
