Key result
The proposed micro neural network (MicroNN) achieved classification accuracies of 98.4% and 98.1% on the MIT-BIH-AR and INCART datasets, respectively, outperforming state-of-the-art methods.
Why the study?
Existing deep learning methods for healthcare sensor data classification require significant time and storage space, making real-time deployment on small edge devices difficult.
The proposed MicroNN model enables highly accurate and computationally efficient classification of physiological signals suitable for deployment on tiny edge devices.
May enable edge-device ECG classification; leaves open prospective clinical validation.
A smart city is an intelligent space, in which large amounts of data are collected and analyzed using low-cost sensors and automatic algorithms. The application of artificial intelligence and Internet of Things (IoT) technologies in electronic health (E-health) can efficiently promote the development of sustainable and smart cities. The IoT sensors and intelligent algorithms enable the remote monitoring and analyzing of the healthcare data of patients, which reduces the medical and travel expenses in cities. Existing deep learning-based methods for healthcare sensor data classification have made great achievements. However, these methods take much time and storage space for model training and inference. They are difficult to be deployed in small devices to classify the physiological signal of patients in real time. To solve the above problems, this paper proposes a micro time series classification model called the micro neural network (MicroNN). The proposed model is micro enough to be deployed on tiny edge devices. MicroNN can be applied to long-term physiological signal monitoring based on edge computing devices. We conduct comprehensive experiments to evaluate the classification accuracy and computation complexity of MicroNN. Experiment results show that MicroNN performs better than the state-of-the-art methods. The accuracies on the two datasets (MIT-BIH-AR and INCART) are 98.4% and 98.1%, respectively. Finally, we present an application to show how MicroNN can improve the development of sustainable and smart cities.
No takes yet. Share an insight, caveat, or question.
Wu et al. (2022) studied Physiological signal monitoring. Micro neural network (MicroNN) vs. State-of-the-art methods was evaluated on Classification accuracy. The proposed micro neural network (MicroNN) achieved classification accuracies of 98.4% and 98.1% on the MIT-BIH-AR and INCART datasets, respectively, outperforming state-of-the-art methods.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: