Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System

Abstract

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.

Figure 1

Wireless sensor equipment. LA: left arm, LL: left leg, RA: right arm, and RL: right leg.

Figure 2

Simple low-lead method.

Figure 3

Complex multilead approach.

Figure 4

ECG algorithm process flow. Bilateral Long- and Short-Term Memory network, BiLSTM. Premature atrial complex, PAC. Premature ventricular contraction, PVC. Atrial fibrillation, AF. Recurrent neural network, RNN.

Figure 5

24 h ECG report grading delivery mechanism.