Abstract
This paper describes the design and implementation of an in-body electromagnetic sensor for patients with implanted pacemakers. The sensor can either be mounted on myocardial tissue and monitor the electrocardiography (ECG) with contact electrodes or implanted under the skin and monitor the ECG with coaxial leads. A 16-bit high-resolution analog front-end (AFE) and an energy-efficient 32-bit CPU are used for instantaneous ECG recording. Wireless data transmission between the sensor and clinician’s computer is achieved by an embedded low-power Bluetooth transmitter. In order to automatically recognize the working status of the pacemaker and alarm the episodes of arrhythmias caused by pacemaker malfunctions, pacing mode classification and fault diagnosis on the recorded ECG were achieved based on an AI algorithm, i.e., a resource allocation network (RAN). A prototype of the sensor was implemented on a human torso, and the in vitro test results prove that the sensor can work properly for the 1-4-meter transmission range.
Highlights
According to the WHO’s 2019 Global Health Estimates [1], cardiovascular diseases (CVD) have become one of the main sources of human death in the last 20 years, accounting for 16 percent of total death cases
Among all the cardiac monitoring technologies, electrocardiography (ECG) signals are most commonly used to assess the state of the heart and indicate irregular heartbeats, due to its high resolution and strong anti-interference abilities
Patients with CVD need to paste the electrodes on their skin which may lead to allergy
Summary
According to the WHO’s 2019 Global Health Estimates [1], cardiovascular diseases (CVD) have become one of the main sources of human death in the last 20 years, accounting for 16 percent of total death cases. When a patient with implanted pacemaker is exposed to a transient electromagnetic field, the electromagnetic interference (EMI) with frequency of ~kHz to ~MHz could be created in the pacing loop formed by the leads and the pulse generator These EMI signals are difficult to detect by a regular Holter, but they need to be properly monitored since these signals could be misunderstood as the normal pacing pulses by the pacemaker and potentially cause pacemaker malfunctions. Recent research has shown that deep machine learning can establish the mapping relationship of nonlinear functions in ECG and fully explore the information that is difficult to identify manually [6, 7] For this reason, the introduction of machine learning to assist the identification and diagnosis of ECG signals detected by an implantable sensor can greatly improve the efficiency of diagnosis, reduce the rate of misdiagnosis, and save medical costs [6,7,8]. The feasibility of the inbody sensor is tested by in vitro tests, where it is indicated that the sensor can work properly for the 1-4-meter transmission range, and two types of the abnormal pacing mode, i.e., pulmonary hypertension (PH) and respiratory sinus arrhythmia (RSA), are successfully validated through the AI-enhanced ECG signals
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