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The intelligent Internet of Things technology is adopted to provide the best exercise load loading mode for stress echocardiography, monitor the body state of exercise load in the whole process, equip intelligent medical monitoring equipment dedicated to exercise, dynamically collect ECG and blood pressure data during exercise, and conduct integration, processing and analysis through the information system, so as to achieve the echocardiography examination under the best exercise load.
The treadmill load test operated in semi recumbent or upright position can continuously image during exercise (low dose and peak dose) to evaluate local ventricular wall motion, and can also provide more Doppler information. However, it is often difficult for patients to reach the maximum exercise load due to premature fatigue of leg muscles.
Treadmill exercise can obtain higher workload and maximum heart rate, and can provide valuable information for clinical diagnosis or prognosis evaluation, such as exercise tolerance, blood pressure response and arrhythmia. If only local wall motion is evaluated, flat plate motion is usually used, but image acquisition is difficult. Therefore, the operator is required to quickly collect images within 1.0~1.5 min after the motion is terminated. If images are not collected within the effective time, false negative results may occur.
Stress echocardiography is mainly used for diagnosis of coronary artery disease, prognosis evaluation and risk stratification of patients who have been diagnosed, preoperative risk assessment, etiology assessment of exertional dyspnea, assessment after revascularization treatment, assessment of ischemic site, assessment of valve stenosis, assessment of coronary artery reserve function, etc. The commonly used stress echocardiography methods include exercise load, drug load, pacing, cold compression, etc. Exercise and drug load are widely used in clinical practice.