(source: IEEE journal of translational engineering in health and medicine. 2018； 6:190050) Silent Myocardial Ischemia (SMI) is an issue of public health that leads to heart attack and significanty influences the mortality rate in patients with type 2 diabetes. SMI is myocardial ischemia without   chest discomfort and other angina symptoms. The incidence rate of SMI in diabetic patients was 2.2 times higher than the incidence rate of SMI in nondiabetic patients. SMI had been investigated and confirmed with a 6%- 23% prevalenco in diabetic patients using Myocardial Perfusion Imaging (MP) and invasive coronary angiography. SMI can lead to acute myocardial infarction, adverse cardiac events, and poor prognosis outcomes, that are severe in diabetic care. Therefore, it is quite important to have an early predictor of SMI that can feasibly screen diabetic patients and give a risk stratification of heart ischemia and may prevent many diabetic patients from sudden cardiac death or adverse cardiovascular events. Holter devices, the ambulatory clectrocardiography (ECG), has proven to be a useful tool to detect patients at high risk of SMI. However, Holter devices need to monitor the patients over the course of 24 hours or even up to 72 hours to detect abnormal clectrical signals from the electrocardiogram. This long-term measurement makes the Holter less effective for screening the whole population of high cardiovascular risk group such as diabetic groups. Besides the ambulatory ECG, low ankle-brachial index (ABI) and microalbuminuria, the albumin-tocreatinine ratio (ACR) between 30mg/g to 300mg/g, have also been investigated in the detecti tion of SMI. Another potential method of detecting SMI is using arterial pulse spectru um analysis. The spectrum of arterial pulse wave reflects the loading condition of the arterial system, which has been investigated, modeled, applied, and interpreted in many clinical studies. According to Lin's model, radial pulse spectrum analysis can reveal the arterial-ventricular function by its harmonics change. Chen et al. validated this concept and proved that the specific characteristic of radial pulse spectrum changed from the resting state to the onset of acute, uncomplicated myocardial infarction state, and gradually shifted to other resting characteristics a week after surgery. Furthermore, the cross-sectional study showed that the harmonics of the radial pulse spectrum were correlated with the ischemic heart disease. To summarize results from those studies, the ventricular-arterial coupling system distributed the pressure pulse wave to different organs in proportions of harmonics according to the system state. Therefore, the pattern of harmonic components could reveal the blood flow condition of organs, and more specifically, reveal the condition of myocardial perfusion. However, there is still a lack of direct statistic evidence quantifying the correlation between harmonics of the radial pulse wave and myocardial perfusion, and validating whether the harmonics of the pulse spectrum contains the information in identifying SMI. Hence, the objective of this study was to statistically validate the degree of confidence that the harmonics of radial pulse spectrum and myocardial perfusion were correlated, using receiver operating characteristic curve (ROC) and multivariable linear regression. This report chose type 2 diabetic patients because of their high-risk prevalence for SMI. We included the patients without any angina pectoris history, at high risk of SMI, and suitable for performing MPI. We further investigated the relationship between SMI and different risk factors. In the end, this report analyzed the different risk factor profiles to propose an effective and efficient method for early SMI diagnosis.