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ObjectiveTo investigate the objective characteristics of tongue manifestations in patients with coronary heart disease （CHD）. MethodsA total of 315 participants with CHD were recruited in the CHD group， and 211 healthy participants who underwent physical examination were recruited as the healthy control group. In addition， according to the common comorbidities （primary hypertension， carotid atherosclerosis， type 2 diabetes mellitus， fatty liver， hyperlipidaemia， heart failure， and cerebral infarction） in 315 participants with CHD， each comorbidity was classified into a group of comorbidities with that disease and a group of non-comorbidities. Tongue images were captured using a TFDA-1 tongue diagnostic instrument to characterise the tongue body （TB） and tongue coating （TC）， comparing the RGB， HIS， and Lab colour spaces in the chromaticity index （R， red； G， green； B， blue； H， hue； I， intensity； S， saturation； L， lightness； a， red-green axis； b， yellow-blue axis）， the tongue coating thickness index （per-All）， contrast （CON）， angular second moment （ASM）， entropy （ENT）， and mean （MEAN） in texture metrics. ResultsCompared with the healthy control group， the characteristic indexes of tongue body in CHD group showed lower TB-R， TB-G， TB-B， TB-I， TB-L and higher TB-H， TB-b； and the characteristic indexes of tongue coating in CHD group showed lower TC-R， TC-B and higher TC-CON， TC-MEAN， TC-H， TC-b （P＜0.05 or P＜0.01）. Compared with non-combined primary hypertension group， CHD combined primary hypertension group showed higher per-All， TB-G， TB-L， and lower TB-a， TC-a （P＜0.05）； compared with the non-combined carotid atherosclerosis group， CHD combined carotid atherosclerosis group showed higher TB-CON， TB-ENT， TB-MEAN， and lower TB-ASM （P＜0.05 or P＜0.01）； compared with the non-combined type 2 diabetes mellitus group， CHD combined type 2 diabetes mellitus group showed lower per-All and higher TB-H （P＜0.05 or P＜0.01）； compared with the non-combined fatty liver group， CHD combined fatty liver group showed higher TB-CON， TB-MEAN， TB-ENT， and lower TB-ASM and TC-S （P＜0.05 or P＜0.01）； compared with the non-combined hyperlipidaemia group， CHD combined hyperlipidaemia group showed lower TB-S and TB-a （P＜0.05）； compared with non-combined heart failure group， CHD combined heart failure group showed lower TB-R， TB-G， TB-I， TB-L， and higher TB-a （P＜0.05 or P＜0.01）； compared with non-combined cerebral infarction group， CHD combined cerebral infarction group showed higher TC-CON， TC-ENT， TC-MEAN， and lower TC-ASM （P＜0.05 or P＜0.01）. ConclusionCompared to healthy individuals， patients with CHD tend to have darker tongue colours and rougher TC textures. Compared with non-comorbidity participants， those with primary hypertension tended to be lighter tongue colour and thicker tongue coating， those with carotid atherosclerosis had paler tongue body， those with type 2 diabetes mellitus had thinner tongue coating， those with fatty liver disease had paler tongue body and whiter tongue colour， those with hyperlipidaemia and heart failure had paler tongue colour， and those with cerebral infarction had rougher tongue texture.
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A novel technology is proposed for non-contact and real-time detection of atrial fibrillation using millimeter-wave radar.A 60 GHz PCR millimeter wave radar is used to continuously detect the chest echo signal of the subject.After signal acquisition,I-Q signal is generated through I-Q demodulation,and the signal phase information is extracted using effective points phase trend evaluation for obtaining the signals from oscillations in the chest wall,from which the respiratory signals and cardiac signals are extracted through digital filtering for the analysis of cardiac movement.Whether the atrial fibrillation occurs or not is determined by the characteristics of atrial fibrillation wave in the time domain.The effective points phase trend evaluation for extracting more accurate signal phase information and the time-domain method for real-time atrial fibrillation detection are the innovations of the study.The experimental results show that the proposed method achieves a detection accuracy of 99.2%in clinic.