Based on the imaging photoplethysmography (iPPG) and blind source separation (BSS) theory the author put forward a method for non-contact heartbeat frequency estimation. Using the recorded video images of the human face in the ambient light with Webcam, we detected the human face through software, separated the detected facial image into three channels RGB components. And then preprocesses i.e. normalization, whitening, etc. were carried out to a certain number of RGB data. After the independent component analysis (ICA) theory and joint approximate diagonalization of eigenmatrices (JADE) algorithm were applied, we estimated the frequency of heart rate through spectrum analysis. Taking advantage of the consistency of Bland-Altman theory analysis and the commercial Pulse Oximetry Sensor test results, the root mean square error of the algorithm result was calculated as 2.06 beat/min. It indicated that the algorithm could realize the non-contact measurement of heart rate and lay the foundation for the remote and non-contact measurement of multi-parameter physiological measurements.
目的:研究老年患者依托咪酯靶控輸注時不同BIS值(腦電雙頻指數)的HRV(心率變異性)的變化情況,探討不同鎮靜深度與HRV之間的關系。方法:選擇65歲以上行門診胃鏡檢查患者30例,隨機分為3組,A組BIS45~55,B組55~65,C組65~75,各組均在麻醉前、麻醉誘導后,術中、術畢監測BIS、HRV及血液動力學指標。結果:A組各監測HRV明顯降低(Plt;0.05),B組僅有輕度下降(Pgt;0.05),C組明顯升高(Plt;0.05)。結論:患者鎮靜深度BIS55~65時,即可明顯抑制內鏡操作刺激所致的HRV變化,是臨床較為合適的鎮靜深度,可顯著降低老年患者交感神經活性、交感/迷走神經均衡性和自主神經總張力,利于機體血液動力學穩定。
目的:探討64層螺旋CT冠狀動脈成像(64-slice CTA)檢查中護理工作的重要性及獲得最佳圖像的影響因素。材料與方法:對462例行64-slice CTA檢查的患者進行有效的護理措施和細致的前期準備工作。結果:462例檢查者中96%的病例達到診斷標準。結論:經過細致準備和護理,可以提高圖像質量和冠脈疾病的診斷率。
The peak period of cardiovascular disease (CVD) is around the time of awakening in the morning, which may be related to the surge of sympathetic activity at the end of nocturnal sleep. This paper chose 140 participants as study object, 70 of which had occurred CVD events while the rest hadn’t during a two-year follow-up period. A two-layer model was proposed to investigate whether hypnopompic heart rate variability (HRV) was informative to distinguish these two types of participants. In the proposed model, the extreme gradient boosting algorithm (XGBoost) was used to construct a classifier in the first layer. By evaluating the feature importance of the classifier, those features with larger importance were fed into the second layer to construct the final classifier. Three machine learning algorithms, i.e., XGBoost, random forest and support vector machine were employed and compared in the second layer to find out which one can achieve the highest performance. The results showed that, with the analysis of hypnopompic HRV, the XGBoost+XGBoost model achieved the best performance with an accuracy of 84.3%. Compared with conventional time-domain and frequency-domain features, those features derived from nonlinear dynamic analysis were more important to the model. Especially, modified permutation entropy at scale 1 and sample entropy at scale 3 were relatively important. This study might have significance for the prevention and diagnosis of CVD, as well as for the design of CVD-risk assessment system.
Objective To investigate the effect of inhaled anticholinergics on heart rate recovery (HRR) in patients with stable chronic obstructive pulmonary disease (COPD). Methods Sixty clinically stable patients with stage Ⅱ-Ⅳ COPD according to the Global Initiative for Chronic Obstructive Lung Disease guidelines were recruited. HRR was analyzed in this study between 28 patients who had received tiotropium≥1 year and 32 patients who never used anticholinergics as control, so as to reflect parasympathetic reactivity of the heart. Results HRR was significantly lower in the subjects with tiotropium than that in the controls [(16±6) beats/min vs. (22±8) beats/min, P<0.05]. Multivariate regression analysis revealed that anticholinergics medication could be used as an independent predictor of HRR in the COPD patients. Conclusion Anticholinergics can affect cardiac autonomic function of stable COPD patients.
【摘要】 目的 探討急性腦梗死對心臟自主神經活性的影響。 方法 Wistar大鼠32只隨機分為正常組、假手術組和腦梗死組，腦梗死組用線栓法行右側大腦中動脈阻塞。腦梗死組和假手術組于術前及術后24 h作心率變異性（HRV）檢測，同時檢測正常組HRV，將3組的HRV指標進行比較。實驗終點取各組心肌組織檢測兒茶酚胺和神經肽Y（NPY），進行組間比較。 結果 術后24 h腦梗死組和正常組、假手術組相比，竇性心搏間期標準差、均方根，總功率譜、高頻功率譜（HF）、低頻功率譜（LF）降低，差異有統計學意義。3組比較LF/HF和分數維無明顯差異。腦梗死組心肌組織去甲腎上腺素（NA）和NPY高于正常組和假手術組。 結論 腦梗死引起心臟自主神經總活性降低、自主神經功能受損，自主神經末梢去甲腎上腺素和NPY的異常分泌可能是重要的原因。【Abstract】 Objective To investigate the effect of acute cerebral infarction on cardiac autonomic nervous activity. Methods A total of 32 Wistar rats were divided into normal group, sham operation group and infarction group by random. Experimental cerebral infarction in Wistar rats was induced by intraluminal occlusion of middle cerebral artery. About 24 hours after the occlusion or 24 hours after sham operation, the heart rate variability (HRV) sequences were measured, and the HRV values in the three groups were compared. The levels of catecholamine and neuropeptide (NPY) in myocardium were measured. Results At the 24th hour after the occlusion, the standard deviation and root mean square standard deviation of R-R interval, the total power, high frequency (HF) and low frequency (LF) in infarction group were lower than those in normal and sham operation group. LF/HF and fractal dimension did not differ much among the three groups. The levels of noradrenaline and NPY in myocardium in infarction group were higher than those in the other groups. Conclusion It is suggested that acute cerebral infarction may cause the decrease of autonomic nervous activity and damage of the autonomic nervous function; the abnormal secretion of noradrenalin in autonomic nerve ending and NPY may be the important reasons.
Heart rate variability (HRV) analysis technology based on an autoregressive (AR) model is widely used in the assessment of autonomic nervous system function. The order of AR models has important influence on the accuracy of HRV analysis. This article presents a method to determine the optimum order of AR models. After acquiring the ECG signal of 46 healthy adults in their natural breathing state and extracting the beat-to-beat intervals (RRI) in the ECG, we used two criteria, i.e. final prediction error (FPE ) criterion to estimate the optimum model order for AR models, and prediction error whiteness test to decide the reliability of the model. We compared the frequency domain parameters including total power, power in high frequency (HF), power in low frequency (LF), LF power in normalized units and ratio of LF/HF of our HRV analysis to the results of Kubios-HRV. The results showed that the correlation coefficients of the five parameters between our methods and Kubios-HRV were greater than 0.95, and the Bland-Altman plot of the parameters was in the consistent band. The results indicate that the optimization algorithm of HRV analysis based on AR models proposed in this paper can obtain accurate results, and the results of this algorithm has good coherence with those of the Kubios-HRV software in HRV analysis.
Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.