【摘要】 目的 探讨炎性标志物高敏C反应蛋白(highsensitivity creaction protein ,hsCRP)、纤维蛋白原(fibrinogen, FIB)与P波离散度(P wave dispersion, PWD)的关系。 方法 回顾分析2005年1〖CD3/5〗8月收治的102例心脏病住院患者的临床资料,分别测量PWD和获得hsCRP、FIB血浓度,对比分析炎性标志物和PWD之间的关系。 结果 心脏病住院患者的PWD (408±93) ms、hsCRP (368±317) mg/L和FIB (411±294) g/L均较正常值高。PWD异常组和正常组的血hsCRP分别为(482±211)、(193±093) mg/L,差异有统计学意义(Plt;001)；血FIB分别为(510±348)、(251±129) g/L,差异有统计学意义(Plt;005)。血hsCRP增高组PWD(549±96) ms，较正常组(285±74) ms显著增大(Plt;001),血FIB增高组PWD(479±68) ms，较正常组(359±87) ms显著增大(Plt;005)。PWD与血hsCRP成正相关(相关系数R=0418,Plt;005)；PWD与血FIB成正相关(相关系数R=0292,Plt;005)。 结论 PWD与血炎性标志物密切相关,血炎性标志物增高的患者PWD增大。【Abstract】〓Objective〓〖WT5”BZ〗To investigate the relationship between P wave dispersion (PWD) and inflammatory marker (serum highsensitivity creaction protein, hsCRP and fibronogen,FIB). Methods Retrospectively measure PWD of 102 inpatients with heart diseases,and get the results of the hsCRP and FIB. Results The average PWD (408±93) ms of 102 inpatients is higher than normal value,the average hsCRP (368±317) mg/L and FIB (411±294) g/L are higher than normal value. The serum concentration of the hsCRP and FIB increase significantly in abnormal PWD subgroup than normal PWD subgroup, respectively [(482±211) mg/L vs (193±093) mg/L, Plt;001 and (510±348) g/L vs (251±129) g/L, Plt;005)]. The PWD of the serum highconcentration hsCRP and FIB subgroup increase than normalconcentration subgroup significantly, respectively [(549±96) ms vs (285±74) ms, Plt;001 and (479±68) ms vs (359±87) ms,Plt;005] PWD has positive relationship with hsCRP(R=08,Plt;005)and FIB (R=0292,Plt;005). Conclusions PWD has good relationship with serum inflammtory makers, PWD increases with the ascending of concentration of the serum hsCRP and FIB.
目的:探讨血管紧张素Ⅱ受体拮抗剂(ARB)对PAF患者P波离散度的影响。方法:观察48 例阵发性AF患者的最宽P 波和P 波离散度,并与ARB干预治疗3 个月后进行对比分析。结果:ARB治疗3个月后最宽P波、P 波离散度及P 波离散度≥40 ms的例数与治疗前比较差异有统计学意义 (Plt;0.05或lt;0.01)。结论:ARB能减轻PAF患者心房结构重构及电重构,减少AF的发生。
Generally, P-wave is the wave of low-frequency and low-amplitude, and it could be affected by baseline drift, electromyography (EMG) interference and other noises easily. Not every heart beat contains the P-wave, and it is also a major problem to determine the P-wave exist or not in a heart beat. In order to solve the limitation of suiting the diverse morphological P-wave using wavelet-amplitude-transform algorithm and the limitation of selecting the pseudo-P-wave sample using the wavelet transform and neural network, we presented new P-wave detecting method based on wave-amplitude threshold and using the multi-feature as the input of neural networks. Firstly, we removed the noise of ECG through the wavelet transform, then determined the position of the candidate P-wave by calculating modulus maxima of the wavelet transform, and then determine the P-wave exist or not by wave-amplitude threshold method initially. Finally we determined whether the P-wave existed or not by the neural networks. The method is validated based on the QT database which is supplied with manual labels made by physicians. We compared the detection effect of ECG P-waves, which was obtained with the method developed in the study, with the algorithm of wavelet threshold value and the method based on "wavelet-amplitude-slope", and verified the feasibility of the proposed algorithm. The detected ECG signal, which is recorded in the hospital ECG division, was consistent with the doctor's labels. Furthermore, after detecting the 13 sets of ECG which were 15min long, the detection rate for the correct P-wave is 99.911%.