【摘要】 目的 探討炎性標志物高敏C反應蛋白(highsensitivity creaction protein ,hsCRP)、纖維蛋白原(fibrinogen, FIB)與P波離散度(P wave dispersion, PWD)的關系。 方法 回顧分析2005年1〖CD3/5〗8月收治的102例心臟病住院患者的臨床資料,分別測量PWD和獲得hsCRP、FIB血濃度,對比分析炎性標志物和PWD之間的關系。 結果 心臟病住院患者的PWD (408±93) ms、hsCRP (368±317) mg/L和FIB (411±294) g/L均較正常值高。PWD異常組和正常組的血hsCRP分別為(482±211)、(193±093) mg/L,差異有統計學意義(Plt;001)；血FIB分別為(510±348)、(251±129) g/L,差異有統計學意義(Plt;005)。血hsCRP增高組PWD(549±96) ms，較正常組(285±74) ms顯著增大(Plt;001),血FIB增高組PWD(479±68) ms，較正常組(359±87) ms顯著增大(Plt;005)。PWD與血hsCRP成正相關(相關系數R=0418,Plt;005)；PWD與血FIB成正相關(相關系數R=0292,Plt;005)。 結論 PWD與血炎性標志物密切相關,血炎性標志物增高的患者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.
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%.
