Mess W H, Titulaer B M, Ackerstaff R G
St. Antonius Hospital, Nieuwegein, Dept. of Clinical Neurophysiology, The Netherlands.
Ultrasound Med Biol. 2000 Mar;26(3):413-8. doi: 10.1016/s0301-5629(99)00168-4.
Research on microembolic signals (MES) using the dual-gate technique has shown promising results, when the time difference (Deltat) of a MES in two sample volumes (SVs) placed serially has been measured manually. On the other hand, the computerized discrimination of MES and artefacts has been reported not to be superior to algorithms based on a single SV. Therefore, a dataset containing MES as well as four types of artefacts was made to test a preliminary version of a new algorithm for automated emboli detection. We monitored 20 patients during carotid endarterectomy (n = 17) and heart surgery (n = 3). Two transcranial Doppler (TCD) signals with a partial overlap of the SVs were recorded online and analysed off-line with an algorithm based on three consecutive steps: 1. Is there an intensity increase in both channels (64-point FFT; 50% overlap)? 2. What is the expected time difference (Deltat), with the velocity measured in channel 1 as the calculation basis? 3. What is the 'exact' Deltat (pseudo-Wigner power function)? Two human experts decided whether a signal was a MES or belonged to one of the four artefact groups. Of a total of 97 MES, 28% (n = 27) could not be detected in the distal channel. Thus, 72% (n = 70) of the MES were present in both channels and could be analysed based on the abovementioned criteria. Of these 70 MES, 87% (n = 61) were correctly identified off-line. We assessed artefact rejection for four different types of artefacts: changes of TCD settings, probe movement, low flow artefacts and electrocautery. The reliability of artefact rejection was 98% for setting changes (n = 382), 96% for probe movement (n = 477) and 98% for low flow artefacts (n = 91), but only 68% for electrocautery (n = 264). These preliminary results are promising, but need careful interpretation: 28% of the MES were not detectable in the distal SV, probably due to a poor signal-to-noise ratio (SNR) and anatomical restrictions. Electrocautery signals were insufficiently rejected. However, even an artefact rejection of 96% can be insufficient if the number of MES is very small compared to the number of artefacts.
使用双门技术对微栓塞信号（MES）进行的研究已显示出有前景的结果，当手动测量串联放置的两个样本体积（SV）中MES的时间差（Δt）时。另一方面，据报道，MES与伪差的计算机化鉴别并不优于基于单个SV的算法。因此，制作了一个包含MES以及四种伪差类型的数据集，以测试一种用于自动栓子检测的新算法的初步版本。我们在颈动脉内膜切除术（n = 17）和心脏手术（n = 3）期间对20名患者进行了监测。在线记录了两个SV部分重叠的经颅多普勒（TCD）信号，并使用基于三个连续步骤的算法进行离线分析：1. 两个通道中是否都有强度增加（64点快速傅里叶变换；50%重叠）？2. 以通道1中测量的速度为计算基础，预期的时间差（Δt）是多少？3. “精确”的Δt是多少（伪维格纳功率函数）？两名人类专家判定一个信号是MES还是属于四个伪差组之一。在总共97个MES中，28%（n = 27）在远端通道中未被检测到。因此，72%（n = 70）的MES在两个通道中都存在，并可根据上述标准进行分析。在这70个MES中，87%（n = 61）在离线时被正确识别。我们评估了四种不同类型伪差的伪差排除情况：TCD设置的变化、探头移动、低流量伪差和电灼。设置变化（n = 382）的伪差排除可靠性为98%，探头移动（n = 477）为96%，低流量伪差（n = 91）为98%，但电灼（n = 264）仅为68%。这些初步结果很有前景，但需要仔细解读：28%的MES在远端SV中无法检测到，可能是由于信噪比（SNR）差和解剖学限制。电灼信号的排除不充分。然而，如果MES的数量与伪差的数量相比非常少，即使96%的伪差排除率也可能不够。
