Steinbigler P, Haberl R, Jilge G, Steinbeck G
Medical Hospital I, University of Munich, Germany.
Eur Heart J. 1998 Mar;19(3):435-46. doi: 10.1053/euhj.1997.0768.
Post-infarction risk stratification can be ascertained from beat-to-beat variations in ventricular late potentials. However, gaining such information by conventional late potential analysis using signal averaging is still not possible.
We therefore developed the spectrotemporal pattern recognition algorithm in order to detect beat-to-beat variations in late potentials. Based on the spectrotemporal pattern recognition algorithm two-dimensional correlation function, the typical spectral pattern of late potentials can be identified in spectrotemporal maps of single beats, even in the presence of noise.
Surface electrocardiograms of 385 patients after myocardial infarction (85 with documented sustained ventricular tachycardia (group 1), 100 with fast, polymorphic ventricular tachycardia (> 270 cycles.min-1) or primary ventricular fibrillation (group 2), 200 without ventricular arrhythmias (group 3) and 45 healthy volunteers (group 4), were analysed. The spectrotemporal pattern recognition algorithm detected late potentials in single beats in 89% of group 1 patients, in 79% of group 2, in 22% of group 3 and in 4% of normals. The spectrotemporal pattern recognition algorithm measured late potential frequency and extension of late potentials into the ST segment, which was significantly different between groups 1 and 2. Beat-to-beat variations in late potentials, with respect to frequency and extension into the ST segment, were markedly higher in patients with a history of primary ventricular fibrillation.
Single-beat analysis using the spectrotemporal pattern recognition algorithm may improve risk stratification of patients after myocardial infarction, and provides information on patients prone to ventricular fibrillation.
心肌梗死后的风险分层可通过心室晚电位的逐搏变化来确定。然而，使用信号平均的传统晚电位分析仍无法获取此类信息。
因此，我们开发了频谱时间模式识别算法，以检测晚电位的逐搏变化。基于频谱时间模式识别算法的二维相关函数，即使在存在噪声的情况下，也可在单搏的频谱时间图中识别晚电位的典型频谱模式。
分析了385例心肌梗死后患者（85例有持续性室性心动过速记录（第1组），100例有快速、多形性室性心动过速（>270次/分钟）或原发性心室颤动（第2组），200例无室性心律失常（第3组），以及45名健康志愿者（第4组））的体表心电图。频谱时间模式识别算法在第1组89%的患者、第2组79%的患者、第3组22%的患者以及4%的正常人中检测到单搏晚电位。该算法测量了晚电位频率以及晚电位向ST段的延伸，第1组和第2组之间存在显著差异。有原发性心室颤动病史的患者，其晚电位在频率和向ST段延伸方面的逐搏变化明显更高。
使用频谱时间模式识别算法进行单搏分析可能会改善心肌梗死后患者的风险分层，并提供有关易发生心室颤动患者的信息。
