Department of Physics, Bogazici University, Istanbul, 34342, Turkey.
Vocational School of Health Services, Istinye University, Istanbul, 34020, Turkey.
Sci Rep. 2022 Apr 19;12(1):6461. doi: 10.1038/s41598-022-10380-z.
Atrial fibrillation (AF) is diagnosed with the electrocardiogram, which is the gold standard in clinics. However, sufficient arrhythmia monitoring takes a long time, and many of the tests are made in only a few seconds, which can lead arrhythmia to be missed. Here, we propose a combined method to detect the effects of AF on atrial tissue. We characterize tissues obtained from patients with or without AF by scanning acoustic microscopy (SAM) and by Raman spectroscopy (RS) to construct a mechano-chemical profile. We classify the Raman spectral measurements of the tissue samples with an unsupervised clustering method, k-means and compare their chemical properties. Besides, we utilize scanning acoustic microscopy to compare and determine differences in acoustic impedance maps of the groups. We compared the clinical outcomes with our findings using a neural network classification for Raman measurements and ANOVA for SAM measurements. Consequently, we show that the stiffness profiles of the tissues, corresponding to the patients with chronic AF, without AF or who experienced postoperative AF, are in agreement with the lipid-collagen profiles obtained by the Raman spectral characterization.
心房颤动（AF）的诊断采用心电图，这是临床的金标准。然而，充分的心律失常监测需要很长时间，而且许多测试仅在几秒钟内进行，这可能导致心律失常漏诊。在这里，我们提出了一种联合方法来检测 AF 对心房组织的影响。我们通过扫描声学显微镜（SAM）和拉曼光谱（RS）对患有或不患有 AF 的患者的组织进行特征描述，以构建机械化学特征。我们使用无监督聚类方法 k-均值对组织样本的拉曼光谱测量值进行分类，并比较它们的化学性质。此外，我们利用扫描声学显微镜来比较和确定组之间的声阻抗图谱的差异。我们使用拉曼测量的神经网络分类和 SAM 测量的 ANOVA 来比较与我们的发现相关的临床结果。结果表明，与慢性 AF、无 AF 或经历术后 AF 的患者相对应的组织硬度谱与拉曼光谱特征获得的脂质-胶原谱一致。
