Atrial fibrillation (AF) is diagnosed with an electrocardiogram, the gold standard in clinics. However, sufficient arrhythmia monitoring takes a long time, and many of the tests take place in only a few seconds, which can miss arrhythmia. Researchers have developed a combined method to detect the effects of AF on atrial tissue.
The researchers characterized tissues obtained from patients with or without atrial fibrillation by scanning acoustic microscopy (SAM) and Raman spectroscopy (RS) to construct a mechano-chemical profile. They classified the Raman spectral measurements of the tissue samples with an unsupervised clustering method, k-means, and compared their chemical properties. Besides, they utilized scanning acoustic microscopy to compare and determine differences in acoustic impedance maps of the groups.
The researchers compared the clinical outcomes with their findings using a neural network classification for Raman measurements and ANOVA for SAM measurements. Consequently, they showed that the stiffness profiles of the tissues corresponding to the patients with or without chronic atrial fibrillation or who experienced postoperative AF are in agreement with the lipid-collagen profiles obtained by the Raman spectral characterization.