Multi-Level Classification of Arteriovenous Graft Degree of Stenosis Based on SSA + Welch Multi-Position Sequence Feature Extraction

This study proposes a phonoangiography (PCG) signal processing method to help non-invasively diagnose Arteriovenous Graft (AVG) stenosis in patients with End Stage Renal Disease (ESRD). Pre-existing binary- classification studies for Degrees Of Stenosis (DOS) indicated that the presence of high frequency acoustic signals characterizes stenosis AVG, but the specific range they pointed out vary from each other, including 300Hz to 600Hz, 400Hz or more, 700Hz. In order to obtain the true relationship between the frequency characteristics of the acoustic signal and DOS of AVG, our team proposed a multi-position sequence feature (MPSF) extraction method, the judgement of DOS has also been expanded from binary-classification to multi-level classification. This method uses a physical model built by our team to simulate normal AVG and AVG with different DOS, we selected upstream 7cm and downstream 7cm of the stenosis section as two measurement positions to collect AVG acoustic signal. The components of the AVG acoustic signal below 200 Hz are filtered using singular spectrum analysis (SSA), and the remaining part is subjected to power spectrum estimation using the Welch method. The frequency spectrum energy obtained from the two measurement positions is combined into MPSF to be trained in the LSTM neural network, thereby realizing multi-level classification of different DOS. Tests on AVG physical models and clinical patient data sets show that, compared to previous studies, this method has the better effect on diagnosis of DOS.