In situ tissue classification during laser ablation using acoustic signals.

We suggest a novel method to classify the type of tissue that is being ablated, using the recorded acoustic sound waves during pulsed ultraviolet laser ablation. The motivation of the current research is tissue classification during vascular interventions, where the identification of the ablated tissue is vital. We classify the acoustic signatures using Mel-frequency cepstral coefficients (MFCCs) feature extraction with a Support Vector Machine (SVM) algorithm, and in addition, use a fully connected deep neural network (FC-DNN) algorithm.