Signal Feature Analysis of Contact Force at the Tip of a Flexible Ureteroscope.

This paper presents a signal analysis approach to identify the contact objects at the tip of a flexible ureteroscope. First, a miniature triaxial fiber optic sensor based on Fiber Bragg Grating(FBG) is devised to measure the interactive force signals at the ureteroscope tip. Due to the multidimensional properties of these force signals, the principal components analysis(PCA) method is introduced to reduce dimensions. The signal features are then extracted from the representative principal component signals using the wavelet transform(WT) method. Experimental results show that the contact objects at the tip of a ureteroscope are readily discriminated from the measured force signals with the proposed approach.Clinical Relevance-This work commits to analyze the contact force signals at the tip of a flexible ureteroscope for the purpose of contact objects identification.

Biological tissues identification from their spectral signals acquired by a Raman needle.

In this paper, biological tissues are discriminated based on their intrinsic Raman spectral features. First, a Raman needle, which comprises of a Raman probe and a puncture needle, is devised to insert into biological tissues and acquire their Raman spectral data. The Savitzky-Golay filter is used to remove the data noise, and an adaptive iterative penalized least squares method to rectify the baseline. To extract the spectral features from the data, the principal component analysis (PCA) is used. Then, a genetic algorithm (GA) based support vector machine (SVM) method is proposed to classify the biological tissues based on the spectral features. Experimental study was conducted with different animal specimens and approved that the proposed methods can identify efficiently the different biological tissues.