Photoacoustic signal classification for in vivo photoacoustic flow cytometry based on support vector machine

Melanoma is a malignant tumor whose circulating tumor cell (CTC) count has been shown as a prognostic marker for metastasis development. Therefore detection of circulating melanoma cells plays an important role in monitoring tumor metastasis and prevention after diagnosis. In Vivo Photoacoustic Flow Cytometry (PAFC) is established here to achieve in vivo melanoma inspection, meanwhile guarantees non-invasive and real-time detection.Accurate tumor cell detection is of great significance to achieve highly specific diagnosis and avoid unnecessary medical tests.However, the amount of data detected by PAFC is large and original photoacoustic signal is mixed with various noises.The traditional triple mean square deviation method has lower accuracy and consumes a lot of time in data processing. Here, a classification approach in photoacoustic is proposed, which could discriminate signals and noises based on features extracted from photoacoustic waves compared to normal cells using Support Vector Machines algorithm. Due to similar shape and structure of cells, the photoacoustic signals usually have similar vibration mode. By analyzing the correlations and the signal features in time domain and frequency domain, we finally choose the continuity, amplitude, and photoacoustic waveform pulse width as the features to characterize the signal.More than 600,000 samples were selected as the training set (normalized in advance), and a classifier with a precision of 85% accuracy to filter out the photoacoustic signals rapidly was trained by the support vector machine method.The classifier introduced here has proved to optimize the signal acquisition and reduce signal processing time, realizing real-time detection and real-time analysis in PAFC.