Atomic force microscopy (AFM) is an attractive technique for studying biomechanical and morphological changes in live cells. Using real-time AFM monitoring of cellular mechanical properties, spontaneous oscillations in cell stiffness and cell adhesion to the extracellular matrix (ECM) have been found. However, the lack of automated analytical approaches to systematically extract oscillatory signals, and noise filtering from a large set of AFM data, is a significant obstacle when quantifying and interpreting the dynamic characteristics of live cells. Here we demonstrate a method that extends the usage of AFM to quantitatively investigate live cell dynamics. Approaches such as singular spectrum analysis (SSA), and fast Fourier transform (FFT) were introduced to analyze a real-time recording of cell stiffness and the unbinding force between the ECM protein-decorated AFM probe and vascular smooth muscle cells (VSMCs). The time series cell adhesion and stiffness data were first filtered with SSA and the principal oscillatory components were isolated from the noise floor with the computed eigenvalue from the lagged-covariance matrix. Following the SSA, the oscillatory parameters were detected by FFT from the noise-reduced time series data sets and the sinusoidal oscillatory components were constructed with the parameters obtained by FFT.