Biosensors for disease diagnosis consist of a bio-receptor to capture target biomolecules, a transducer to convert chemical phenomena into optical or electrical signals, and a detector to receive the signals. The performance of a biosensor depends not only on the physical sensitivity of the bio-receptors and transducers integrated into the biochip, but also on the analytical sensitivity or accuracy to estimate the presence or concentration of target biomolecules from the detector signal. However, there have been very few previous studies on detector signal analysis compared with the development study on biochip itself. In this study, we propose a time-frequency signal analysis system for drastic improvement of sensitivity and detection duration of biosensors. This system includes three major functions: 1) preprocessor of time-series signal such as differentiator, low-pass filter, segment splitter, and auto-correlator; 2) frequency analyzer such as Fourier transformer and inter-frequency band statistical analyzer; and 3) correlation analyzer with target biomolecule. The prototype system enabled us, for the first time, to perform exploratory time-frequency analysis of time-series signal detected from a cantilever-type biochip with liposomes as bio-receptors. The specific frequency bands revealed significant correlation with the concentration of alpha-synuclein, a major causative protein of Parkinson's disease. The spectral shape was kept in a shorter segment of the original time-series signal. These results clearly revealed the possibility to increase the sensitivity or reduce the detection duration drastically by focusing on specific frequency bands, demonstrating the significant impact of this system on the performance improvement of biosensors.