Biosensors for disease diagnosis are composed 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, but also on the postprocessing performance of the sensor signals. However, there have been very few studies on postprocessing of sensor signals compared with the studies on biochip itself. This study proposes a drastic reduction in assay duration of biosensors by post-processing sensor signals using a time-frequency signal analysis system. The system includes: (1) time-series signal preprocessors; (2) frequency analyzers; and (3) correlation analyzers with target biomolecules. The experiment focused on a liposome-immobilized cantilever biosensor developed to detect aggregated and fibrillated α-synuclein(αSyn), a major causative protein of Parkinson’s disease. The sensor signals from a built-in strain gauge R(t_i) were acquired for multiple samples with different αSyn concentrations. The system was utilized to split the sensor signal into small segments, preprocess them with low-pass filters and autocorrelators, convert them into frequency spectra, and then examine their correlation with αSyn concentration. The frequency spectra revealed clear difference among different αSyn concentration even in the shorter segments. Several segments revealed significant correlation ( r≥ 0.95 ) in the DC and the 10–20 mHz bands. Such good correlations were found in the segments of 560 s and 1,120 s, which are much shorter than the conventional assay duration, demonstrating the potential for drastic reduction in assay duration.
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