Document Type

Conference Proceeding

Publication Date



Early detection of diseases such as cancer can drastically improve prognosis and treatment. To this end, solid-state micropores can measure distinct mechanical properties of diseased cells from their translocation behavior — detected as pulses in the temporal data stream of ionic current — and help diagnose diseases at early stages. However, the obstacle in such approaches is that the accuracy of the sensor is affected by noise, making the pulse detection task too subjective. This is inefficient especially when the disease-relevant data is only a fraction of the total acquired data. Thus, it is important to intelligently automate the detection process to eliminate the noise and to identify useful patterns towards error-free decision-making in real-time. This work describes a pattern detection approach based on moving-average filtering, which mitigates the impact of noise. Moreover, a detection threshold is computed from the mean and standard deviation of the data. The threshold is then used to detect different types of pulses stemming from the healthy and diseased human cells when these translocate through micropores. Extent of smoothing is an important factor for the data: greater smoothing suppresses the noise but deteriorates the pulse shape and vice versa. Additionally, the design approach computes useful features of the detected data and delivers the results for real-time analysis. This can help physicians and scientists to change their strategies of diagnosis by providing a validation of manual reviews.


Copyright © 2023 by ASME

Publication Title

Proceedings of the ASME 2023 International Mechanical Engineering Congress and Exposition. Volume 12: Micro- and Nano-Systems Engineering and Packaging





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