Sun, Tao, Green, Nicolas and Morgan, Hywel
Adaptive noise cancellation for single cell impedance spectroscopy using Maximum Length Sequences.
At 11th annual European conference on micro & nanoscale technologies for biosciences, Montreux, Switzerland,
14 - 16 Nov 2007.
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We have recently developed a novel impedance spectroscopy for high throughput analysis on single biological particles in microfluidic cytometers using Maximum Length Sequences (MLS) 1-3. This technique uses MLS as the excitation signal to the micro-impedance system (figure 1) and allows multi-frequency impedance data to be obtained in one measurement, due to the white noise-like properties of MLS. The data flow diagram of MLS measurement system is shown in figure 2. The digital MLS signal, generated in MATLABTM (Mathworks, Inc., USA) is converted into the analog MLS by D/A converter and applied to the cytometer. After low pass filtering (LPF), the response of the system is sampled into digital form. A Fast M-sequence Transform (FMT) converts the sampled response into the Impulse Response (IR), which is equivalent to the transfer-function of the system. Finally Fast Fourier Transform (FFT) is applied to the IR, characterizing the transfer-function in the frequency domain, from which the impedance of the cell is extracted. The impedance spectra of polystyrene beads and human red blood cells have been measured using this new technology. We have derived 512 frequency data evenly distributed between 976.56 Hz and 500 kHz within approximately 1 ms and the data have been verified by the conventional AC single frequency measurement and the circuit simulations in PSpice (Cadence Inc. USA)2, 3. However, the original measured data using MLS technology exhibits inferior signal-to-noise ratio (SNR), compared to the AC single frequency measurement, in which the energy of the excitation signal is purely concentrated on one specific frequency and the lock-in amplifier for demodulation has a strong ability to reject the noise at other frequencies. In order to improve the SNR of the MLS measurement system without adding any hardware, we use adaptive filters to perform the noise cancellation4, which is based on the least mean square (LMS) principle. A fixed delay is inserted in the original data as a reference input to the adaptive filter for cancelling the noise interference from the background, as shown in figure 3. The SNR of the system can be improved approximately 20 dB at each measured frequency by attenuating the noise level.
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