Development of real-time cellular impedance analysis system

The cell impedance analysis technique is a label-free, non-invasive method, which simplifies sample preparation and allows applications requiring unmodified cell retrieval. However, traditional impedance measurement methods suffer from various problems (speed, bandwidth, accuracy) for extracting the cellular impedance information. This thesis proposes an improved system for extracting precise cellular impedance in real-time, with a wide bandwidth and satisfactory accuracy. The system hardware consists of five main parts: a microelectrode array (MEA), a stimulation circuit, a sensing circuit, a multi-function card and a computer. The development of system hardware is explored. Accordingly, a novel bioimpedance measurement method coined digital auto balancing bridge method, which is improved from the traditional analogue auto balancing bridge circuitry, is realized for real-time cellular impedance measurement. Two different digital bridge balancing algorithms are proposed and realized, which are based on least mean squares (LMS) algorithm and fast block LMS (FBLMS) algorithm for single- and multi-frequency measurements respectively. Details on their implementation in FPGA are discussed. The test results prove that the LMS-based algorithm is suitable for accelerating the measurement speed in single-frequency situation, whilst the FBLMS-based algorithm has advantages in stable convergence in multi-frequency applications. A novel algorithm, called the All Phase Fast Fourier Transform (APFFT), is applied for post-processing of bioimpedance measurement results. Compared with the classical FFT algorithm, the APFFT significantly reduces spectral leakage caused by truncation error. Compared to the traditional FFT and Digital Quadrature Demodulation (DQD) methods, the APFFT shows excellent performance for extracting accurate phase and amplitude in the frequency spectrum. Additionally, testing and evaluation of the realized system has been performed. The results show that our system achieved a satisfactory accuracy within a wide bandwidth, a fast measurement speed and a good repeatability. Furthermore, our system is compared with a commercial impedance analyzer (Agilent 4294A) in biological experiments. The results reveal that our system achieved a comparable accuracy to the commercial instrument in the biological experiments. Finally, conclusions are given and the future work is proposed

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces