A High-Speed ANN-Based Data Acquisition Hardware Accelerator Targeting Electrical Impedance Tomography

This paper presents a novel design for a high-speed Data Acquisition (DAQ) system tailored for Electrical Impedance Tomography (EIT). Our proposed solution leverages a high-speed Analog-to-Digital Converter (ADC) to digitize analog signals from multiple electrode pairs within a single cycle, employing a time-multiplexed approach. The resulting samples are then fed into an Artificial Neural Network (ANN) for accurate estimation of peak amplitudes across all channels, subsequently used for image reconstruction. To optimize the performance, we explored various ANN models with customized loss functions and devised an effective model selection approach using the grid search technique. In contrast to other multi-frequency techniques, our proposed approach eliminates the need for a multi-frequency current source, thereby simplifying the DAQ system. Additionally, it obviates the requirement for high-quality narrow-band pass-band filters designed for different frequencies. By employing our approach, EIT systems can achieve remarkable throughput rates exceeding 2,800 Frames Per Second (fps) for a 50 kHz excitation signal, even with 32 or more electrodes. Extensive experimental testing demonstrated peak estimation accuracy surpassing 98%, even in scenarios with signals exhibiting 40 dB Signal-to-Noise Ratio (SNR). Consequently, our suggested approach exhibits tremendous potential for EIT applications that demand high SNR and rapid DAQ.