Development of a low-cost, compact, wireless, 16 - channel biopotential data acquisition, signal conditioning and arbitrary waveform stimulator.

The health and fitness of the human body rely heavily on physiological parameters. These parameters can be measured using various tools such as ECG, EMG, EEG, EOG, among others, to obtain real-time physiological data. Analysing the bio-signals obtained from these measurements can provide valuable information that can be used to improve health-care in terms of observation, diagnosis, and treatment. In bio-signal pattern recognition applications, more channels provide multiple information simultaneously. Different biosignal acquisition devices are available in the market, most of which are designed for specific signals like ECG, EMG, EEG etc The gain of the amplifiers and frequency of the filters are designed as per the targeted signals; due to which one device cannot be used for other signals. Also, most of the systems are wired system which is not comfortable for animal studies. In this paper, a low-cost, compact, wireless, 16 channel biopotential data acquisition system with integrated electrical stimulator is designed and implemented. There are several novel and flexible design approaches were incorporated in the proposed design like (1) It has user selectable digital filter in each channel based on the signal frequencies like ECG, EMG, EEG, EOG. The same system will be used to acquire different signals simultaneously. (2) It has variable gain with a configurable analog bandpass filter. (3) It can acquire signals from 4 patients simultaneously. (4) The system is capable to acquire signal from both two-electrode as well as three-electrode configurations. (5) It has integrated stimulator with trapezoidal, charge-balanced, biphasic stimulus output with near zero DC level and user selectable pulse duration or frequency of the stimulus. The developed system has the ability to acquire and transmit data wirelessly in real-time at a high transfer rate. To validate the performance of the system, tests were conducted on the acquired signals using a simulator.

Improvising limitations of DNN based ultrasound image reconstruction.

Ultrasound modalities are cost-effective and radiation-free technology for real-time medical imaging. These modalities require image reconstruction to obtain the actual ultrasound images from ultrasound raw data. The ultrasound raw data is obtained in the form of echo after scanning an imaging plane through ultrasound waves. The most commonly used image reconstruction beamforming technique is Delay and Sum (DAS). Other sophisticated beamforming techniques are Delay Multiply and Sum (DMAS) and Minimum Variance Distortionless Response (MVDR). DAS has limited image quality, and the employment of sophisticated techniques increases the computational complexity and computational time with improvement in image quality. To overcome these problems, various DNN (Deep Neural Networks) based techniques have been proposed which can reconstruct ultrasound images directly from ultrasound raw data. But DNN implementation has two limitations: accuracy of reconstruction and generalizability of the model. To overcome these limitations, we are proposing methodologies with a DNN model which was able to reduce these limitations. Firstly, we generated the datasets which include multiple shapes such as line, circle, ellipse, and parabola. After that, we have implemented a CNN-DNN (Convolution Neural Network and Deep Neural Network) hybrid model which has significantly improved computational time as well as image quality. We have trained our model with different sets of data to validate the reconstruction of the image matrix. We achieved a significant improvement in computational time of around 100 times (from around 0.6 s to 0.0059 s) as compared to DAS beamforming technique. At the same time, we also achieved a significant improvement in image quality with 37.19 dB average and 41.37 dB maximum improved Peak Signal to Noise Ratio (PSNR), and 87.41% average and 95% maximum Structural Similarity Index Matrix (SSIM) value. We also achieved generalizability and precise image reconstruction by using the proposed model.