Design and In Vivo Evaluation of a Non-Invasive Transabdominal Fetal Pulse Oximeter.

OBJECTIVE: Current intrapartum fetal monitoring technology is unable to provide physicians with an objective metric of fetal well-being, leading to degraded patient outcomes and increased litigation costs. Fetal oxygen saturation (SpO2) is a more suitable measure of fetal distress, but the inaccessibility of the fetus prior to birth makes this impossible to capture through current means. In this paper, we present a fully non-invasive, transabdominal fetal oximetry (TFO) system that provides in utero measures of fetal SpO2. METHODS: TFO is performed by placing a reflectance-mode optode on the maternal abdomen and sending photons into the body to investigate the underlying fetal tissue. The proposed TFO system design consists of a multi-detector optode, an embedded optode control system, and custom user-interface software. To evaluate the developed TFO system, we utilized an in utero hypoxic fetal lamb model and performed controlled desaturation experiments while capturing gold standard arterial blood gases (SaO2). RESULTS: Various degrees of fetal hypoxia were induced with true SaO2 values ranging between 10.5% and 66%. The non-invasive TFO system was able to accurately measure these fetal SpO2 values, supported by a root mean-squared error of 6.37% and strong measures of agreement with the gold standard. CONCLUSION: The results support the efficacy of the presented TFO system to non-invasively measure a wide-range of fetal SpO2 values and identify critical levels of fetal hypoxia. SIGNIFICANCE: TFO has the potential to improve fetal outcomes by providing obstetricians with a non-invasive measure of fetal oxygen saturation prior to delivery.

Compact and Low-Power Neural Spike Compression Using Undercomplete Autoencoders.

Implantable microsystems that collect and transmit neural data are becoming very useful entities in the field of neuroscience. Limited by high data rates, on-chip compression is often required to transmit the recorded data without causing power dissipation at levels that would damage sensitive brain tissue. This paper presents a data compression system designed for brain-computer interfaces (BCIs) based on undercomplete autoencoders. To the best of our knowledge, the proposed system is the first to achieve an average spike reconstruction quality of 14-dB signal-to-noise-and-distortion ratio (SNDR) at a 32× compression ratio (CR), 18-dB SNDR at a 16× CR, 22-dB SNDR at an 8× CR, and 35-dB SNDR at a 4× CR of neural spikes. The spike detection and autoencoder-based compression modules are designed and implemented in a standard 45-nm CMOS process. The post-synthesis simulation results report that the compression module consumes between 1.4 and 222.5 [Formula: see text] of power per channel and takes between 0.018 and 0.082mm2 of silicon area, depending on the desired CR and number of channels.

Frameworks for Efficient Brain-Computer Interfacing.

One challenge present in brain-computer interface (BCI) circuits is finding a balance between real-time on-chip processing in-vivo and wireless transmission of neural signals for off-chip in-silico processing. This article presents three potential frameworks for investigating an area- and energy-efficient realization of BCI circuits. The first framework performs spike detection on the filtered neural signal on a brain-implantable chip and only transmits detected spikes wirelessly for offline classification and decoding. The second framework performs in-vivo compression of the on-chip detected spikes prior to wireless transmission for substantially reducing wireless transmission overhead. The third framework performs spike sorting in-vivo on the brain-implantable chip to classify detected spikes on-chip and hence, even further reducing wireless data transmission rate at the expense of more signal processing. To alleviate the on-chip computation of spike sorting and also utilizing a more area- and energy-effective design, this work employs, for the first time, to the best of our knowledge, an artificial neural network (ANN) instead of using relatively computationally-intensive conventional spike sorting algorithms. The ASIC implementation results of the designed frameworks are presented and their feasibility for efficient in-vivo processing of neural signals is discussed. Compared to the previously-published BCI systems, the presented frameworks reduce the area and power consumption of implantable circuits.