This paper presents an implantable bio-impedance measurement system for cardiac pacemakers. The fully integrated system features a low power analog front-end and pulse width modulated output. The bio-impedance readout benefits from voltage to time conversion to achieve a very low power consumption for wirelessly transmitting the data outside the body. The proposed IC is fabricated in a 0.18 μm CMOS process and is capable of measuring the bio-impedance at 2 kHz over a wide dynamic range from ${\text{100}}\,\Omega $ to $\text{3.3}{\;\text{k}\Omega }$ with $\text{1.35}\;\Omega _{{\rm{rms}}}$ accuracy and ${\text{1}}\; {\mu}\text{A}$ maximum current injection while consuming just $\text{1.55}{\;{\mu}}{\text{A}}$ from a 1 V supply.

Objective. Therapeutic intervention in neurological disorders still relies heavily on pharmacological solutions, while the treatment of patients with drug resistance remains an unresolved issue. This is particularly true for patients with epilepsy, 30% of whom are refractory to medications. Implantable devices for chronic recording and electrical modulation of brain activity have proved a viable alternative in such cases. To operate, the device should detect the relevant electrographic biomarkers from local field potentials (LFPs) and determine the right time for stimulation. To enable timely interventions, the ideal device should attain biomarker detection with low latency while operating under low power consumption to prolong battery life. Approach. Here we introduce a fully-analog neuromorphic device implemented in CMOS technology for analyzing LFP signals in an in vitro model of acute ictogenesis. Neuromorphic networks have progressively gained a reputation as low-latency low-power computing systems, which makes them a promising candidate as processing core of next-generation implantable neural interfaces. Main results. The developed system can detect ictal and interictal events with ms-latency and with high precision, consuming on average 3.50 nW during the task. Significance. The work presented in this paper paves the way to a new generation of brain implantable devices for personalized closed-loop stimulation for epilepsy treatment.

Recent research provides examples of neuromorphic systems applied to process biological signals or to interface biological tissues. Usually, in such contexts, the neuromorphic system is used for automatic anomaly detection. The automation of the long-term monitoring of biological signals holds promise for lightening the burden placed on clinicians. At the same time, the adoption of such devices potentially allows processing to be performed locally, without the need to transfer data to an external processor. In turn, on-site signal analysis makes closed-loop intervention feasible, to correct the source of the anomalies. So far, the common approach has been to deploy the network of spiking neurons either on multi-core neuromorphic platforms or on programmable units (field programmable gate array). However, if the aim is to develop wearable or even chronically implantable devices, it is imperative to move in the direction of embedded solutions, tailored and optimized for the specific application. To this end, the present study proposes a neuromorphic device implemented in CMOS technology for the detection of epileptic seizures (ictal events) from local field potential (LFP) signals. The LFP data have been acquired by a multi-electrode array in a slice of mouse hippocampus-cortex. The system includes an analog-to-event converter (AEC) encoding the recorded signals into trains of spikes, and a small spiking neural network (SNN) of 2 × 1 neurons with online biological-plausible learning. The AEC yields two spike-trains: UP spikes that account for the positive slope signal and DOWN spikes that correspond to the negative slope signal. The synapses among the input and output layers are plastic and follow the spike-timing-dependent plasticity rule. Early results show that the SNN module is able to detect ictal events with a delay of 64.98 ± 30.92 ms, consuming < 50 pW. The layout of the entire system occupies 528 µm × 278 µm.

Recording electrical activities through high-density electrodes using neural amplifiers plays a crucial role in the successful implementation of a data acquisition system targeting deep-brain regions. To advance the concept of high-density electrodes, neural amplifiers with not only minimum area, but also high power efficiency are needed. This paper presents a neural recording chopper amplifier using auxiliary path and gain revision techniques to boost the input impedance to a DC value of 6.7 GΩ that meets the requirements of an implantable multi-channel recording system. The front-end fabricated in 180 nm CMOS technology occupies an area of 0.051 mm2, and consumes a total power consumption of 2.1 μW drawn from a 1 V supply. The proposed work increases the DC input impedance (18×) and decreases the area by 25% in comparison with conventional auxiliary path impedance boosting with a CMRR of -75 dB, and the input-referred noise of 2.1 μVrms.

The rapid growth of wearable technology has opened new possibilities for smart health-monitoring systems. Among various sensing methods, bio-impedance sensing has stood out as a powerful, non-invasive, and energy-efficient way to track physiological changes and gather important health information. This review looks at the basic principles behind bio-impedance sensing, how it is being built into wearable devices, and its use in healthcare and everyday wellness tracking. We examine recent progress in sensor design, signal processing, and machine learning, and show how these developments are making real-time health monitoring more effective. While bio-impedance systems offer many advantages, they also face challenges, particularly when it comes to making devices smaller, reducing power use, and improving the accuracy of collected data. One key issue is that analyzing bio-impedance signals often relies on complex digital signal processing, which can be both computationally heavy and energy-hungry. To address this, researchers are exploring the use of neuromorphic processors—hardware inspired by the way the human brain works. These processors use spiking neural networks (SNNs) and event-driven designs to process signals more efficiently, allowing bio-impedance sensors to pick up subtle physiological changes while using far less power. This not only extends battery life but also brings us closer to practical, long-lasting health-monitoring solutions. In this paper, we aim to connect recent engineering advances with real-world applications, highlighting how bio-impedance sensing could shape the next generation of intelligent wearable devices.