The key challenges in designing a multi-channel biosignal acquisition system for an ambulatory or invasive medical application with a high channel count are reducing the power consumption, area consumption and the outgoing wire count. This article proposes a spread-spectrum modulated biosignal acquisition system using a shared amplifier and an analog-to-digital converter (ADC). We propose a design method to optimize a recording system for a given application based on the required SNR performance, number of inputs, and area. The proposed method is tested and validated on real pre-recorded atrial electrograms and achieves an average percentage root-mean-square difference (PRD) performance of 2.65% and 3.02% for sinus rhythm (SR) and atrial fibrillation (AF), respectively by using pseudo-random binary-sequence (PRBS) codes with a code-length of 511, for 16 inputs. We implement a 4-input spread-spectrum analog front-end in a 0.18 μ m CMOS process to demonstrate the proposed approach. The analog front-end consists of a shared amplifier, a 2nd order Σ Δ ADC sampled at 7.8 MHz, used for digitization, and an on-chip 7-bit PRBS generator. It achieves a number-of-inputs to outgoing-wire ratio of 4:1 while consuming 23 μ A/input including biasing from a 1.8 V power supply and 0.067 mm ² in area.

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Due to its intrinsic sparsity both in time and space, event-based data is optimally suited for edge-computing applications that require low power and low latency. Time varying signals encoded with this data representation are best processed with Spiking Neural Networks (SNN). In particular, recurrent SNNs (RSNNs) can solve temporal tasks using a relatively low number of parameters, and therefore support their hardware implementation in resource-constrained computing architectures. These premises propel the need of exploring the properties of these kinds of structures on low-power processing systems to test their limits both in terms of computational accuracy and resource consumption, without having to resort to full-custom implementations. In this work, we implemented an RSNN model on a low-end, resource-constrained ARM-Cortex-M4-based Micro Controller Unit (MCU). We trained it on a down-sampled version of the N-MNIST event-based dataset for digit recognition as an example to assess its performance in the inference phase. With an accuracy of 97.2%, the implementation has an average energy consumption as low as 4.1μJ and a worst-case computational time of 150.4μs per time-step with an operating frequency of 180 MHz, so the deployment of RSNNs on MCU devices is a feasible option for small image vision real-time tasks.

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We consider the design of Wireless Power Transfer (WPT) systems based on inductive links and focus on recent works where the whole WPT system (i.e. both energy transmitter and energy receiver) is designed as an isolated resonant class-E DC-DC converter characterized by a loosely-coupled transformer. The aim of this work is to compare the classic WPT design approach with a novel one, which allows achieving the same performance with a significant reduction in the number of reactive components of the circuit, with beneficial effects in terms of system complexity, size, and cost. We will also show that such a reduction in the number of reactive components leads to improved performance robustness to variations in the inductive link coupling factor.@en

In this paper, the design of a wireless power transfer system (WPT) targeting biomedical implants is considered. The novelty of the approach is to propose a co-design of the transmitter and receiver side based on the design of class-E isolated DC-DC converters. The solution, along with the simple introduction of a shunt regulator at the receiver, allows us to solve the problem of ensuring optimal efficiency in the WPT link. In conventional solutions, in order to cope with coupling factor and load variations, information from the receiver is needed, which is usually relayed back onto the transmitter by means of telemetry. With the proposed approach, a very simple minimum power point tracking (mPPT) algorithm can be used to maximize the WPT efficiency based on the information already available at the transmitter side. This reduces the complexity of the circuitry of the implant and thereby its power overhead and possibly its size, both being crucial constraints of a biomedical implant.

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Atrial electrograms (AEGs) acquired with a high spatio-temporal resolution are a promising approach for early detection of atrial fibrillation. Due to the high data rate, transmission of AEG signals is expensive in terms of power consumption and resources, making its adoption a challenge for low-power wireless devices. In this paper, we investigate the feasibility of using compressed sensing (CS) for the acquisition of AEGs while reducing redundant data without losing information. We apply two CS approaches, standard CS and rakeness-based CS (rak-CS) on a data set, composed of real medical recordings. We find that the AEGs are compressible in time, and, more interestingly, in the spatial domain. The performance of rak-CS is better than standard CS, especially at higher compression ratios (CR), both during sinus rhythm (SR) and atrial fibrillation (AF). The difference in the achieved average reconstruction signal-to-noise (ARSNR) in rak-CS and standard CS, for CR = 4.26, in the time domain is 7.7 dB and 2.6 dB for AF and SR, respectively. Multi-channel data is modeled as a multiple-measurement-vector problem and the mixed norm is used to exploit the group structure of the signals in the spatial domain to obtain improved reconstruction performance over $l_{1}$ norm minimization. Using the mixed-norm recovery approach, for CR = 4.26, the difference in achieved ARSNR performance between rak-CS and standard CS is 5 dB and 2 dB for AF and SR, respectively.@en

Atrial electrogram (AEG) acquired with a high spatio-temporal resolution is a promising approach for early detection of atrial fibrillation. Due to the high data rate, transmission of AEG signals requires considerable energy, making its adoption a challenge for low-power wireless devices. In this paper, we investigate the feasibility of using compressed sensing (CS) for the acquisition of AEGs while reducing redundant data without losing information. We apply two CS approaches, standard CS and rakeness-based CS (rak-CS) on real medical recordings. We find that the AEGs are compressible in time, and, more interestingly, in the spatial domain. The performance of rak-CS is better than standard CS, especially at higher compression ratios (CR), both during sinus rhythm (SR) and atrial fibrillation (AF). More specifically, the difference in the achieved average reconstruction signal-to-noise (ARSNR) in rak-CS and standard CS, for CR = 4.26, in the time domain is 7.7 dB and 2.6 dB for AF and SR, respectively. Multi-channel data is modeled as a multiple-measurement-vector problem and a suitable mixed norm is used to exploit the group structure of the signals in the spatial domain to obtain improved reconstruction performance over l1 norm minimization. Using the mixed-norm recovery approach, for CR = 4.26, the difference in achieved ARSNR performance between rak-CS and standard CS is 5 dB and 2 dB for AF and SR, respectively.

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This paper presents the design of an ultra-low energy, rakeness-based compressed sensing (CS) system that utilizes time-mode (TM) signal processing (TMSP). To realize TM CS operation, the presented implementation makes use of monostable multivibrator based analog-to-time converters, fixed-width pulse generators, basic digital gates and an asynchronous time-to-digital converter. The TM CS system was designed in a standard 0.18 µm IC process and operates from a supply voltage of 0.6V. The system is designed to accommodate data from 128 individual sensors and outputs 9-bit digital words with an average reconstruction SNR of 35.31 dB, a compression ratio of 3.2, with an energy dissipation per channel per measurement vector of 0.621 pJ at a rate of 2.23 k measurement vectors per second.

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