Feature extraction using extrema sampling of discrete derivatives for spike sorting in implantable upper-limb neural prostheses

Next generation neural interfaces for upper-limb (and other) prostheses aim to develop implantable interfaces for one or more nerves, each interface having many neural signal channels that work reliably in the stump without harming the nerves. To achieve real-time multi-channel processing it is important to integrate spike sorting on-chip to overcome limitations in transmission bandwidth. This requires computationally efficient algorithms for feature extraction and clustering suitable for low-power hardware implementation. This paper describes a new feature extraction method for real-time spike sorting based on extrema analysis (namely positive peaks and negative peaks) of spike shapes and their discrete derivatives at different frequency bands. Employing simulation across different datasets, the accuracy and computational complexity of the proposed method are assessed and compared with other methods. The average classification accuracy of the proposed method in conjunction with online sorting (O-Sort) is 91.6%, outperforming all the other methods tested with the O-Sort clustering algorithm. The proposed method offers a better tradeoff between classification error and computational complexity, making it a particularly strong choice for on-chip spike sorting

A Sinusoidal Current Driver With an Extended Frequency Range and Multifrequency Operation for Bioimpedance Applications

This paper describes an alternative sinusoidal current driver suitable for bioimpedance applications where high frequency operation is required. The circuit is based on a transconductor and provides current outputs with low phase error for frequencies around its pole frequency. This extends the upper frequency operational limit of the current driver. Multifrequency currents can be generated where each individual frequency is phase corrected. Analysis of the circuit is presented together with simulation and experimental results which demonstrate the proof of concept for both single and dual frequency current drivers. Measurements on a discrete test version of the circuit demonstrate a phase reduction from 25 ^{\circ} to 4 ^{\circ} at 3 MHz for 2 mAp-p output current. The output impedance of the current driver is essentially constant at about 1.1 M \Omega over a frequency range of 100 kHz to 5 MHz due to the introduction of the phase compensation. The compensation provides a bandwidth increase of a factor of about six for a residual phase delay of 4 ^{\circ

Advances in Microelectronics for Implantable Medical Devices

Implantable medical devices provide therapy to treat numerous health conditions as well as monitoring and diagnosis. Over the years, the development of these devices has seen remarkable progress thanks to tremendous advances in microelectronics, electrode technology, packaging and signal processing techniques. Many of today’s implantable devices use wireless technology to supply power and provide communication. There are many challenges when creating an implantable device. Issues such as reliable and fast bidirectional data communication, efficient power delivery to the implantable circuits, low noise and low power for the recording part of the system, and delivery of safe stimulation to avoid tissue and electrode damage are some of the challenges faced by the microelectronics circuit designer. This paper provides a review of advances in microelectronics over the last decade or so for implantable medical devices and systems. The focus is on neural recording and stimulation circuits suitable for fabrication in modern silicon process technologies and biotelemetry methods for power and data transfer, with particular emphasis on methods employing radio frequency inductive coupling. The paper concludes by highlighting some of the issues that will drive future research in the field

Low Computational Sensing with Goertzel Filtering for Mobile Industrial IoT Devices

Internet-of Things (IoT) is getting connected to an increasing number of mobile devices such as autonomous vehicles, drones and robots. Termed as Mobile Industrial Internet-of Things (MI²oT) devices in this paper, a key requirement of these devices is to accurately estimate range and Doppler in various applications, in addition to data communication. Research efforts therefore include incorporating MI²-oT devices with high-data rate communications together with Frequency Modulated Continuous Wave Radar (FMCW) sensing capabilities. Range and Doppler sensing, in FMCW radars is undertaken by a twostage Fast Fourier Transform (FFT) which is computationally demanding. It is challenging to design baseband processing with FFTs that can be implemented as low computational hardware or application specific integrated circuits (ASIC) in MI²-oT devices. This paper, presents a novel range and Doppler sensing technique based on Goertzel filtering, leading to considerable reduction in computations compared to the FFT. FMCW radar with Goertzel filtering and FFT are examined in three cases viz., sensing the range and velocity of a car, vibration and respiration monitoring. Simulation results show a computation reduction of the order of 6.3×, 7.7× and 8.1× \u1d422\u1d427Giga-operations per second (GOPS) for the three cases respectively. The reduced computations increase the feasibility of implementing range and Doppler sensing in MI²oT devices which have restricted computational resources

A framework for adapting deep brain stimulation using Parkinsonian state estimates

The mechanisms underlying the beneficial effects of deep brain stimulation (DBS) for Parkinson's disease (PD) remain poorly understood and are still under debate. This has hindered the development of adaptive DBS (aDBS). For further progress in aDBS, more insight into the dynamics of PD is needed, which can be obtained using machine learning models. This study presents an approach that uses generative and discriminative machine learning models to more accurately estimate the symptom severity of patients and adjust therapy accordingly. A support vector machine is used as the representative algorithm for discriminative machine learning models, and the Gaussian mixture model is used for the generative models. Therapy is effected using the state estimates obtained from the machine learning models together with a fuzzy controller in a critic-actor control approach. Both machine learning model configurations achieve PD suppression to desired state in 7 out of 9 cases; most of which settle in under 2 s

Integrated Circuits for Medical Ultrasound Applications: Imaging and Beyond

Medical ultrasound has become a crucial part of modern society and continues to play a vital role in the diagnosis and treatment of illnesses. Over the past decades, the develop- ment of medical ultrasound has seen extraordinary progress as a result of the tremendous research advances in microelectronics, transducer technology and signal processing algorithms. How- ever, medical ultrasound still faces many challenges including power-efficient driving of transducers, low-noise recording of ultrasound echoes, effective beamforming in a non-linear, high- attenuation medium (human tissues) and reduced overall form factor. This paper provides a comprehensive review of the design of integrated circuits for medical ultrasound applications. The most important and ubiquitous modules in a medical ultrasound system are addressed, i) transducer driving circuit, ii) low- noise amplifier, iii) beamforming circuit and iv) analog-digital converter. Within each ultrasound module, some representative research highlights are described followed by a comparison of the state-of-the-art. This paper concludes with a discussion and recommendations for future research directions

A 32-by-32 CMOS microelectrode array for capacitive biosensing and impedance spectroscopy

This paper presents the design of a 1024-channel dual-modality CMOS biosensor suitable for both capacitive sensing and impedance spectroscopy. The chip serves as a platform for detection, localization and monitoring of bacteria and can be adopted for affinity-based assays. The chip features a 32×32 array of unpassivated metal electrodes formed on the top metal of a 0.18μm CMOS process, with an overall sensing area of 2.06 mm2. The system design is based on a shared in-pixel integrator that can be used as a charge amplifier for capacitive sensing (CS) or as part of a transimpedance amplifier for electrical impedance spectroscopy (EIS). The CS mode is capable of a operation bandwidth of 50 MHz at a current consumption of 82 μA per pixel. The EIS channel operates over a bandwidth between 100 Hz and 1 MHz with a total input-referred current noise of 48 pArms and a current consumption of 210 μA per channel

Optimized Lateral Flow Immunoassay Reader for the Detection of Infectious Diseases in Developing Countries

Detection and control of infectious diseases is a major problem, especially in developing countries. Lateral flow immunoassays can be used with great success for the detection of infectious diseases. However, for the quantification of their results an electronic reader is required. This paper presents an optimized handheld electronic reader for developing countries. It features a potentially low-cost, low-power, battery-operated device with no added optical accessories. The operation of this proof of concept device is based on measuring the reflected light from the lateral flow immunoassay and translating it into the concentration of the specific analyte of interest. Characterization of the surface of the lateral flow immunoassay has been performed in order to accurately model its response to the incident light. Ray trace simulations have been performed to optimize the system and achieve maximum sensitivity by placing all the components in optimum positions. A microcontroller enables all the signal processing to be performed on the device and a Bluetooth module allows transmission of the results wirelessly to a mobile phone app. Its performance has been validated using lateral flow immunoassays with influenza A nucleoprotein in the concentration range of 0.5 ng/mL to 200 ng/mL

Low Power Analog Processing for Ultra-High-Speed Receivers with RF Correlation

Ultra-high-speed data communication receivers (Rxs) conventionally require analog digital converters (ADC)s with high sampling rates which have design challenges in terms of adequate resolution and power. This leads to ultra-high-speed Rxs utilising expensive and bulky high-speed oscilloscopes which are extremely inefficient for demodulation, in terms of power and size. Designing energy-efficient mixed-signal and baseband units for ultra-high-speed Rxs requires a paradigm approach detailed in this paper that circumvents the use of power-hungry ADCs by employing low-power analog processing. The low-power analog Rx employs direct-demodulation with RF correlation using low-power comparators. The Rx is able to support multiple modulations with highest modulation of 16-QAM reported so far for direct-demodulation with RF correlation. Simulations using Matlab, Simulink R2020a® indicate sufficient symbol-error rate (SER) performance at a symbol rate of 8 GS/s for the 71 GHz Urban Micro Cell and 140 GHz indoor channels. Power analysis undertaken with current analog, hybrid and digital beamforming approaches requiring ADCs indicates considerable power savings. This novel approach can be adopted for ultra-high-speed Rxs envisaged for beyond fifth generation (B5G)/sixth generation (6G)/ terahertz (THz) communication without the power-hungry ADCs, leading to low-power integrated design solutions