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

Q-enhancement with on-chip inductor optimization for reconfigurable Δ-Σ radio-frequency ADC

The paper details on-chip inductor optimization for a reconfigurable continuous-time delta-sigma (Δ-Σ) modulator based radio-frequency analog-to-digital converter. Inductor optimisation enables the Δ-Σ modulator with Q enhanced LC tank circuits employing a single high Q-factor on-chip inductor and lesser quantizer levels thereby reducing the circuit complexity for excess loop delay, power dissipation and dynamic element matching. System level simulations indicate at a Q-factor of 75 Δ- Σ modulator with a 3-level quantizer achieves dynamic ranges of 106, 82 dB and 84 dB for RFID, TETRA, and Galileo over bandwidths of 200 kHz, 10 MHz and 40 MHz respectively

A wideband low-distortion CMOS current driver for tissue impedance analysis

Bioimpedance measurements are performed in a variety of medical applications including cancer detection in tissue. Such applications require wideband (typically 1 MHz) accurate ac current drivers with high output impedance and low distortion. This paper presents an integrated current driver that fulfills these requirements. The circuit uses negative feedback to accurately set the output current amplitude into the load. It was fabricated in a 0.35- μm complementary metal–oxide–semiconductor (CMOS) process technology, occupies a core area of 0.4 mm, and operates from ±2.5-V power supplies. For a maximum output current of 1mA p-p, the measured total harmonic distortion is below 0.1%, and the variability of the output current with respect to the load is below 0.5% up to 800 kHz increasing to 0.86% at 1 MHz. The current driver was tested for the detection of cancer sites from postoperative human colon specimens. The circuit is intended for use in active electrode applications

Dual Output Regulating Rectifier for an Implantable Neural Interface

This paper presents the design of a power management circuit consisting of a dual output regulating rectifier configuration featuring pulse width modulation (PWM) and pulse frequency modulation (PFM) to control the regulated output of 1.8 V, and 3.3 V from a single input ac voltage. The PFM control feedback consists of feedback-driven regulation to adjust the driving frequency of the power transistors through the buffers in the active rectifier. The PWM mode control provides a feedback loop to accurately adjust the conduction duration. The design also includes an adiabatic charge pump (CP) to power stimulators in an implantable neural interface. The adiabatic CP consists of latch up and power saving topologies to enhance its energy efficiency. Simulation results show that the dual regulating rectifier has 94.3% voltage conversion efficiency with an ac input magnitude of 3.5 Vp. The power transfer efficiency of the regulated 3.3 V output voltage is 82.3%. The dual output regulating rectifier topology is suitable for multi-functional implantable devices. The adiabatic CP has an overall efficiency of 92.9% with an overall on-chip capacitance of 60 pF. The circuit was designed in a 180-nm CMOS technology

A Compact CNN-Based Speech Enhancement With Adaptive Filter Design Using Gabor Function And Region-Aware Convolution

Speech enhancement (SE) is used in many applications, such as hearing devices, to improve speech intelligibility and quality. Convolutional neural network-based (CNN-based) SE algorithms in literature often employ generic convolutional filters that are not optimized for SE applications. This paper presents a CNN-based SE algorithm with an adaptive filter design (named ‘CNN-AFD’) using Gabor function and region-aware convolution. The proposed algorithm incorporates fixed Gabor functions into convolutional filters to model human auditory processing for improved denoising performance. The feature maps obtained from the Gabor-incorporated convolutional layers serve as learnable guided masks (tuned at backpropagation) for generating adaptive custom region-aware filters. The custom filters extract features from speech regions (i.e., ‘region-aware’) while maintaining translation-invariance. To reduce the high cost of inference of the CNN, skip convolution and activation analysis-wise pruning are explored. Employing skip convolution allowed the training time per epoch to be reduced by close to 40%. Pruning of neurons with high numbers of zero activations complements skip convolution and significantly reduces model parameters by more than 30%. The proposed CNN-AFD outperformed all four CNN-based SE baseline algorithms (i.e., a CNN-based SE employing generic filters, a CNN-based SE without region-aware convolution, a CNN-based SE trained with complex spectrograms and a CNN-based SE processing in the time-domain) with an average of 0.95, 1.82 and 0.82 in short-time objective intelligibility (STOI), perceptual evaluation of speech quality (PESQ) and logarithmic spectral distance (LSD) scores, respectively, when tasked to denoise speech contaminated with NOISEX-92 noises at −5, 0 and 5 dB signal-to-noise ratios (SNRs)

Wearable Neuromodulators

In neuromodulation, by delivering a form of stimulus to neural tissue the response of an associated neural circuit may be changed, enhanced or inhibited (i.e., modulated) as desired. This powerful technique may be used to treat various medical conditions as outlined in this chapter. After a brief introduction to the human nervous system, key example applications of electrical neuromodulation such as cardiac pacemakers, devices for pain relief, deep brain stimulation, cochlear implant and visual prosthesis and their respective methods of deployment are discussed. Furthermore, key features of wearable neuromodulators with reference to some existing devices are briefly reviewed. This chapter is concluded by a case study on design and development of a wearable device with non-invasive electrodes for treating lower urinary tract dysfunctions after spinal cord injury

Efficient Approximation of Action Potentials with High-Order Shape Preservation in Unsupervised Spike Sorting

This paper presents a novel approximation unit added to the conventional spike processing chain which provides an appreciable reduction of complexity of the high-hardware cost feature extractors. The use of the Taylor polynomial is proposed and modelled employing its cascaded derivatives to non-uniformly capture the essential samples in each spike for reliable feature extraction and sorting. Inclusion of the approximation unit can provide 3X compression (i.e. from 66 to 22 samples) to the spike waveforms while preserving their shapes. Detailed spike waveform sequences based on in-vivo measurements have been generated using a customized neural simulator for performance assessment of the approximation unit tested on six published feature extractors. For noise levels σN between 0.05 and 0.3 and groups of 3 spikes in each channel, all the feature extractors provide almost same sorting performance before and after approximation. The overall implementation cost when including the approximation unit and feature extraction shows a large reduction (i.e. up to 8.7X) in the hardware costly and more accurate feature extractors, offering a substantial improvement in feature extraction design

A Bidirectional ASIC for Active Microchannel Neural Interfaces

Closed-loop neural prostheses have been widely used as a therapeutic strategy for a range of neurological, inflammatory, and cardiac disorders. Vagus nerve stimulation has shown promising results for the monitoring and treatment of post-operation symptoms of heart transplant recipients. A prime candidate for selective control of vagal fibres is the microchannel neural interface (MNI), which provides a suitable environment for neural growth and enables effective control of the neural activity in a bidirectional system. This paper presents the design and simulation of an ASIC in 180-nm high-voltage CMOS technology, capable of concurrent stimulation and neural recording with artifact reduction in a seven-channel MNI. The analog front-end amplifies action potentials with a gain of 40 dB, presenting a common-mode rejection ratio of 81 dB at 1 kHz and a noise efficiency factor of 5.13 over the 300 Hz to 5 kHz recording bandwidth. A 42-V-compliant stimulation module operates concurrently and independently across the seven channels

Toward on-demand deep brain stimulation using online Parkinson’s disease prediction driven by dynamic detection

In Parkinson’s disease (PD), on-demand deep brain stimulation (DBS) is required so that stimulation is regulated to reduce side effects resulting from continuous stimulation and PD exacerbation due to untimely stimulation. Also, the progressive nature of PD necessitates the use of dynamic detection schemes that can track the nonlinearities in PD. This paper proposes the use of dynamic feature extraction feature extraction and dynamic pattern classification to achieve dynamic PD detection taking into account the demand for high accuracy, low computation and real-time detection. The dynamic feature extraction and dynamic pattern classification are selected by evaluating a subset of feature extraction, dimensionality reduction and classification algorithms that have been used in brain machine interfaces. A novel dimensionality reduction technique, the maximum ratio method (MRM) is proposed, which provides the most efficient performance. In terms of accuracy and complexity for hardware implementation, a combination having discrete wavelet transform for feature extraction, MRM for dimensionality reduction and dynamic k-nearest neighbor for classification was chosen as the most efficient. It achieves mean accuracy measures of classification accuracy 99.29%, F1-score of 97.90% and a choice probability of 99.86%

Design of a CMOS active electrode IC for wearable electrical impedance tomography systems

This paper describes the design of an active electrode integrated circuit (IC) for a wearable electrical impedance tomography (EIT) system required for real time monitoring of neonatal lung function. The IC comprises a wideband high power current driver (up to 6 mAp-p output current), a low noise voltage amplifier and two shape sensor buffers. The IC has been designed in a 0.35-μm CMOS technology. It operates from ±9 V power supplies and occupies a total die area of 5 mm2. Post-layout simulations are presented