Herming Chiueh

A Fully Integrated 8-Channel Closed-Loop Neural-Prosthetic CMOS SoC for Real-Time Epileptic Seizure Control

An 8-channel closed-loop neural-prosthetic SoC is presented for real-time intracranial EEG (iEEG) acquisition, seizure detection, and electrical stimulation in order to suppress epileptic seizures. The SoC is composed of eight energy-efficient analog front-end amplifiers (AFEAs), a 10-b delta-modulated SAR ADC (DMSAR ADC), a configurable bio-signal processor (BSP), and an adaptive high-voltage-tolerant stimulator. A wireless power-and-data transmission system is also embedded. By leveraging T-connected pseudo-resistors, the high-pass (low-pass) cutoff frequency of the AFEAs can be adjusted from 0.1 to 10 Hz (0.8 to 7 kHz). The noise-efficiency factor (NEF) of the AFEA is 1.77, and the DMSAR ADC achieves an ENOB of 9.57 bits. The BSP extracts the epileptic features from time-domain entropy and frequency spectrum for seizure detection. A constant 30- μA stimulus current is delivered by closed-loop control. The acquired signals are transmitted with on-off keying (OOK) modulation at 4 Mbps over the MedRadio band for monitoring. A multi-LDO topology is adopted to mitigate the interferences across different power domains. The proposed SoC is fabricated in 0.18- μm CMOS and occupies 13.47 mm2. Verified on Long Evans rats, the proposed SoC dissipates 2.8 mW and achieves high detection accuracy (> 92%) within 0.8 s.

Closed-loop seizure control on epileptic rat models

In this paper numerous alternative treatments in addition to pharmacological therapy are proposed for their use in epileptic patients. Epileptic animal models can play a crucial role in the performance evaluation of new therapeutic techniques. The objective of this research is to first develop various epileptic rat models; second, develop a portable wireless closed-loop seizure controller including on-line seizure detection and real-time electrical stimulation for seizure elimination; and third, apply the developed seizure controller to the animal models to perform on-line seizure elimination. The closed-loop seizure controller was applied to three Long-Evans rats with spontaneous spike-wave discharges (non-convulsive) and three Long-Evans rats with epileptiform activities induced by pentylenetetrazol (PTZ) injection (convulsive) for evaluation. The seizure detection accuracy is greater than 92% (up to 99%), and averaged seizure detection latency is less than 0.6 s for both spontaneous non-convulsive and PTZ-induced convulsive seizures. The average false stimulation rate is 3.1%. Near 30% of PTZ-induced convulsive seizures need more than two times of 0.5 s electrical stimulation for suppression and 90% of the non-convulsive seizures can be suppressed by only one 0.5 s electrical stimulation.

A hierarchical approach for online temporal lobe seizure detection in long-term intracranial EEG recordings

OBJECTIVE Around 1% of the worlds population is affected by epilepsy, and nearly 25% of patients cannot be treated effectively by available therapies. The presence of closed-loop seizure-triggered stimulation provides a promising solution for these patients. Realization of fast, accurate, and energy-efficient seizure detection is the key to such implants. In this study, we propose a two-stage on-line seizure detection algorithm with low-energy consumption for temporal lobe epilepsy (TLE). APPROACH Multi-channel signals are processed through independent component analysis and the most representative independent component (IC) is automatically selected to eliminate artifacts. Seizure-like intracranial electroencephalogram (iEEG) segments are fast detected in the first stage of the proposed method and these seizures are confirmed in the second stage. The conditional activation of the second-stage signal processing reduces the computational effort, and hence energy, since most of the non-seizure events are filtered out in the first stage. MAIN RESULTS Long-term iEEG recordings of 11 patients who suffered from TLE were analyzed via leave-one-out cross validation. The proposed method has a detection accuracy of 95.24%, a false alarm rate of 0.09/h, and an average detection delay time of 9.2 s. For the six patients with mesial TLE, a detection accuracy of 100.0%, a false alarm rate of 0.06/h, and an average detection delay time of 4.8 s can be achieved. The hierarchical approach provides a 90% energy reduction, yielding effective and energy-efficient implementation for real-time epileptic seizure detection. SIGNIFICANCE An on-line seizure detection method that can be applied to monitor continuous iEEG signals of patients who suffered from TLE was developed. An IC selection strategy to automatically determine the most seizure-related IC for seizure detection was also proposed. The system has advantages of (1) high detection accuracy, (2) low false alarm, (3) short detection latency, and (4) energy-efficient design for hardware implementation.

A Dynamic Thermal Management Circuit for System-On-Chip Designs

A novel fully integrated dynamic thermal management circuit for system-on-chip design is proposed. Instead of worst-case thermal management used in conventional systems, this design yields continual monitoring of thermal activity and reacts to specified conditions. With the above system, we are able to incorporate on-chip power/speed modulation and integrated multi-stage fan controllers, which allows us to achieve nominal power dissipation and ensure operation within specification. Both architecture and circuitry are optimized for modern system-on-chip designs. This design yields intricate control and optimal mangement with little system overhead and minimum hardware requirements, as well as provides the flexibility to support different thermal mangement algorithms.

The Implementation of a Low-Power Biomedical Signal Processor for Real-Time Epileptic Seizure Detection on Absence Animal Models

Epilepsy is one of the most common neurological disorders, with a worldwide prevalence of approximately 1%. A considerable portion of epilepsy patients cannot be treated sufficiently by todays available therapies. Implantable closed-loop neurostimulation is an innovative and effective method for seizure control. A real-time seizure detector is the kernel of a closed-loop seizure controller. In this paper, a low-power biomedical signal processor based on reduced instruction set computer (RISC) architecture for real-time seizure detection is implemented to achieve low-power consumption and perform continuous and real-time processing. The low-power processor is implemented in a 0.18

A novel fully integrated fan controller for advanced computer systems

\mu

A novel model for on-chip heat dissipation

m complementary–metal–oxide semiconductor technology to verify functionality and capability. The measurement results show the implemented processor can reduce over 90% power consumption compared with our previous prototype, which was implemented on an enhanced 8051 microprocessor. This seizure detector was applied to the continuous EEG signals of four Long–Evans rats with spontaneous absence seizures. It also processed 24 h long-term and uninterrupted EEG sequence. The developed seizure detector can be applied for online seizure monitoring and integrated with an electrical stimulator to perform a closed-loop seizure controller in the future.

Evaluation of subcortical grey matter abnormalities in patients with MRI-negative cortical epilepsy determined through structural and tensor magnetic resonance imaging

A low-cost, high-efficiency, compact architecture of a PWM (pulse-width-modulation) drive fan controller is designed for use in an embedded multicomputer system with an integrated hierarchical thermal management scheme. This pure digital design yields lower cost and higher conventional linear drive fan providing the functionality and advantages of PWM drive fan controllers. The implementation and system integration of this circuit is also described in this paper.

The design of CMOS general-purpose analog front-end circuit with tunable gain and bandwidth for biopotential signal recording systems

This paper presents an analytical model for on-chip heat dissipation in VLSI design. A chip and its test configuration also are developed to verify modeling results. The model and chip are representative of general IC packages. Our research shows that circuit location on a chip determines its default offset temperature and heat transport properties, which must be considered for accurate prediction of junction temperature and electrothermal analysis. The model yields insights about on-chip heat dissipation, which are very useful for mixed-signal VLSI designs and circuit reliability analysis.

High linear voltage references for on-chip CMOS smart temperature sensor from −60°C to 140°C

BackgroundAlthough many studies have found abnormalities in subcortical grey matter (GM) in patients with temporal lobe epilepsy or generalised epilepsies, few studies have examined subcortical GM in focal neocortical seizures. Using structural and tensor magnetic resonance imaging (MRI), we evaluated subcortical GM from patients with extratemporal lobe epilepsy without visible lesion on MRI. Our aims were to determine whether there are structural abnormalities in these patients and to correlate the extent of any observed structural changes with clinical characteristics of disease in these patients.MethodsTwenty-four people with epilepsy and 29 age-matched normal subjects were imaged with high-resolution structural and diffusion tensor MR scans. The patients were characterised clinically by normal brain MRI scans and seizures that originated in the neocortex and evolved to secondarily generalised convulsions. We first used whole brain voxel-based morphometry (VBM) to detect density changes in subcortical GM. Volumetric data, values of mean diffusivity (MD) and fractional anisotropy (FA) for seven subcortical GM structures (hippocampus, caudate nucleus, putamen, globus pallidus, nucleus accumbens, thalamus and amygdala) were obtained using a model-based segmentation and registration tool. Differences in the volumes and diffusion parameters between patients and controls and correlations with the early onset and progression of epilepsy were estimated.ResultsReduced volumes and altered diffusion parameters of subcortical GM were universally observed in patients in the subcortical regions studied. In the patient-control group comparison of VBM, the right putamen, bilateral nucleus accumbens and right caudate nucleus of epileptic patients exhibited a significantly decreased density Segregated volumetry and diffusion assessment of subcortical GM showed apparent atrophy of the left caudate nucleus, left amygdala and right putamen; reduced FA values for the bilateral nucleus accumbens; and elevated MD values for the left thalamus, right hippocampus and right globus pallidus A decreased volume of the nucleus accumbens consistently related to an early onset of disease. The duration of disease contributed to the shrinkage of the left thalamus.ConclusionsPatients with neocortical seizures and secondary generalisation had smaller volumes and microstructural anomalies in subcortical GM regions. Subcortical GM atrophy is relevant to the early onset and progression of epilepsy.