Yu Lin Wang

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.

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.

Rapid Amygdala Kindling Causes Motor Seizure and Comorbidity of Anxiety- and Depression-Like Behaviors in Rats.

Amygdala kindling is a model of temporal lobe epilepsy (TLE) with convulsion. The rapid amygdala kindling has an advantage on quick development of motor seizures and for antiepileptic drugs screening. The rapid amygdala kindling causes epileptogenesis accompanied by an anxiolytic response in early isolation of rat pups or depressive behavior in immature rats. However, the effect of rapid amygdala kindling on comorbidity of anxiety- and depression-like behaviors is unexplored in adult rats with normal breeding. In the present study, 40 amygdala stimulations given within 2 days were applied in adult Wistar rats. Afterdischarge (AD) and seizure stage were recorded throughout the amygdala kindling. Anxiety-like behaviors were evaluated by the elevated plus maze (EPM) test and open field (OF) test, whereas depression-like behaviors were assessed by the forced swim (FS) and sucrose consumption (SC) tests. A tonic-clonic convulsion was provoked in the kindle group. Rapid amygdala kindling resulted in a significantly lower frequency entering an open area of either open arms of the EPM or the central zone of an OF, lower sucrose intake, and longer immobility of the FS test in the kindle group. Our results suggest that rapid amygdala kindling elicited severe motor seizures comorbid with anxiety- and depression-like behaviors.

A Versatile Wireless Portable Monitoring System for Brain–Behavior Approaches

It is critical to set up a precise and feasible monitoring system for a variety of animal and human studies. A multichannel wireless system for monitoring physiological signals of freely moving rats is presented. This system combines electroencephalogram (EEG) and acceleration signals, enabling the study of association between brain and behavior. A combination of EEG and accelerometers eliminates the necessity for complicated video installation as well as time-consuming and tedious analysis of recorded videos. The IEEE 802.15.4 based wireless communication frees the experimental subject from the hassle of wires and reduces wire artifacts during recording. Long-period continuous recording was possible because of the low power feature of the system. Methods for automatic wake-sleep state discrimination and temporal lobe epileptic seizure detection are also proposed to demonstrate the advantages of the system. An accuracy of up to 96.22% for the automatic discrimination of wake-sleep states is an advantage of our system. In addition, the detection of amygdala-kindling temporal lobe seizures reaches 100% with zero false alarms, greatly saving manpower in the identification of temporal lobe epilepsy.

Dynamic Gop Structure Determination for Real-Time MPEG-4 Advanced Simple Profile Video Encoder

MPEG-4 advanced simple profile video provides I, P, and B-type frames in each GOP (group of pictures). To maximize the coding efficiency, it is important to determine the distribution of the three frame types, also called the GOP structure. In this paper, the GOP structure is determined dynamically in real time by using the information, such as Sad (sum of absolute difference) and Mad (mean of absolute difference), generated in the encoding process and little amount of computation is used. The algorithm first determines whether the current frame is an intra-picture or an inter-picture. If it is an inter-picture, one has to decide whether it is a P_picture or a B_picture. No more than 4 consecutive B_pictures can be employed to reduce the video buffer size. The proposed method is tested over a wide range of video sequences at different data rate conditions and produces consistent improvement compared to fixed GOP methods

Detection of spontaneous temporal lobe epilepsy in rats by means of 1-axis accelerometor signal

Epilepsy is the most common psychological disorders in humans. Patients suffering from epilepsy usually experience behavioral symptoms, such as involuntary movement and rage reaction. Conventional monitoring with electroencephalogram and video is unpleasant for patients and not feasible for long-term monitoring. In this work, a seizure detection system with an accelerometer set on the subjects head is proposed to monitor the behavioral activities and to detect the seizure events in subjects. The Wistar rats with temporal lobe epilepsy (TLE) were used in our experiment. To reduce the computational energy, only one axis signal was utilized for seizure detection. A three-state finite state machine (FSM) was applied to determine the seizure activities, and the temporal features of y-axis ACC signal were calculated for state transition. The results show that our proposed on-line detection method can achieve 100% accuracy with extremely low false alarm rate of 0.035. It has the advantage of easy measurement and good performance for real-world applicability.

On SOT coefficient ordering of a SPIHT coder and its fast analysis method

Evaluating coding efficiency in early coding stage is important in modern RDO compression methods. Bit-plane coding algorithms such as SPIHT are widely used in many scalable coders. The locality of a spatial-oriented tree (SOT) affects the coding efficiency of a SPIHT coder. In this paper, a fast algorithm is proposed to determine the numbers of bits to be consumed at all coding levels for a SPIHT coder without performing the entire coding process. The proposed algorithm can also evaluate the localities of various SOTs so that one can decide which types of SOTs should be used. Experiments of using different coefficient ordering schemes on construct SOTs for image compression using SPIHT are presented.

Epileptic Pattern Recognition and Discovery of the Local Field Potential in Amygdala Kindling Process

Epileptogenesis, which occurs in an epileptic brain, is an important focus for epilepsy. The spectral analysis has been popularly applied to study the electrophysiological activities. However, the resolution is dominated by the window function of the algorithm used and the sample size. In this report, a temporal waveform analysis method is proposed to investigate the relationship of electrophysiological discharges and motor outcomes with a kindling process. Wistar rats were subjected to electrical amygdala kindling to induce temporal lobe epilepsy. During the kindling process, different morphologies of afterdischarges (ADs) were found and a recognition method, using template matching techniques combined with morphological comparators, was developed to automatically detect the epileptic patterns. The recognition results were compared to manually labeled results, and 79%-91% sensitivity was found. In addition, the initial ADs (the first 10 s) of different seizure stages were specifically utilized for recognition, and an average of 85% sensitivity was achieved. Our study provides an alternative viewpoint away from frequency analysis and time-frequency analysis to investigate epileptogenesis in an epileptic brain. The recognition method can be utilized as a preliminary inspection tool to identify remarkable changes in a patients electrophysiological activities for clinical use. Moreover, we demonstrate the feasibility of predicting behavioral seizure stages from the early epileptiform discharges.

EMG based rehabilitation systems - approaches for ALS patients in different stages

For the patients suffering from amyotrophic lateral sclerosis (ALS), they are encouraged to exercise their bodies routinely to prevent or delay the paralysis of muscles. This work proposes an electromyogram (EMG) based ALS rehabilitation system via playing computer games. The multi-channel EMG measuring system and the controlled interfaces to computer games were developed. According to the symptoms of disability, the controls from different muscles are designed. For ALS patients in the early stage, the EMG electrodes were placed on the forearm to detect the finger gestures; for ALS patients in the middle stage, the EMG signals of upper extremity were employed to detect the hand gestures and arm moving; for the late ALS stage, the EMG electrodes were placed on chin to detect the facial expression. A commercial video game as well as a self-modified computer game are utilized in our rehabilitation systems. We believe that the patients are more preferable to exercise their bodies in a form of entertainment.

An open electronic system level multi-SPARC virtual platform and its toolchain

We present a multi-core virtual platform which follows single-core architecture, SPARC v8, available as an open source development suite. The proposed multi-SPARC system operates at electronic system level to accelerate its simulation speed. TLM channels are devised to connect the processors. To simplify the use of the proposed virtual platform, we define some specific APIs for data transaction and developers can simply follow the pre-defined protocol and complete the data transaction. We also implement the TLM interface for external modules to communicate with the host virtual platform. Furthermore, the proposed virtual platform is capable of running multiple applications with dynamic loading of application programs in run time. That means the multiple applications could execute sequentially without pre-loading all programs when initialized. The virtual platform with 4 SPARC cores can execute up to 1605.88K cycles per second on a 2.4 GHz Core 2 Duo machine. Moreover, the developers do not take too much effort to get used to our virtual platform, since its architecture follows the traditional one, and they could concentrate on architecture implementation as well.