1,720,968 research outputs found
A 1.52 uJ/classification Patient-Specific Seizure Classification Processor using Linear SVM
No full textThis paper presents an 8-channel electroencephalograph (EEG) classification processor for seizure detection and recording. To integrate 8 channels, an area- and energy-efficient filter architecture using Distributed Quad-LUT (DQ-LUT) is proposed, which reduces area by 64.2% with minimal overhead in power. delay product. The on-chip patient specific classification with a Linear Support-Vector Machine (SVM) results in 82.7% seizure detection accuracy with a 2 second latency using the CHB-MIT EEG database [1]. The overall energy efficiency is measuredN
A 1.83 μJ/Classification, 8-Channel, Patient-Specific Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine
No full textA non-linear support vector machine (NLSVM) seizure classification SoC with 8-channel EEG data acquisition and storage for epileptic patients is presented. The proposed SoC is the first work in literature that integrates a feature extraction (FE) engine, patient specific hardware-efficient NLSVM classification engine, 96 KB SRAM for EEG data storage and low-noise, high dynamic range readout circuits. To achieve on-chip integration of the NLSVM classification engine with minimum area and energy consumption, the FE engine utilizes time division multiplexing (TDM)-BPF architecture. The implemented log-linear Gaussian basis function (LL-GBF) NLSVM classifier exploits the linearization to achieve energy consumption of 0.39 mu J/operation and reduces the area by 28.2% compared to conventional GBF implementation. The readout circuits incorporate a chopper-stabilized DC servo loop to minimize the noise level elevation and achieve noise RTI of 0.81 mu. V-rms for 0.5-100 Hz bandwidth with an NEF of 4.0. The 5 x 5 mm(2) SoC is implemented in a 0.18 mu m 1P6M CMOS process consuming 1.83 /./ J/classification for 8-channel operation. SoC verification has been done with the Children's Hospital Boston-MIT EEG database, as well as with a specific rapid eye-blink pattern detection test, which results in an average detection rate, average false alarm rate and latency of 95.1%, 0.94% (0.27 false alarms/hour) and 2 s, respectively.N
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
A 1.83μJ/Classification Nonlinear Support-Vector-Machine-Based Patient-Specific Seizure Classification SoC
No full textN
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A 16-Channel Patient-Specific Seizure Onset and Termination Detection SoC With Impedance-Adaptive Transcranial Electrical Stimulator
No full textA 16-channel noninvasive closed-loop beginning-and end-of-seizure detection SoC is presented. The dual-channel charge recycled (DCCR) analog front end (AFE) achieves chopping and time-multiplexing an amplifier between two channels simultaneously which exploits fast-settling DC servo-loop with current consumption and NEF of 0.9 mu A/channel and 3.29/channel, respectively. The dual-detector architecture (D(2)A) classification processor utilizes two linear support-vector machine (LSVM) classifiers based on digital hysteresis to enhance both the sensitivity and the specificity simultaneously. The pulsating voltage transcranial electrical stimulator (PVTES) automatically configures the number of pulses to control the amount of charge delivered based on skin-electrode impedance variation in efforts to suppress the seizure activity, while burning only 2.45 mu W. The 25 mm(2) SoC implemented in 0.18 mu m CMOS consumes 2.73 mu J/classification for 16 channels with an average sensitivity, specificity, and latency of 95.7%, 98%, and 1 s, respectively.N
A 1.1mW Hybrid OFDM Ground Effect-Resilient Body Coupled Communication Transceiver for Head and Body Area Network
No full textA Hybrid OFDM transceiver for the head-and body-area network is presented. This is the first transceiver in the literature that mitigates all the body channel impairments at once. It combines baseband BPSK-OFDM with FSK to alleviate the impacts of variable ground effect and signal multipath on the body channel quality with measured BER improvement of >70% compared to FSK modulation. It can tolerate up to 20dB of channel gain variation. Skin-electrode contact impedance variation is also continuously monitored and compensated at both TX and RX. An 8-point FFT/IFFT with no floating point multipliers is utilized in H-OFDM TX and RX that reduces the gate count by 54% compared to conventional floating point multipliers. A glitch-free FSK demodulation RX with variable threshold limiter and all digital cycle correction is proposed to support a scalable data rate (200Kbps-2Mbps). The 0.54mm(2) transceiver in 65nm CMOS consumes 1.1mW.N
Design of Energy-Efficient On-Chip EEG Classification and Recording Processors for Wearable Environments
No full textClassification of EEG under wearable environment faces many challenges including motion artifact, electrode DC offset, noise and limited available energy source. This paper describes the design consideration of a multi-channel machine-learning based EEG classification and recording processors for wearable form-factor sensors. The goal is to optimize the detection performance while balancing the analog and digital signal processing to optimize its energy consumption. On-chip classification significantly helps achieving energy-efficiency by reducing the communication overhead of the data. With epileptic seizure detection and recording system examples, we start from choosing number of channels, the sampling rate, and how to effectively extract features out of the down-sampled data. After that, classification algorithms are also discussed in detail. When verified with the Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) EEG database, based on Repeated Random Sub-Sampling validation, the seizure detection sensitivity and specificity of the Non-Linear SVM are improved by 12.4% P and 3.56% P, respectively, compared to the Linear-SVM. The LSVM and NLSVM processors are fabricated in 0.18 mu m 1P6M CMOS and consume 1.52 mu J/classification and 1.34 mu J/classification, respectively. Finally, the on-chip memory requirements for storing the raw seizure data is discussed.N
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