• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.5
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• pp.380-388
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• 2004

This study proposed an improved spike detection algorithm which automatically detects suitable spike threshold on the amplitude of surface electromyography(SEMG) signal during isometric contraction. The EMG data from the low back muscles was obtained in six channels and the proposed signal processing algorithm is compared with the median frequency and Gabriel's spike parameter. As a result, the reliability of spike parameter was inferior to the median frequency. This fact indicates that a spike parameter is inadequate for analysis of multi-channel EMG signal. Because of uncertainty of fixed spike threshold, the improved spike detection algorithm was proposed. It automatically detects suitable spike threshold depending on the amplitude of the EMG signal, and the proposed algorithm was able to detect optimal threshold based on mCFAR(modified Constant False Alarm Rate) in the every EMG channel. In conclusion, from the reliability points of view, neither median frequency nor existing spike detection algorithm was superior to the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.10
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• pp.1951-1958
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• 2011

The objective of this study is to analyze the performance of digital low-pass differentiators(LPD) and then to provide a method to select effective LPD filter, for detecting spikes of surface motor unit action potentials(MUAP). The successful spike detection of MUAPs is a first important step for EMG signal decomposition. The performances of simple and weighted LPD(SLPD and WLPD) filters are analyzed based on different filter lengths and varying MUAPs from simulated surface EMG signals. The SNR improving coefficient and effective MUAP duration range from the analysis results can be used to select proper LPD filters under the varying conditions of surface EMG.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.783-786
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• 1997

Automatic realization of on-off human decision making was derived based on a conditional probability. Following the proposed procedure, problems of insulator washing timing in power substations and spike detection on EEG(electroencephalogram) records were appropriately solved.

• KIEE International Transactions on Power Engineering
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• v.3A no.2
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• pp.85-92
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• 2003

When a current transformer (CT) is saturated, the wave-shape of the secondary current is distorted and contains points of inflection, which correspond to the start or end of each saturation period. Discontinuity in the first-difference function of the current arises at points of inflection, where the second and third differences convert into pulses that can be used to detect saturation. This paper describes the design and evaluation of a CT saturation detection technique using the third-difference function and includes the countermeasure for a spike signal. Test results clearly demonstrate that the algorithm successfully detects the start and end of each saturation period irrespective of the remanent flux and magnetization inductance in the saturated region. This paper concludes by describing the results of hardware implementation of the algorithm using a DSP.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.558-559
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• 2007

The detection of neural spike activity is a technical challenge that is very important for studying many types of brain function. On temporal recordings of firing events or interspike interval series of neural signal, spike pattern correspond to action will be repeated in the presence of background noise and they need to be detected to develop higher applications. We will introduce new method to find these patterns in raw multitrial data and is tested on surrogate data sets with the main target to get meaningful analysis of electrophysiological data from microelectrode arrays (MEA).

• Journal of the Korean Institute of Telematics and Electronics
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• v.17 no.6
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• pp.28-32
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• 1980

A digital system has been designed for the detection of abnormal spikes appearing in the epileptic patient's electroencephalogram(EEG). The detection is based on the waveform characteristics of spikes, such as the large slope, the sharpness of the apex, and the time duration of the spike. After the patient's data are collected and processed suing a minicomputer and A/D converter, the computer algorithms recognize the spikes based on the parameters representing the above waveform characteristics.

• The Journal of the Acoustical Society of Korea
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• v.20 no.5
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• pp.21-27
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• 2001

Impulsive sound and vibration signals in gear system are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in condition monitoring of gear system. The traditional continuous wavelet transform has been used for detection of impulsive signals. However, it is often difficult for the continuous wavelet transform to identify spikes at high frequency and meshing frequencies at low frequency simultaneously since the continuous wavelet transform is to apply the linear scaling (a-dilation) to the mother wavelet. In this paper, the spike wavelet transform is developed to extract these impulsive sound and vibration signals. Since the spike wavelet transform is to apply the non-linear scaling, it has better time resolution at high frequency and frequency resolution at low frequency than that of the continuous wavelet transform respectively. The spike wavelet transform can be, therefore, used to detect fault position clearly without the loss of information for the damage of a gear system. The spike wavelet transform is successfully is applied to detection of the gear fault with tip breakage.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.99-102
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• 1997

A neural spike sorting technique has been developed that also has the capability of template learning. A system of software has been written that first obtains the templates by learning, and then performs the sorting of the spikes into single units. The spike sorting can be done in real time. The template learning consists of spike detection based on the discrete Haar transform (DHT), feature extraction by clustering of spike amplitude and duration, classification based on rms error, and fabrication of templates. The developed algorithms can be implemented into real time systems using digital signal processors.

• Journal of Biomedical Engineering Research
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• v.2 no.2
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• pp.83-88
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• 1981

A method of detecting abnormal spikes occuring in the EEG of subjects suffering from epilepsy is studied. The detection scheme is to take the first derivative of EEG and to determine if it exceed some threshold value. This study is focused on the digital signal processing for detecting abnormal spikes using microcomputer.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.2
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• pp.70-78
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• 1998

Spike detection in long-term EEG monitoring forepilepsy by wavelet transform(WT), artificial neural network(ANN) and the expert system is presented. First, a small set of wavelet coefficients is used to represent the characteristics of a singlechannel epileptic spikes and normal activities. In this stage, two parameters are also extracted from the relation between EEG activities before the spike event and EEG activities with the spike. then, three-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained from the first stage. Spikes are identified in individual EEG channels by 16 identical neural networks. Finally, 16-channel expert system based on the context information of adjacent channels is introducedto yield more reliable results and reject artifacts. In this study, epileptic spikes and normal activities are selected from 32 patient's EEG in consensus among experts. The result showed that the WT reduced data input size and the preprocessed ANN had more accuracy than that of ANN with the same input size of raw data. Ina clinical test, our expert rule system was capable of rejecting artifacts commonly found in EEG recodings.