• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.193-196
• /
• 1995

An algorithm using topological mapping has been developed for a real-time detection of the QRS complexes of ECG signals. As a measurement of QRS complex energy, we used topological mapping from one dimensional sampled ECG signals to two dimensional vectors. These vectors are reconstructed with the sampled ECG signals and the delayed ones. In this method, the detection rates of CRS complex vary with the parameters such as R-R interval average and peak detection threshold coefficient. We use mean, median, and iterative method to determint R-R interval average and peak estimation. We experiment on various value of search back coefficient and peak detection threshold coefficient to find optimal rule.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.6
• /
• pp.1194-1199
• /
• 2009

Heart rate may be a very important parameter in human health. To extract heart rate, the electrocardiogram(ECG) is commonly used. But the ECG acquisition procedure is very complex. On the other hand, the acquisition of pulse wave or photoplethysmogram(PPG) is very easy. However, the peak of PPG is not so sharp as ECG. This study tries to enhance the performance of peak detection in PPG signal. The method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.7 no.1
• /
• pp.50-54
• /
• 2014

This study presents a method for heart rate detection using oscillation wave signal and tries to enhance the performance of peak detection. For this objective, the method uses the average slopes around the main peak. The crossing point of the increasing and the decreasing slopes is selected as the peak point of heart rate period. The proposed method showed smoothed heart rate graph and reduced irregularity in heart rate values.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.6
• /
• pp.666-674
• /
• 2019

In the mass spectrum of the mass spectrometer, the spectrum of the low peak adjacent to the spectrum having the high peak value is connected to each other and thus the separation is difficult. This inter-spectral overlap causes degradation of the mass spectral detection performance and resolution. In this paper, we propose a method to improve the mass spectrum detection performance and peak accuracy of residual gas analyzer. The type discrimination according to the characteristics of the ion signal block and the pre-correction for the peak position error can separate and detect the spectrum of the low peak connected to the adjacent spectra. To verify the performance of the proposed method, we compared the proposed method with the conventional method in simulations using actual ion signals obtained from the mass spectrometer under development.

• Proceedings of the IEEK Conference
• /
• 2001.06b
• /
• pp.149-152
• /
• 2001

In this paper, a 1.5V 2㎓ low-power peak detector is presented. Analyzing the designed peak detector circuit which is composed with two NMOSs, two diodes, and two capacitors, the detection characteristic relationships are derived. The simulation results with SPICE for 2㎓ pulse signals and sinusoidal signals on the 1.5V supply voltages show the good detection characteristics for input signal levels of 50㎷~500㎷, and show very small power dissipation of 0.332㎽.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.1
• /
• pp.102-112
• /
• 1992

We have already proposed an extended fuzzy clustering algorithm which considers the importance of the data to be classified in a previous paper. In this paper, we suggest the extended fuzzy clustering algorithm based new method to slove a multiple peak detection problem, and prove experimently that this algorithm can detect the multiple peak adaptively to the noise and the shape of peaks.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.6
• /
• pp.138-144
• /
• 2013

In XRF systems, various techniques have been developed for the synthesis of pulse shapes using digital methods instead of traditional analog methods. Trapezoidal pulse shaping algorithms can be used for digital multi-channel pulse height analysis in X-ray spectrometer systems. In this paper, an efficient trapezoidal filter architecture is presented. In addition, we present a hardware-efficient peak value detection algorithm. By the proposed algorithm, peak value detection error is decreased by half compared with the conventional algorithm. The proposed Digital Pulse Processing(DPP) algorithm is designed using Verilog HDL and implemented using an FPGA on a test board. It is demonstrated that the implemented DPP board works successfully in practical XRF systems.

• Journal of Broadcast Engineering
• /
• v.19 no.6
• /
• pp.967-970
• /
• 2014

An efficient peak detection algorithm in magnitude spectrum is proposed to distinguish the M-frequency shift keying(FSK) signals from other digitally modulated signal. In addition, recognition of the modulation order estimation of FSK signals is also studied based on the fact that the magnitude spectrum of FSK signals reveals the number of peaks equal to the modulation order. When no a priori information about the signals, we utilize the histogram of the magnitude spectrum to determine the threshold which is important factor in peak detection algorithm. The simulation results show high probability of classification under 500 symbols and signal-to-noise ratio(SNR) higher than 4dB.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.4
• /
• pp.91-98
• /
• 2017

Previous works for detecting arrhythmia have mostly used nonlinear method such as artificial neural network, fuzzy theory, support vector machine to increase classification accuracy. Most methods require higher computational cost and larger processing time. Therefore it is necessary to design efficient algorithm that classifies PVC(premature ventricular contraction) and decreases computational cost by accurately detecting minimal feature point based on only R peak through optimal R wave. We propose an optimal R wave detection and PVC classification method through extracting minimal feature point in IoT environment. For this purpose, we detected R wave through optimal threshold value and extracted RR interval and R peak pattern from noise-free ECG signal through the preprocessing method. Also, we classified PVC in realtime through RR interval and R peak pattern. The performance of R wave detection and PVC classification is evaluated by using record of MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.758% in R wave detection and the rate of 93.94% in PVC classification.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.21 no.11
• /
• pp.997-1004
• /
• 2011

A fault detection algorithm of a charge and discharge system to ensure the safe use of hybrid electric vehicle is proposed in this paper. This algorithm can be used as a complementary way to existing fault detection technique for a charge and discharge system. The proposed algorithm uses a SVDD technique, which additionally utilizes two methods for learning a large amount of data; one is to incrementally learn a large amount of data, the other one is to remove the data that does not affect the next learning using a new data reduction technique. Removal of data is selected by using lines connecting support vectors. In the proposed method, the data processing speed is drastically improved and the storage space used is remarkably reduced than the conventional methods using the SVDD technique only. A battery data and speed data of a commercial hybrid electrical vehicle are utilized in this study. A fault boundary is produced via SVDD techniques using the input and output in normal operation of the system without using mathematical modeling. A fault detection simulation is performed using both an artificial fault data and the obtained fault boundary via SVDD techniques. In the fault detection simulation, fault detection time via proposed algorithm is compared with that of the peak-peak method. Also the proposed algorithm is revealed to detect fault in the region where conventional peak-peak method is never able to do.