• 한국통신학회논문지
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• 제40권4호
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• pp.731-739
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• 2015

부정맥 심전도 신호에는 전도장애 및 발생부위에 따라 다양한 비정상 모양을 띄는 특이심박들이 포함되어 있고, 이들 특이심박은 부정맥 등의 심장질환을 진단하는데 있어 매우 중요하다. 본 논문에서는 심실질환에 관련한 PVC 특이심박 검출 알고리즘을 제안한다. PVC 특이심박에서는 심전도 신호의 구성요소 가운데 QRS 군의 왜곡이 발생하는 특징이 있다. 따라서 QRS 군의 왜곡 정도에 따라 PVC 특이심박을 검출할 수 있다. 먼저 R-peak의 전위, 첨도, 주기를 사용하여 QRS 군의 왜곡을 정량화하고, 이들 값들의 평균과 표준편차를 이용하여 정상 심박과의 왜곡의 정도에 따라 PVC 특이심박을 검출한다. 제안한 알고리즘은 MIT-BIH 부정맥 데이터베이스 중 심실질환과 관계되는 AAMI-V class 타입의 특이심박을 평균 98% 이상을 검출할 수 있었다.

• 대한의용생체공학회:의공학회지
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• 제36권5호
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• pp.143-149
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• 2015

In this paper, we proposed a novel method for automatic detection for snoring and heart beat using a single piezoelectric sensor. For this study multi-rate signal processing technique was applied to detect snoring and heart beat from the single source signal. The sound event duration and intensity features were used to snore detection and heart beat was found by autocorrelation. The performance of the proposed method was evaluated on clinical database, which is the nocturnal piezoelectric snoring data of 30 patients that suffered obstructive sleep apnea. The method achieved sensitivity of 88.6%, specificity of 96.1% with accuracy of 95.6% for snoring and sensitivity of 94.1% and positive predictive value of 87.6% for heart beat, respectively. These results suggest that the proposed method can be a useful tool in sleep monitoring and sleep disordered breathing diagnosis.

• 대한임베디드공학회논문지
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• 제13권6호
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.203-204
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• 1998

ECG analysis is main techniques for diagnosing heart disease. In recent, some studies have been performed about detection of QT interval. But, it's difficult to detect QT interval because T wave is evasive. In this paper, we have detected peak point and end point of T wave and calculated QT interval. And the result has been compared with the other algorithm after detection of QT interval.

• 대한의용생체공학회:의공학회지
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• 제17권4호
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• pp.449-458
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• 1996

Most of the conventional electrocardiowaphs foil to detect signals other than P-QRS-T due to the limited SNR and bandwidth. High-resolution electrocardiography(HRECG) provides better SNR and wider bandwidth for the detection of micro-potentials with higher frequency components such as vontricular late potentials(LP). We have developed a HRECG using uncorrected XYZ lead for the detection of LPs. The overall gain of the amplifier is 4000 and the bandwidth is 0.5-300Hz without using 60Hz notch filter. Three 16-bit A/D converters sample X, Y, and Z signals simultaneously with a sampling frequency of 2000Hz. Sampled data are transmitted to a PC via a DMA-controlled, optically-coupled serial communication channel. In order to further reduce the noise, we implemented a signal averaging algorithm that averaged many instances of aligned beats. The beat alignment was carried out through the use of a template matching technique that finds a location maximizing cross-correlation with a given beat tem- plate. Beat alignment error was reduced to $\pm$0.25ms. FIR high-pass filter with cut-off frequency of 40Hz was applied to remove the low frequency components of the averaged X, Y, and Z signals. QRS onset and end point were determined from the vector magnitude of the sigrlaIL and some parameters needed to detect the existence of LP were estimated. The entire system was designed for the easy application of the future research topics including the optimal lead system, filter design, new parameter extraction, etc. In the developed HRECG, without signal averaging, the noise level was less than 5$\mu$V$_rms RTI$. With signal averaging of at least 100 beats, the noise level was reduced to 0.5$\mu$V$_rms RTI$, which is low enough to detect LPs. The developed HRECG will provide a new advanced functionality to interpretive ECG analyzers.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.334-336
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• 2007

This paper presents a beat gesture recognition method to synchronize the tempo of a robot playing a piano with the desired tempo of the user. To detect an unstructured beat gesture expressed by any part of a body, we apply an optical flow method, and obtain the trajectories of the center of gravity and normalized central moments of moving objects in images. The period of a beat gesture is estimated from the results of the fast Fourier transform. In addition, we also apply a motion control method by which robotic fingers are trained to follow a set of trajectories, Since the ability to track the trajectories influences the sound a piano generates, we adopt an iterative learning control method to reduce the tracking error.

• 한국소음진동공학회논문집
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• 제19권10호
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• pp.1028-1033
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• 2009

To detect faults and monitor thinning on a pipe, many people use ultra sonic sensors that are operated in high frequency range. Because there are many modes in high frequency range, it is difficult to find faults and monitor pipe thinning on a structure. If we deal with signals in a low frequency range which include only A0 wave and S0 wave, the information of monitoring and diagnosis can be easily obtained. In this paper, the technique for exciting low frequency range using ultra sonic sensors is proposed. The main idea of the proposed method comes from the beat phenomenon. The beat frequency is equal to the absolute value of the difference in frequency of the two waves. If the beat frequency is tuned by two ultra waves, we can excite A0 mode and S0 mode of structures. To verify the proposed method, we have performed a steel plate and pipe experiments. Experimental results show that two ultra sonic sensors can well excite low frequency range.

• 한국정보통신학회논문지
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• 제26권1호
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• pp.30-40
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• 2022

심전도 신호는 개인에 따라 형태와 특징이 다양하므로, 하나의 신경망으로는 분류하기가 어렵다. 주어진 데이터를 직접적으로 분류하는 것은 어려우나, 대응되는 정상 데이터가 있을 경우, 이를 비교하여 정상 및 비정상을 분류하는 것은 상대적으로 쉽고 정확하다. 본 논문에서는 템플릿 군을 이용하여 대표정상심박 정보를 획득하고, 이를 입력 심박에 결합함으로써 심박을 분류한다. 결합된 심박을 영상화한 후, 학습 및 분류를 진행하여, 하나의 신경망으로도 다양한 레코드의 비정상심박을 검출이 가능하였다. 특히, GoogLeNet, ResNet, DarkNet 등 다양한 신경망에 대해서도 비교학습 기법을 적용한 결과, 모두 우수한 검출성능을 가졌으며, GoogLeNet의 경우 99.72%의 민감도로, 실험에 사용된 신경망 중 가장 우수한 성능을 가졌음을 확인하였다.

• 대한임베디드공학회논문지
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• 제13권1호
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• pp.1-8
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• 2018

This paper proposes a low complexity super resolution algorithm for frequency modulated continuous wave (FMCW) radar systems for foreign object debris (FOD) detection. FOD radar has a requirement to detect foreign object in small units in a large area. However, The fast Fourier transform (FFT) method, which is most widely used in FMCW radar, has a disadvantage in that it can not distinguish between adjacent targets. Super resolution algorithms have a significantly higher resolution compared with the detection algorithm based on FFT. However, in the case of the large number of samples, the computational complexity of the super resolution algorithms is drastically high and thus super resolution algorithms are difficult to apply to real time systems. In order to overcome this disadvantage of super resolution algorithm, first, the proposed algorithm coarsely obtains the frequency of the beat signal by employing FFT. Instead of using all the samples of the beat signal, the number of samples is adjusted according to the frequency of the beat signal. By doing so, the proposed algorithm significantly reduces the computational complexity of multiple signal classifier (MUSIC) algorithm. Simulation results show that the proposed method achieves accurate location even though it has considerably lower complexity than the conventional super resolution algorithms.

• 한국정보통신학회논문지
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• 제19권7호
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• pp.1728-1736
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• 2015

부정맥 분류를 위한 기존 연구들은 개인별 ECG신호의 차이는 고려하지 않고 특정 ECG 데이터에 종속적으로 개발되었기 때문에 다른 환경에 적용할 경우 그 성능에 변화가 많아 임상 적용에 한계가 있다. 또한 기존의 방법들은 각 ECG 특징점의 정확한 측정을 필요로 하며, 연산이 매우 복잡하다. 복잡도를 줄이기 위한 여러 가지 방법들이 제안되었지만, 그에 따른 분류의 정확도가 떨어지는 문제점이 있었다. 따라서 이러한 문제점을 극복하기 위해서는 개인별 다양한 ECG 신호의 패턴에 따라 최소한의 특징점을 추출함으로써 연산의 복잡도를 줄이고 부정맥을 정확하게 분류 할 수 있는 방법이 필요하다. 본 연구에서는 대상 유형별 ECG 신호의 QRS 패턴을 이용한 부정맥 분류 방법을 제안한다. 이를 위해 전처리를 통해 잡음이 제거된 심전도 신호에서 R파를 검출하고 QRS 특징점을 통해 대상 유형별 ECG 신호의 QRS 패턴을 정의하였다. 이후 패턴분류에 따른 오류를 검출 및 수정하고, 중복된 QRS 패턴을 별도의 부정맥으로 분류하였다. 제안한 방법의 우수성을 입증하기 위해 MIT-BIH 부정맥 데이터베이스 43개의 레코드를 대상으로 PVC, PAC, Normal, LBBB, RBBB, Paced beat의 검출율을 비교하였다. 실험결과 Normal, PVC, PAC, LBBB, RBBB, Paced beat의 검출율은 각각 99.98, 97.22 95.14, 91.47, 94.85, 97.48%의 우수한 검출율을 나타내었다.