• Korean Journal of Clinical Pharmacy
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• v.33 no.2
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• pp.86-96
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• 2023

Background: Using KIDS-KAERS database (KIDS-KD) from 2016 to 2020, the aim is to investigate signals of adverse events of alpha-adrenoceptor antagonists and to present adverse events that are not included in the precautions for use when marketing approval. Methods: This study was conducted by disproportionality analysis. Data mining analysis was performed to detect signals of alpha-adrenoceptor antagonists, such as terazosin, doxazosin, alfuzosin, silodosin, and tamsulosin. The signal was defined by three criteria as proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC). Detected signals were compared with product labeling and the European Medicines Agency-Important Medical Events list. Results: Out of the total number of 408,077 reports for adverse events, 6,750 cases were reported as adverse events of alpha-adrenoceptor antagonists. Dizziness, mouth dryness, hypotension postural, and oedema peripheral are identified as common adverse events of five alpha-adrenoceptor antagonists and are typically listed on drug labels. However, new signals were detected for pneumonia, chronic obstructive airway disease, eye diseases such as glaucoma and cataracts, fracture, and ileus of tamsulosin that were not previously listed on the drug labels in Korea. Conclusions: This study identified signals related to adverse drug reactions of alpha-adrenoceptor antagonists and presented serious adverse events, suggesting new adverse reactions to be aware of when using alpha-adrenoceptor antagonists.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.42-47
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• 2001

Analyzing the ECG signal, we can find heart disease, for example, arrhythmia and myocardial infarction, etc. Particularly, detecting arrhythmia is more important, because serious arrhythmia can take away the life from patients within ten minutes. In this paper, we would like to introduce the signal processing for ECG analysis and the device made for wireless communication of ECG data. In the signal processing, the wavelet transform decomposes the ECG signal into high and low frequency components using wavelet function. Recomposing the high frequency bands including QRS complex, we can detect QRS complex and eliminate the noise from the original ECG signal. To recognize the ECG signal pattern, we adopted the polynomial approximation partially and statistical method. The ECG signal is divided into small parts based on QRS complex, and then, each part is approximated to the polynomials. Comparing the approximated ECG pattern with the database, we can detect and classify the heart disease. The ECG detection device consists of amplifier, filters, A/D converter and RF module. After amplification and filtering, the ECG signal is fed through the A/D converter to be digitalized. The digital ECG data is transmitted to the personal computer through the RF transceiver module and serial port.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.267-276
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• 1991

This paper describes syntactic pattern recognition algorithm for pattern recognition and diagnostic parameter extraction of ECG signal. ECG signal which is represented linguistic string is evaluated by pattern grammar and its interpreter-LALR(1) parser for pattern recognition. The proposed pattern grammar performs syntactic analysis and semantic evaluation simultaneously. The performance of proposed algorithm has been evaluated using CSE database.

• The Journal of the Acoustical Society of Korea
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• v.17 no.4E
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• pp.41-47
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• 1998

Microphone is the first link in the speech recognition system. Depending on its type and mounting position, the microphone can significantly distort the spectrum and affect the performance of the speech recognition system. In this paper, characteristics of the speech signal for different microphones and microphone distances are investigated both in time and frequency domains. In the time domain analysis, the average signal-to-noise ration is measure ration is measured for the database we collected depending on the microphones and microphone distances. Mel-frequency spectral coefficients and mel-frequency cepstrum are computed to examine the spectral characteristics. Analysis results are discussed with our findings, and the result of recognition experiments is given.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.13-16
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• 2001

논문에서는 인터넷을 이용한 응용분야 중에서 원격으로 네트워크의 트래픽을 모니터링함에 있어서 시간이 흐름에 따라서 메모리의 증가를 요구하지 않는 Robin Database 방식을 이용한 방법에 대한 응용 연구 방법을 기술하였다. 먼저 기본적인 RRDtool을 이용하여 트래픽 모니터링 데이터베이스 구조 설계를 구현한 과정을 기술하였고, 데이터의 모니터링을 위한 모니터링 프로그램으로서 Perl 스크립트 언어를 이용한 작업 과정을 보려다. 마지막으로 본 연구결과를 통하여 구축된 트래픽 모니터링 시스템을 이용하여 실제 트래픽을 모니터링한 결과를 실험 결과에 제시하였다.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.47-53
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• 2002

In this paper, we proposed a content-based video search method using the eigen value of key frame and intensity component. We divided the video stream into shot units to extract key frame representing each shot, and get the intensity distribution of the shot from the database generated by using ECA(Eigen Component Analysis). The generated codebook, their index value for each key frame, and the intensity values were used for database. The query image is utilized to find video stream that has the most similar frame by using the euclidean distance measure among the codewords in the codebook. The experimental results showed that the proposed algorithm is superior to any other methols in the search outcome since it makes use of eigen value and intensity elements, and reduces the processing time etc.

• Journal of Biomedical Engineering Research
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• v.22 no.3
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• pp.285-291
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• 2001

In this paper, we designed two preprocessing adaptive filter - high pass filter and notch filter - using FPGA. For minimizing the calculation load of multi-channel and high-resolution ECG system, we utilize FPGA rather than digital signal processing chip. To implement the designed filters in FPGA, we utilize FPGA design tool(Altera corporation, MAX-PLUS II) and CSE database as test data. In order to evaluate the performance in terms of processing time, we compared the designed filters with the digital filters implemented by ADSP21061(Analog Devices). As a result, the filters implemented by FPGA showed better performance than the filters based on ADSP21061. As a consequence of examination, we conclude that FPGA is a useful solution in multi-channel and high-resolution signal processing.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.433-441
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• 1992

This paper describes an algorithm that can efficiently analyze the ECG signal using frame knowledge representation technique. Input to the analysis process is a set of significant points which have been extracted from an original sampled signal(lead II) by the syntactic peak recognition algorithm. The hierarchical property of ECG signal is represented by hierarchical AND/OR graph. The semantic information and constraints of the ECG signal are desctibed by frame. As the control mechanism for labeling points, the search mechanism with the mixed paradigms of data-driven and model driven hypothesis formation, scoring function, hypothesis modification network and instance inheritance are used. We used the CSE database in order to evaluate the performance of the proposed algorithm.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.10
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• pp.80-88
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• 1997

This paper describes a design of a Wavelet Adaptive Filter(WAF) for the removal of the baseline wandering and the minimization of the signal distortion using by wavelet transform and adaptive filter in the ECG signal. WAF consists of two parts. The first part is wavelet transform that decomposes the ECG signal into seven frequency bands using Vaidyanathan and Hoang wavelet. The second part is adaptive filter that uses the signal of seventh low frequency band among the wavelet transformed signals as primary input and a unit impulse sequence as reference input. For the evaluation of the performance of WAF, we used several baseline wandering elimination filters such as commerical standard filter with cutoff frequency of 0.5Hz and general adaptive filter. We made use of MIT/BIH database and real patient data for the evaluation. In conclusion, WAF showed a lower ST segement distortion than standard filter and adaptive filter and has a higher eliminated noise power than standard filter and adaptive filter.

• Speech Sciences
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• v.5 no.1
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• pp.151-165
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• 1999

This paper describes a series of researches to diagnose vocal diseases using the statistical method and the acoustic signal analysis method. Speech materials are collected at the hospital. Using the pathological database, the basic parameters for the diagnosis are obtained. Based on the statistical characteristics of the parameters, valid parameters are chosen and those are used to diagnose the pathological speech signal. Cepstrum is used to extract parameters which represents characteristics of pathological speech. 3 layered neural network is used to train and classify pathological speech into normal, benign and malignant case.