• The KIPS Transactions:PartD
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• v.19D no.1
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• pp.105-112
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• 2012

Most methods for detecting PVC and APC require the measurement of accurate QRS complex, P wave and T wave. In this study, we propose new algorithm for detecting PVC and APC without using complex parameter and algorithms. Proposed algorithm have wide applicability to abnormal waveform by personal distinction and difference as well as all sorts of normal waveform on ECG. To achieve this, we separate ECG signal into each unit patterns and made a standard unit pattern by just using unit patterns which have normal R-R internal. After that, we detect PVC and APC by using similarity analysis for pattern matching between standard unit pattern and each unit patterns.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.325-327
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• 2004

ECG limb lead II signal widely used to diagnosis heart diseases and it is essential to detect ECG events (onsets, offsets and peaks of the QRS complex P wave and T wave) and extract them from ECG signal for heart diseases diagnoses. However, it is very difficult to develop standardized feature extraction formulas since ECG signals are varying on patients and disease types. In this paper, simple feature extraction method from normal and abnormal types of ECG signals is proposed. As a signal features, heart rate, PR interval, QRS interval, QT interval, interval between S wave and baseline, and T wave types are extracted. To show the validity of proposed method, Right Bundle Branch Block (RBBB), Left Bundle Branch Block (LBBB), Sinus Bradycardia, and Sinus Tachycardia data from MIT-BIH arrhythmia database are used for feature extraction and the extraction results showed higher extraction capability compare to conventional formula based extraction method.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.355-356
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• 2013

심장의 활동을 전기적 변위로 표현되는 심전도 신호는 심장병 진단에 중요한 임상적 파라미터들을 제공한다. 특히 심전도 신호에서 P, QRS Complex,, T 특징점들로 대표되는 파형 변곡점들의 시간상 위치와 크기 및 형태학적 모양은 심장의 이상 리듬을 나타내는 부정맥여부를 검출하는데 핵심적인 역할을 한다. 본 연구에서는 특히 QRS complex 구간에 대한 첨도치의 연산 해석을 통하여 정상적인 심전도 리듬과 심실조기수축 부정맥 리듬을 구분하는 방법을 제시하고 또한 스마트폰을 기반으로 하는 심전도 모니터링 시스템에 적용하고자 하였다.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.2001-2004
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• 2008

모든 심전도 압축에서는 압축율과 신호왜곡간의 관계를 다루며 이는 매우 중요하다. 특별히 임상적인 의미를 가진다고 평가되는 5%의 Percent Root mean square Difference(PRD)값을 만족 시키면서 높은 압축율을 얻기 위한 연구는 필수적이다. 본 논문에서는 DCT를 사용하여 심전도 압축을 수행하였을 때, 심전도의 주요한 파라미터인 파형과 RRI(R-R Interval)가 압축율에 따라 어떻게 변화하는지를 평가하고 심전도의 두 가지 주요 파라미터를 진단정보의 왜곡 없이 압축할 수 있는 DCT계수 및 압축율을 도출해 내었다. 실험에는 MIT-BIH ECG Compression Test Database를 사용하였으며 DCT압축을 수행하였을 때 5 % 이하의 PRD를 확보하기 위해서는 81개 샘플에 대하여 평균 4.496 : 1, 최하 3.422 : 1 의 CR을 가지는 것을 확인할 수 있었으며, QRS를 올바르게 검출하는 범위에서의 78개의 샘플에 대하여 평균 CR은 17.3 : 1 최저 CR은 4.6512 : 1 로 나타났다. QRS 검출 한계에서의 RRI 시간왜곡은 평균 3.7149 $\pm$ 4.3147 ms로 나타났으며, 최대 시간왜곡은 13.0256 $\pm$ 14.2035 ms 로 조사되었다.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1395-1396
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• 2015

본 연구의 목적은 광용적맥파 해석에 중요하게 사용되는 최대 상승기울기(maximum dV/dt) 지점 검출 알고리즘 개발로, 미분 및 필터링을 통한 전처리 과정, 극점 검출과정, 역탐색 등의 후처리 과정으로 구성되는 알고리즘을 구현하였다. 제안된 알고리즘의 성능을 평가하기 위하여 총 74,225개의 맥박파형을 사용한 검증을 수행하였으며, 동시에 측정된 심전도 QRS지점을 기준으로 최대 dV/dt 측정 위치 정확성을 판정하였다. 시뮬레이션 결과, 적응형 임계치 극점 검출 방법과 함께 사용하였을 때, 제안된 알고리즘은 기존 광용적맥파 상단, 하단극점 검출 알고리즘과 유사한 성능인 98.57%, 99.98%의 민감도와 특이도, 0.02%의 오검출율을 가지는 것으로 나타났다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.834-837
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• 2015

In this paper, we designed the FPGA hardware-based real-time ECG simulator, which generates an analog ECG signal within the range of 0 to 5 volts and described function. The ECG signal generated by the simulator can be applied to laboratory tests, the medical device, and the calibration study in various ways. ECG signals generated by simulator are obtained with conventional 24bit quantization to generate the signal data, and they are sampled and quantized to 1kHz of the 8-bit resolution when used as actual data. The proposed simulator is implemented using xilix Spartan-3 and data are transmitted through an RS-232 between the PC and the FPGA simulator. The transmitted data are stored in the memory and the stored data are printed out with the analog ECG signal through DAC (0808). It can also control the heart rate (HR) via the two buttons level UP-DOWN. We used existing ECG input rating for the evaluation of the designed system and evaluated differential circuit for obtaining QRS waveform and the output signal. We finally could obtained proper the result.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.2
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• pp.117-126
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• 2019

Legacy study for detecting arrhythmia have mostly used nonlinear method to increase classification accuracy. Most methods are complex to process and manipulate data and have difficulties in classifying various arrhythmias. Therefore it is necessary to classify various arrhythmia based on short-term data. In this study, we propose a feature extraction based on auto regressive modeling and an premature contraction arrhythmia classification method using SVM., For this purpose, the R-wave is detected in the ECG signal from which noise has been removed, QRS and RR interval segment is modelled. Also, we classified Normal, PVC, PAC through SVM in realtime by extracting four optimal segment length and AR order. The detection and classification rate of R wave and PVC is evaluated through MIT-BIH arrhythmia database. The performance results indicate the average of 99.77% in R wave detection and 99.23%, 97.28%, 96.62% in Normal, PVC, PAC classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.37-43
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• 2020

Recently, many researches have been actively to diagnose symptoms of heart disease using ECG signal, which is an electrical signal measuring heart status. In particular, the electrocardiogram signal can be used to monitor and diagnose arrhythmias that indicates an abnormal heart status. In this paper, we proposed 1-D convolutional neural network for arrhythmias classification systems. The proposed model consists of deep 11 layers which can learn to extract features and classify 5 types of arrhythmias. The simulation results over MIT-BIH arrhythmia database show that the learned neural network has more than 99% classification accuracy. It is analyzed that the more the number of convolutional kernels the network has, the more detailed characteristics of ECG signal resulted in better performance. Moreover, we implemented a practical application based on the proposed one to classify arrythmias in real-time.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.3
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• pp.87-93
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• 2015

The accidents caused by drivers while driving are considered as the major causes along with other causes such as conditions of roads, weather and cars. In this study, we investigated the driver's workloads under three different driving conditions (Weather, Driving time zone, and Traffic density) through analyzing biological signals obtained from a car driving simulator system. The proposed method is able to detect R waves and R-R interval calculation in the ECG. Heart rate variability (HRV) was investigated for the time domain to determine the changes in driver's conditions.

• Journal of Biomedical Engineering Research
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• v.16 no.4
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• pp.403-414
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• 1995

It is very difficult to quantize the ECG analysis because the decision criterion for ECG is different with each other depending on the medical specialists of the heart and there are measured detecting errors for each ECG measurement system. Therefore, we developed the real-time ECG identification system using digital fuzzy processor for STD-BUS, in order to reduce ambiguity generated in the process of ECG identification and to analyze the irregular ECG stastically to ECG's repetition interval. The variables such as AGE (months), width of QRS, average RRI, and RRI were used to classify the ECG, and were applied to ECG signal indentification system which is developed for the purpose of research. It was found that the automatic diagnosis of ECG signal was possible in the real time process which was impossible in general process of algorithm.