• Journal of Biomedical Engineering Research
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• 제43권6호
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• pp.424-433
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• 2022

As the proportion of non-contact telemedicine increases and the number of electrocardiogram (ECG) data measured using portable ECG monitors increases, the demand for automatic algorithms that can precisely analyze vast amounts of ECG is increasing. Since the P, QRS, and T waves of the ECG have different shapes depending on the location of electrodes or individual characteristics and often have similar frequency components or amplitudes, it is difficult to distinguish P, QRS and T waves and measure each parameter. In order to measure the widths, intervals and areas of P, QRS, and T waves, a new algorithm that recognizes the start and end points of each wave and automatically measures the time differences and amplitudes between each point is required. In this study, the start and end points of the P, QRS, and T waves were measured using six Deep Neural Networks (DNN) that recognize the start and end points of each wave. Then, by synthesizing the results of all DNNs, 12 parameters for ECG characteristics for each heartbeat were obtained. In the ECG waveform of 10 subjects provided by Physionet, 12 parameters were measured for each of 660 heartbeats, and the 12 parameters measured for each heartbeat well represented the characteristics of the ECG, so it was possible to distinguish them from other subjects' parameters. When the ECG data of 10 subjects were combined into one file and analyzed with the suggested algorithm, 10 types of ECG waveform were observed, and two types of ECG waveform were simultaneously observed in 5 subjects, however, it was not observed that one person had more than two types.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제2권1호
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• pp.11-17
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• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, p, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detecting techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source. In this paper, we performed the extracting parameters from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Journal of the Korean Institute of Telematics and Electronics S
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• 제35S권8호
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• pp.39-46
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• 1998

One of the main techniques for diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, P, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detection techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source. In this paper, we performed the extracting parameters from and recovering the ECG signal using wavelets transform that has recently been applying to various fields.

• Proceedings of the KOSOMBE Conference
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.435-438
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• 1997

One of the main techniques or diagnosing heart disease is by examining the electrocardiogram(ECG). Many studies on detecting the QRS complex, P, and T waves have been performed because meaningful information is contained in these parameters. However, the earlier detecting techniques can not effectively extract those parameters from the ECG that is severely contaminated by noise source such 60Hz powerline interference, motion artifact and baseline drift. in this paper, we performed the extracting parameters from and recovering the ECG signal using wavelet transform that has recently been applying to various fields.

• Journal of Multimedia Information System
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• 제3권2호
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• pp.1-8
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• 2016

In this paper, we propose a method to automatically diagnose various diseases. The input data consists of electrocardiograph (ECG) recordings. We extract R-to-R interval (RRI) signals from ECG recordings, which are preprocessed to remove trends and ectopic beats, and to keep the signal stationary. After that, we perform some prospective analysis to extract time-domain parameters, frequency-domain parameters, and nonlinear parameters of the signal. Those parameters are unique for each disease and can be used as the statistical symptoms for each disease. Then, we perform feature selection to improve the performance of the diagnosis classifier. We utilize the selected features to diagnose various diseases using machine learning. We subsequently measure the performance of the machine learning classifier to make sure that it will not misdiagnose the diseases. The first two steps, which are R-to-R extraction and preprocessing, have been successfully implemented with satisfactory results.

• Journal of Digital Contents Society
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• 제17권2호
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• pp.97-103
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• 2016

This work introduces techniques experienced for the electrocardiogram(ECG) visual analysis, able to characterize the major parameters and events with clinical relevance for heart failure management and cardiovascular risk assessment. In particular, it includes approaches for ECG data visual processing such as the variable charts, graphs base on the complex MIT-BIH ECG database. Through the experienced this works of ECG database visualization, so many researcher more easily access the complex ECG database and can intuitionally understand the meanings via a variable ECG visualized data.

• The Transactions of the Korean Institute of Electrical Engineers D
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• 제55권2호
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• pp.75-81
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• 2006

The purpose of this paper is to recognize the feature points of ECG and human pulse -which signal shows the electric and physical characteristics of heart respectively- using wavelet transform. Wavelet transform is proper method to analyze a signal in time-frequency domain. In the process of wavelet decomposition and reconstruction of ECG and human pulse signal, we removed the noises of signal and recognized the feature points of signal using some of decomposed component of signal. We obtained the result of recognition rate that is estimated about 95.45$\%$ in case of QRS complex, 98.08$\%$ in case of S point and P point and 92.81$\%$ in case of C point. And we computed diagnosis parameters such as RRI, U-time and E-time.

• Journal of Biomedical Engineering Research
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• 제9권1호
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• pp.79-86
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• 1988

This paper describes the design of a hardware systenl for ECG feature extraction based on pipeline processor consistinsf of three microcomputers. ECG data is acquisited by 12 bit A/D converter with hardware QRS triggered detector. Four diagnostic parameters parameters-heart rate, morPhology, axis, and 57 segment-are used for the classification and the diagnosis of arrhythmia. The functions of the main CPU were distributed and processed with three microcomputers. Therefore the effective data process and the real time process using microcomputer can be obtained. The interconnection structure consisting of two common memory units is designed to decrease the delay time caused by data transfer between processors and designed by which the delay time can be taken Loye of one clock period.

• Journal of the Korean Institute of Telematics and Electronics
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• 제26권9호
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• pp.1444-1452
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• 1989

This paper describes the implementation of a real time fetal ECG monitoring system in which an adaptive multi-channel noise canceller is realized using the Texas Instruments TMS32020 progrmmmable ditital signal processor. An ECG signal from the electrode placed on the mother's abdomen and three ECGs from those on the chest are applied as the desired signal and the referened inputs, respectively, of the multi-channel filter. The coefficients of the filter are updated using the LMS algorithm such that the output of the multi-channel filter copies the maternal ECG embedded in the abdominal ECG. The enhanced fetal ECG is obtained by subtracting the filter output from the abdominal ECG, and the difference signal is recorded. Both off-line and on-line experimental results are presented to verify the effectiveness of the parameters for the digital signal processing algorithms and the prototype system.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 한국감성과학회 2002년도 춘계학술대회 논문집
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• pp.177-180
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• 2002

The type of physiological stress involved in driving is probably complex, and a comprehensive study involving recording of physiological signals such as electrocardiogram(ECG), electromyogram(EMG). Changes in relevant Physiological parameters, such as ECG, EMG, reflected changes in driver status. In order to derive the mental and physical load of driving a motor vehicle from driving behaviour alone it is necessary to establish the relationship between changes in a driver's physiological parameters and behavioral parameters. In this study, we choose two different condition and investigated driver's status using HRV analysis method. Many previous studies have shown that increasing driving time causes a variation of HRV signal.