• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.137-142
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• 2019

This paper Various services exist to detect and monitor abnormal event. However, most services focus on fires and gas leaks. so It is impossible to prevent and respond to emergency situations for the elderly and severely disabled people living alone. In this study, AI model is designed and compared to detect abnormal event of heart rate signal which is considered to be the most important among various bio signals. Specifically, electrocardiogram (ECG) data is collected using Physionet's MIT-BIH Arrhythmia Database, an open medical data. The collected data is transformed in different ways. We then compare the trained AI model with the modified and ECG data.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.48-50
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• 2006

In this paper, we propose a method to minimize the baseline wandering that make hard to detect R wave in ECG. This method uses a different signal between ECG and ascending slope tracing waves to minimize the baseline wandering. When the slope of ECG signal maintains the value or falls, the ascending slope tracing wave fellows ECG signal directly, and this wave holds that value of ECG signal when the slope begins to rises in a certain time(=hold time). After this hold time, this wave traces ECG signal again. To evaluate this minimizing method for baseline wandering, the experiments are carried out with 5 ECG data in the database of MIT/BIH. R waves in the proposed different signal are detected by using descending slope trace waves and compared with the annotation file. The results show that the proposed method Is sure to minimize the baseline wandering in ECG.

• Journal of Biomedical Engineering Research
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• v.10 no.3
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• pp.343-350
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• 1989

A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.29-38
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• 2024

After Transcatheter Aortic Valve Replacement (TAVR), comprehensive management of complications, including the need for Permanent Pacemaker Implantation (PPI), is crucial, increasing the demand for accurate prediction models. Departing from traditional image-based methods, this study developed an optimal PPI prediction model based on ECG data using the XGBoost algorithm. Focusing on ECG signals like DeltaPR and DeltaQRS as key indicators, the model effectively identifies the correlation between conduction disorders and PPI needs, achieving superior performance with an AUC of 0.91. Validated using data from two hospitals, it demonstrated a high similarity rate of 95.28% in predicting PPI from ECG characteristics. This confirms the model's effective applicability across diverse hospital data, establishing a significant advancement in the development of reliable and practical PPI prediction models with reduced dependence on human intervention and costly medical imaging.

• Journal of Biomedical Engineering Research
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• v.13 no.1
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• pp.69-78
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• 1992

This Paper describes implementation of a remote ECG diagnostic system using telephone line. The overall system includes ECG data acquisition system, ECG terminal, system control software, automatic diagnosis system, and transmission system.'The proposed system provides various functions, which are ECG data acquisition, transmission, receiving, diagnosis and dialogue between patients and medical doctors. Thls system is very simple and convienient to use. We evaluate the performance of modem and the accuracy of automatic diagnosis algorithm. The obtained results suggest the Possibilities of a remote ECG diagnostic system using the only existed telephone line.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.441-448
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• 2007

Heart rate variability (HRV) in electrocardiogram (ECG) is an important index for understanding the health status of heart and the autonomic nervous system. Most HRV analysis approaches are based on the proper heart rate (HR) data. Estimation of heart rate is thus a key process in the HRV study. In this paper, we report an innovative method to estimate the heart rate. This method is mainly based on the concept of periodicity transform (PT) and instantaneous period (IP) estimate. The method presented is accordingly called the "PT-IP method." It does not require ECG R-wave detection and thus possesses robust noise-immune capability. While the noise contamination, ECG time-varying morphology, and subjects' physiological variations make the R-wave detection a difficult task, this method can help us effectively estimate HR for medical research and clinical diagnosis. The results of estimating HR from empirical ECG data verify the efficacy and reliability of the proposed method.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2879-2882
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• 2003

Obstructive Sleep Apnea(OSA) is a representative symptom of sleep disorder which is caused by airway obstruction. OSA is usually diagnosed through the laboratory based Polysomnography(PSG) which is uncomfortable and expensive. In this paper, the detection method for OSA events, using ECG, has been developed. The proposed method uses the ECG data sets provided from Physionet. The features for OSA events detection are the average and standard deviation of 1 minute R-R interval, power spectrum of R-R interval and S-pulse amplitude from data sets. These features are applied to the input of Neural Network. To evaluate the method, we used the another ECG data sets. And we achieved sensitivity of 89.66％, specificity of 95.25％. So, we can know that the features proposed in this paper are important to detect OSA.

• Journal of Biomedical Engineering Research
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• v.43 no.6
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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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• v.4 no.1
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• pp.27-33
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• 2000

Nowadays is the increasing percentage of the aged in the population. Advanced age shows concern at own health status. So it is needed the home medical instrument which is cheap and connectable to hospital with a LAN or a public network In this paper, We have implemented a ECG (Electrocadiogram) terminal. The ECG terminal is composed of two parts. One is the analog board to remove the baseline drift. The other is the digital board consists of a data aquisition part and data transmission part. The ECG terminal doesn't have the display region to show a ECG curve and uses the modified digital filter to remove the power noise. The ECG terminal transmits a ECG signal with a LAM using TCP/IP. So ECG signal can be seen by the Central Patient Monitor Program connected TCP/IP network.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.269-275
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• 2022

Public healthcare has recently become an issue of great importance due to the exponential growth in the human population, the increase in medical expenses, and the COVID-19 pandemic. Speed is one of the crucial factors in saving life, particularly in case of heart attack. Therefore, a healthcare device is needed to continuously monitor and follow up heart health conditions remotely without the need for the patient to attend a medical center. Therefore, this paper proposes a portable electrocardiogram (ECG) monitoring system to improve healthcare for heart attack patients in both home and ambulance settings. The proposed system receives the ECG signals of the patient and sends the ECG values to a MySQL database on the IoT-cloud via Wi-Fi. The signals are displayed as an ECG data chart on a webpage that can be accessed by the patient's doctor based on the HTTP protocol that is employed in the IoT-cloud. The proposed system detects the ECG data of the patient to calculate the total number of heartbeats, number of normal heartbeats, and the number of abnormal heartbeats, which can help the doctor to evaluate the health status of the patient and decide on a suitable medical intervention. This system therefore has the potential to save time and life, but also cost. This paper highlights the five main advantages of the proposed ECG monitoring system and makes some recommendations to develop the system further.