• Journal of Biomedical Engineering Research
• /
• v.29 no.6
• /
• pp.443-450
• /
• 2008

The purpose of this study is to measure both ECG and BCG(Ballistocariograph) signal of a subject on moving or resting wheelchair and detect the heart rate and respiratory rate and transmit an event message to remote server on emergent situation. To acquire ECG and BCG data, amplifier circuits were composed to be suitable for their characteristics. The output signals were converted to digital data and stored in bio-signal archiving media(SD card). CDMA module was used to transmit the event data on ECG electrode detachment and the received data was monitored by the developed C# application program. 5 volunteers participated in the experiment to evaluate the validity of the developed device. When the event occurs in each subject, 48 Kbyte data, stored for 32 seconds from that point, was transmitted to remote server through CDMA cellular phone network correctly. The received data of ECG, BCG, and 3-axial acceleration could be archived in server and the heart rate and respiratory rate could be measured and analyzed. The developed device in this study could acquire the ECG and BCG data of subjects on wheelchair simultaneously and measure their heart rate and respiratory rate. In addition, event data was verified to be transmitted to remote server without any errors.

• Smart Media Journal
• /
• v.8 no.4
• /
• pp.33-37
• /
• 2019

Because the ECG signals are time-series data acquired as time elapses, it is important to obtain comparative data the same in size as the enrolled data every time. This paper suggests a network model of GAN (Generative Adversarial Networks) based on an auxiliary classifier to generate synthetic ECG signals which may address the different data size issues. The Cosine similarity and Cross-correlation are used to examine the similarity of synthetic ECG signals. The analysis shows that the Average Cosine similarity was 0.991 and the Average Euclidean distance similarity based on cross-correlation was 0.25: such results indicate that data size difference issue can be resolved while the generated synthetic ECG signals, similar to real ECG signals, can create synthetic data even when the registered data are not the same as the comparative data in size.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.513-516
• /
• 1999

I here are many kinds of method to compress data. To very simple methods from very complex methods, a kind is various. In this study, the simplest form of the Tolerance-Comparison method, zero-order method is used. Using this method, despite using low speed CPUs, it is possible to compress real time data. So this method is suitable for ECG holler system. In this study, to complement zero-order method, it is needed to develop prediction technique and to research ways to apply the technique.

• Journal of Biomedical Engineering Research
• /
• v.31 no.5
• /
• pp.365-374
• /
• 2010

This paper is about the personal identification algorithm using an ECG that has been studied by a few researchers recently. Previously published algorithm can be classified as two methods. One is the method that analyzes ECG features and the other is the morphological analysis of ECG. The main characteristic of proposed algorithm uses together two methods. The algorithm consists of training and testing procedures. In training procedure, the features of all recognition objects' ECG were extracted and the PCA was performed for morphological analysis of ECG. In testing procedure, 6 candidate ECG's were chosen by morphological analysis and then the analysis of features among candidate ECG's was performed for final recognition. We choose 18 ECG files from MIT-BIH Normal Sinus Rhythm Database for estimating algorithm performance. The algorithm extracts 100 heartbeats from each ECG file, and use 40 heartbeats for training and 60 heartbeats for testing. The proposed algorithm shows clearly superior performance in all ECG data, amounting to 90.96% heartbeat recognition rate and 100% ECG recognition rate.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.33B no.12
• /
• pp.45-61
• /
• 1996

This paper presents the ECG data compression using wavelet transform (WT) and adaptive fractal interpolation (AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data cmpression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 wihtout any entropy coding.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.11
• /
• pp.221-224
• /
• 1996

This paper presents the ECG data compression using wavelet transform(WT) and adaptive fractal interpolation(AFI). The WT has the subband coding scheme. The fractal compression method represents any range of ECG signal by fractal interpolation parameters. Specially, the AFI used the adaptive range sizes and got good performance for ECG data compression. In this algorithm, the AFI is applied into the low frequency part of WT. The MIT/BIH arrhythmia data was used for evaluation. The compression rate using WT and AFI algorithm is better than the compression rate using AFI. The WT and AFI algorithm yields compression ratio as high as 21.0 without any entroy coding.

• Journal of Biomedical Engineering Research
• /
• v.35 no.4
• /
• pp.87-94
• /
• 2014

Recent technological advances have increased interest in personal health monitoring. Electrocardiogram(ECG) monitoring is a basic healthcare activity and can provide decisive information regarding cardiovascular system status. In this study, we developed a capacitive ECG measurement system that can be included within a cloth mattress pad. The device permits ECG data to be obtained during sleep by using capacitive electrodes. However, it is difficult to detect R-wave peaks automatically because signals obtained from the system can include a high level of noise from various sources. Because R-peak detection is important in ECG applications, we developed an algorithm that can reduce noise and improve detection accuracy under noisy conditions. Algorithm reliability was evaluated by determining its sensitivity(Se), positive predictivity(+P), and error rate(Er) by using data from the MIT-BIH Polysomnographic Database and from our capacitive ECG system. The results showed that Se = 99.75%, +P = 99.77%, and Er = 0.47% for MIT-BIH Polysomnographic Database while Se = 96.47%, +P = 99.32%, and Er = 4.34% for our capacitive ECG system. Based on those results, we conclude that our R-peak detection method is capable of providing useful ECG information, even under noisy signal conditions.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.4
• /
• pp.205-212
• /
• 2023

Among cardiac diseases, arrhythmias can lead to serious complications such as stroke, heart attack, and heart failure if left untreated, so continuous and accurate ECG monitoring is crucial for clinical care. However, the accurate interpretation of electrocardiogram (ECG) data is entirely dependent on medical doctors, which requires additional time and cost. Therefore, this paper proposes an arrhythmia recognition module for the purpose of developing a medical platform through the analysis of abnormal pulse waveforms based on Lifelogs. The proposed method is to convert ECG data into image format instead of time series data, apply visual pattern recognition technology, and then detect arrhythmia using CNN model. In order to validate the arrhythmia classification of the CNN model by image type conversion of ECG data proposed in this paper, the MIT-BIH arrhythmia dataset was used, and the result showed an accuracy of 97%.

• Journal of Sensor Science and Technology
• /
• v.21 no.4
• /
• pp.241-248
• /
• 2012

Recently healthcare industry such as pharmaceutical, medical device and healthcare service technology is growing significantly. Mobile healthcare has attracted big attention due to IT convergence technology. Paradigm of healthcare has been changed from the 1st generation(communicable disease prevention) and the 2nd generation(treatment of disease due to extended life expectancy) to the 3rd generation(extended life expectancy due to prevention and control). In our study, we suggest the 3rd generation mobile healthcare system using Bluetooth based wearable ECG monitoring system and smart phone technology. The mobile healthcare system consists of wearable shirts with Bluetooth communication module, ECG sensor, battery, and mobile phone. The ECG data is obtained by a miniaturized sensor and the data is transferred to a mobile phone using Bluetooth communication. Then, user can monitor his/her own ECG signal on an application using Android in mobile phone. The Bluetooth communication device is used due to highly reliable data transmission property and the Bluetooth chip is embedded in every mobile phone. The wearable shirts with chest belt of Bluetooth ECG module is designed with a focus on convenience in the daily life of a wearer. The ECG signal evaluation software in Android based mobile phone is developed for the health check and the ECG signal variation is tested according to the activities of the wearer such as walking, climbing stairs, stand up and sit down, and so on.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.34 no.1
• /
• pp.112-125
• /
• 2010

A Holter monitor is used for ECG monitoring of ambulatory daily life in hospital. However, the use of this apparatus causes skin allergies and discomfort in patients because of the attachment gel and tapes used to attach disposable electrodes to the skin. In this study, the development of tight-fitting clothing connected to a portable Holter monitor was proposed. In addition, the use of conductive fabrics as electrodes was proposed; this will enable the use of garments in u-health care for measuring ECG signals. The male subjects were university students in the ages of 20 to 24. Subjective wear sensations of the experimental garments were rated using seven Likert scales. A Likert type scale was used for the evaluation and a 7 point score indicates that it provided the best fit as a tight-fitting upper clothing. Clothing pressure was measured using an air-pack-type pressure sensor (model AMI 3037-2) at 4 locations (the conductive fabric electrode) As results, a male basic sloper for upper clothing was developed and that pattern was manipulated to the tight fit pattern by considering the reduction rate of the percentage stretch in the fabric. The developed tight-fitting garment was superior in terms of subjective sensation and 6t. The mean pressure of the garment with reduction rates of 40% in width and of 50% in length was 8.45gf/$cm^2$. A conductive fabric electrode was developed by considering the sewing method and the developed electrode was detected well. The ECG data were recorded for 13 hr 19 min 44 sec and the artifacts in the ECG signals were recorded for 9 hr 3 min 46 sec (total time: 22 hr 23 min 23 sec). The artifacts data were obtained during heavy activities.