• Korean Circulation Journal
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• v.54 no.3
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• pp.140-153
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• 2024

Background and Objectives: Although a single-lead electrocardiogram (ECG) patch may provide advantages for detecting arrhythmias in outpatient settings owing to user convenience, its comparative effectiveness for real-time telemonitoring in inpatient settings remains unclear. We aimed to compare a novel telemonitoring system using a single-lead ECG patch with a conventional telemonitoring system in an inpatient setting. Methods: This was a single-center, prospective cohort study. Patients admitted to the cardiology unit for arrhythmia treatment who required a wireless ECG telemonitoring system were enrolled. A single-lead ECG patch and conventional telemetry were applied simultaneously in hospitalized patients for over 24 hours for real-time telemonitoring. The basic ECG parameters, arrhythmia episodes, and signal loss or noise were compared between the 2 systems. Results: Eighty participants (mean age 62±10 years, 76.3% male) were enrolled. The three most common indications for ECG telemonitoring were atrial fibrillation (66.3%), sick sinus syndrome (12.5%), and atrioventricular block (10.0%). The intra-class correlation coefficients for detecting the number of total beats, atrial and ventricular premature complexes, maximal, average, and minimal heart rates, and pauses were all over 0.9 with p values for reliability <0.001. Compared to a conventional system, a novel system demonstrated significantly lower signal noise (median 0.3% [0.1-1.6%] vs. 2.4% [1.4-3.7%], p<0.001) and fewer episodes of signal loss (median 22 [2-53] vs. 64 [22-112] episodes, p=0.002). Conclusions: The novel telemonitoring system using a single-lead ECG patch offers performance comparable to that of a conventional system while significantly reducing signal loss and noise.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.113-124
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• 2011

Electrocardiography (ECG) is a widely accepted approach for monitoring of cardiac activity and clinical diagnosis of heart diseases. Since cardiologists have been well-trained to accept 12-lead ECG information, a huge number of ECG systems are using such number of electrodes and placement configuration to facilitate fast interpretation. Our goal is to design a wireless ECG system which renders conventional 12-lead ECG information.We propose the three-pad ECG system (W3ECG). W3ECG furthers the pad design idea of the single-pad approach. Signals obtained from these three pads, plus their placement information, make it possible to synthesize conventional 12-lead ECG signals.We provide one example of pad placement and evaluate its performance by examining ECG data of four patients available from online database. Feasibility test of our selected pad placement positions show comparable results with respect to the EASI lead system. Experimental results also exhibit high correlations between synthesized and directly observed 12-lead signals (9 out of 12 cross-correlation coefficients higher than 0.75).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2361-2376
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• 2023

The electrocardiogram (ECG) signal is commonly used to screen and diagnose cardiovascular diseases. In recent years, deep neural networks have been regarded as an effective way for automatic ECG disease diagnosis. The convolutional neural network is widely used for ECG signal extraction because it can obtain different levels of information. However, most previous studies adopt single scale convolution filters to extract ECG signal features, ignoring the complementarity between ECG signal features of different scales. In the paper, we propose a dual-scale network with convolution filters of different sizes for 12-lead ECG classification. Our model can extract and fuse ECG signal features of different scales. In addition, different spatial and time periods of the feature map obtained from the 12-lead ECG may have different contributions to ECG classification. Therefore, we add a spatial-temporal attention to each scale sub-network to emphasize the representative local spatial and temporal features. Our approach is evaluated on PTB-XL dataset and achieves 0.9307, 0.8152, and 89.11 on macro-averaged ROC-AUC score, a maximum F1 score, and mean accuracy, respectively. The experiment results have proven that our approach outperforms the baselines.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.7
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• pp.335-343
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• 2006

We have designed the system that measure one channel ECG by two electrode and extract real-time EDR with more related resipiration and comportable to subject by using conductive textile. On the assumption that relation between RL electrode and potential measurement electrode is coupled with RC connected model, we designed RL drive output to feedback two electrode for reduction of common mode signal. The conductive textile which was used for two ECG electrode was offered more comfort during night sleep in bed than any other method using attachments. In the method of single-lead EDR, R wave point or QRS interval area could be used for EDR estimation in traditional method, it is, so to speak, the amplitude modulation(AM) method for EDR. Alternatively, R-R interval could be used for frequency modulation(FM) method based on Respiratory Sinus Arrhythmia(RSA). For evaluation of performance on AM EDR and FM EDR from 14 subject, ECG lead III was measured. Each EDR was compared with both temperature around nose(direct measurement of respiration) and respiration signal from thoracic belt(indirect measurement of respiration) on mean squared error(MSE), cross correlation(Xcorr), and Coherence. The upsampling interpolation technique of multirate signal processing is applied to interpolating data instead of cubic spline interpolation. As a result, we showed the real-time EDR extraction processing to be implemented at micro-controller.

• Korean Circulation Journal
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• v.54 no.3
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• pp.154-155
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• 2024

• Journal of Chest Surgery
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• v.57 no.2
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• pp.205-212
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• 2024

Background: Postoperative atrial fibrillation (A-fib) is a serious complication of cardiac surgery that is associated with increased mortality and morbidity. Traditional 24-hour Holter monitors have limitations, which have prompted the development of innovative wearable electrocardiogram (ECG) monitoring devices. This study assessed a patch-type wearable ECG device (MobiCARE-MC100) for monitoring A-fib in patients undergoing cardiac surgery and compared it with 24-hour Holter ECG monitoring. Methods: This was a single-center, prospective, investigator-initiated cohort study that included 39 patients who underwent cardiac surgery between July 2021 and June 2022. Patients underwent simultaneous monitoring with both conventional Holter and patchtype ECG devices for 24 hours. The Holter device was then removed, and patch-type monitoring continued for an additional 48 hours, to determine whether extended monitoring provided benefits in the detection of A-fib. Results: This 72-hour ECG monitoring study included 39 patients, with an average age of 62.2 years, comprising 29 men (74.4%) and 10 women (25.6%). In the initial 24 hours, both monitoring techniques identified the same number of paroxysmal A-fib in 7 out of 39 patients. After 24 hours of monitoring, during the additional 48-hour assessment using the patch-type ECG device, an increase in A-fib burden (9%→38%) was observed in 1 patient. Most patients reported no significant discomfort while using the MobiCARE device. Conclusion: In patients who underwent cardiac surgery, the mobiCARE device demonstrated diagnostic accuracy comparable to that of the conventional Holter monitoring system.

• Journal of Sensor Science and Technology
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• v.15 no.5
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• pp.334-340
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• 2006

A compact biosignal monitoring device was developed. Electrodes for electrocardiogram (ECG) and a LED and silicon detector for photoplethysmogram (PPG) were used. A lead II type was arranged for ECG measurement and reflected light was measured at the finger tip for PPG. A single chip microprocessor (model ADuC812, Analog Device) controlled a measurement protocol and processed measured signals. PPG and ECG had a sampling rate of 300 Hz with 8-bit resolution. The maximum power consumption was 100 mW. The microprocessor computed pulse transit time (PTT) between the R-wave of ECG and the peak of PPG. To increase the resolution of PTT, analog peak detectors obtained the peaks of ECG and PPG whose interval was calculated using an internal clock cycle of 921.6 kHz. The device was designed to be operated by 3-volt battery. Biosignals can be measured for $2{\sim}3$ days continuously without the external interruptions and data is stored to an on-board memory. Our system was successfully tested with human subjects.

• Journal of Biomedical Engineering Research
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• v.35 no.3
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• pp.42-49
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• 2014

We propose an individual identification method using a single-lead electrocardiogram signal. In this paper, lead I ECG is measured from subjects in various physical and psychological states. We performed a noise reduction for lead I signal as a preprocessing stage and this signal is used to acquire the representative beat waveform for individuals by utilizing the ensemble average. From the P-QRS-T waves, features are extracted to identify individuals, 19 using the duration and amplitude information, and 16 from the QRS complex acquired by applying Pan-Tompkins algorithm to the ensemble averaged waveform. To analyze the effect of each feature and to improve efficiency while maintaining the performance, Relief-F algorithm is used to select features from the 35 features extracted. Some or all of these 35 features were used in the support vector machine (SVM) learning and tests. The classification accuracy using the entire feature set was 98.34%. Experimental results show that it is possible to identify a person by features extracted from limb lead I signal only.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.1-7
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• 2012

In general, currently developed ECG(electrocardiogram) based biometrics approaches are not suitable for real market applications since they require high cost ECG monitoring device and their measurement methods showed poor usability. In this paper, we developed lead I signal based biometrics system using special purpose ECG measurement hardware. To guarantee signal quality for biometrics from various signal measurement environment in our ordinary life, several filters are applied. In addition, to enhance usability, only two skin on electrodes without reference point are used for measurement. Lead I signals of seventeen candidates are measured from developed hardware and features are extracted. Extracted features are applied to support vector machine (SVM) pattern classifier for biometrics, and the experimental results showed 98.59% of sensitivity (SN) and 97.21% of accuracy (ACC). Compare to conventional ECG biometrics approaches, proposed system showed enhanced usability with low-cost measurement hardware.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.57-58
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• 2022

Sleep apnea (SA) is a common chronic sleep disorder that disrupts breathing during sleep. Clinically, the standard for diagnosing SA involves nocturnal polysomnography (PSG). However, this requires expert human intervention and considerable time, which limits the availability of SA diagnoses in public health sectors. Therefore, ECG-based methods for SA detection have been proposed to automate the PSG procedure and reduce its discomfort. We propose a preprocessing method to convert the one-dimensional time series of ECG into two-dimensional images using the Gramian Angular Field (GAF) algorithm, extract temporal features, and use a two-dimensional convolutional neural network for classification. The results of this study demonstrated that the proposed method can perform SA detection with specificity, sensitivity, accuracy, and area under the curve (AUC) of 88.89%, 81.50%, 86.11%, and 0.85, respectively. Our experimental results show that SA is successfully classified by extracting preprocessing transforms with temporal features.