• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• Journal of the Institute of Convergence Signal Processing
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• v.9 no.2
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• pp.104-111
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• 2008

The ECG is biomedical electrical signal occurring on the surface of the body due to the contraction and relaxation of the heart. This signal represents an extremely important measure for health monitoring, as it provides vital information about a patient's cardiac condition and general health. ECG signals are contaminated with high frequency noise such as power line interference, muscle artifact and low frequency nose such as motion artifact. But it is difficult to filter nose from ECG signal, and errors resulting from filtering can distort a ECG signal. The present study implemented a small-size and low-power ECG measurement system that can remove motion artifact for convenient health monitoring during daily life. The implemented ECG monitoring system consists of ECG amplifier, a low power microprocessor, bluetooth module and monitoring program. Amplifier was designed and implemented using low power instrumentation amplifier, and microprocessor was interfaced to the ECG amplifier to collect the data, process, store and feed to a transmitter. And bluetooth module used to wirelessly transmit and receive the vital sign data from the microprocessor to an PC at the receiving site. In order to evaluate the performance of the implemented system, we assessed motion artifact rejection performance in each situation with artificially set condition using adaptive filter.

• International Journal of Control, Automation, and Systems
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• v.5 no.5
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• pp.601-605
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• 2007

In this paper, we propose a simple and relative electrode contact monitoring method. By exploiting the power line interference, which is regarded as one of the worst noise sources for bio-potential measurement, the relative difference in electrode impedance can be measured without a current or voltage source. Substantial benefits, including no extra circuit components, no degradation of the body potential driving circuit, and no electrical safety problem, can be achieved using this method. Furthermore, this method can be applied to multi-channel isolated bio-potential measurement systems and home health care devices under a steady measuring environment.

• Journal of Sensor Science and Technology
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• v.20 no.2
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• pp.137-144
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• 2011

Wireless sensor network(WSN) has the potential to greatly effect many aspects of u-healthcare. By outfitting the potential with WSN, wearable sensor node can collects real-time data on physiological status and transmits through base station to server PC. However, there is a significant gap between WSN and healthcare. WSN has the limited resource about computing capability and data transmission according to bio-sensor sampling rates and channels to apply healthcare system. If a wearable node transmits ECG and accelerometer data of 4 channel sampled at 100 Hz, these data may occur high loss packets for transmitting human activity and ECG to server PC. Therefore current wearable sensor nodes have to solve above mentioned problems to be suited for u-healthcare system. Most WSN based activity and ECG monitoring system have been implemented some algorithms which are applied for signal vector magnitude(SVM) algorithm and ECG noise algorithm in server PC. In this paper, A wearable sensor node using integrated ECG and 3-axial accelerometer based on wireless sensor network is designed and developed. It can form multi-hop network with relay nodes to extend network range in WSN. Our wearable nodes can transmit 1-channel activity data processed activity classification data vector using SVM algorithm to 3-channel accelerometer data. ECG signals are contaminated with high frequency noise such as power line interference and muscle artifact. Our wearable sensor nodes can remove high frequency noise to clear original ECG signal for healthcare monitoring.

• Journal of Biomedical Engineering Research
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• v.25 no.2
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• pp.137-144
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• 2004

ECG signals are often contaminated with high-frequency noise such as muscle artifact, power line interference, and others. In the ECG signal processing, especially during a pre-processing stage, numerous noise removal techniques have been used to reduce these high-frequency noise without much distorting the original signal. This paper proposes a new type of digital filter with a continuously variable cutoff frequency to improve the signal quality This filter consists of a cutoff frequency controller (CFC) and variable cutoff frequency lowpass filter (VCF-LPF). From the noisy input ECG signal, CFC produces a cutoff frequency control signal using the signal slew rate. We implemented VCF-LPF based on two new filter design methods called convex combination filter (CCF) and weight interpolation fille. (WIF). These two methods allow us to change the cutoff frequency of a lowpass filter In an arbitrary fine step. VCF-LPF shows an excellent noise reduction capability for the entire time segment of ECG excluding the rising and falling edge of a very sharp QRS complex. We found VCF-LPF very useful and practical for better signal visualization and probably for better ECG interpretation. We expect this new digital filter will find its applications especially in a home health management system where the measured ECG signals are easily contaminated with high-frequency noises .