Implementation of Extended Kalman Filter for Real-Time Noncontact ECG Signal Acquisition in Android-Based Mobile Monitoring System

Rachim, Vega Pradana; seungsik, kang; Chung, Wan-Young; Kwon, Tae Ha

doi:10.5369/jsst.2014.23.1.7

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…Numerous studies have been conducted in order to study ways to reduce motion noise [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. The simplest way is the application of a band-pass filter (BPF).…”

Section: Introductionmentioning

confidence: 99%

“…The extended Kalman filter (EKF) uses a nonlinear model of ECG signal and tries to apply the model to the Kalman filter that is modified to deal with nonlinear relations. EKF was studied to reduce the noise of conventional contact ECG in other papers, and it was also used for non-intrusive ECG [ 21 , 22 ]. Although it was useful for many cases, it can not be effective to reduce motion noise in non-intrusive ECG because of the model-based approach of EKF.…”

Section: Introductionmentioning

confidence: 99%

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Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Choi

Jeong

Kim

et al. 2016

Sensors

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Non-intrusive electrocardiogram (ECG) monitoring has many advantages: easy to measure and apply in daily life. However, motion noise in the measured signal is the major problem of non-intrusive measurement. This paper proposes a method to reduce the noise and to detect the R peaks of ECG in a stable manner in a sitting arrangement using non-intrusive sensors. The method utilizes two capacitive ECG sensors (cECGs) to measure ECG, and another two cECGs located adjacent to the sensors for ECG are added to obtain the information on motion. Then, active noise cancellation technique and the motion information are used to reduce motion noise. To verify the proposed method, ECG was measured indoors and during driving, and the accuracy of the detected R peaks was compared. After applying the method, the sum of sensitivity and positive predictivity increased 8.39% on average and 26.26% maximally in the data. Based on the results, it was confirmed that the motion noise was reduced and that more reliable R peak positions could be obtained by the proposed method. The robustness of the new ECG measurement method will elicit benefits to various health care systems that require noninvasive heart rate or heart rate variability measurements.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Choi

Jeong

Kim

et al. 2016

Sensors

View full text Add to dashboard Cite

show abstract

“…However, it requires a high input impedance to electrodes. To overcome this high input impedance amplifier can be used to lower the skin-to-electrode resistance [7]. But, this system requires direct contact with the body, and it is also not feasible for long term monitoring as it leaves marks and creates discomfort.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Design of a Wireless Non-Contact Wearable System for Infants Using Adaptive Filter

Mahmud

Wang

Fang

2017

Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications

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Cardiovascular signal is a fundamental physiological sign to assess health condition. Continuous and long term health monitoring of infants would be helpful to prevent and predict illness. However, there is no noninvasive method to monitor their health. The developed system consists of a noncontact electrocardiogram (ECG) sensor with fully integrated analog front end (AFE), an accelerometer, and a Bluetooth low energy (BLE) module with USB charging module. The accelerometer output is the reference signal for Least Mean Square (LMS) adaptive filter. The LMS filter is a noise canceler that will adaptively remove the motion artifacts and interferences.

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A Robust Algorithm for Real-Time Peak Detection of Photoplethysmograms Using a Personal Computer Mouse

Tran

Chung

2015

IEEE Sensors J.

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Monitoring the photoplethysmogram (PPG) signal is essential for cardiovascular patients in a hospital or at home, as well as for those working in front of a personal computer (PC) at the office every day. Therefore, we developed a wireless PPG mouse that consists of a PC mouse, PPG sensor, and Bluetooth mote. The sensor is located within the PC mouse therefore the structure of the ordinary mouse is not changed. A user's thumb can easily touch the surface of a sensor for PPG signal monitoring. However, it is challenging to process the signals collected from the PPG mouse, especially in cases where the mouse moves quickly or the user performs multiple actions on the mouse buttons. In this study, we propose a robust algorithm to detect the PPG peak under big motion artifact conditions. In the proposed algorithm, an adaptive method enables simultaneous detection of true peaks and eliminates fake peaks from the acquired PPG signal. Next, these detected error peaks can be corrected by a random error estimator. The combination of two sequential methods enhances the robustness of the algorithm for distinguishing irregular PPG patterns. The proposed algorithm presents an advantage for real-time applications and continuous heart rate monitoring systems using a wireless PPG sensor implemented in a PC mouse. . His current research interests include the design of ultra-low-power body sensor nodes with energy harvesting, wireless body sensor network, and biomedical signal processing.Wan-Young Chung received the B.Eng. and Master degrees in Electronic . His current research interests include wireless sensor networks, ubiquitous healthcare and automobile applications, smart lighting with visible light communication, and embedded systems. He is now the leader of team for next generation u-healthcare technology development, which is supported by the Brain Korea 21 (BK21) plus project.

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Implementation of Extended Kalman Filter for Real-Time Noncontact ECG Signal Acquisition in Android-Based Mobile Monitoring System

Cited by 8 publications

References 13 publications

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Reduction of Motion Artifacts and Improvement of R Peak Detecting Accuracy Using Adjacent Non-Intrusive ECG Sensors

Design of a Wireless Non-Contact Wearable System for Infants Using Adaptive Filter

A Robust Algorithm for Real-Time Peak Detection of Photoplethysmograms Using a Personal Computer Mouse

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