DENOISING AND REMOTE MONITORING OF ECG SIGNAL WITH REAL-TIME EXTENDED KALMAN FILTER IN A WEARABLE SYSTEM

2015 ◽  
Vol 27 (01) ◽  
pp. 1550009 ◽  
Author(s):  
Osman Ozkaraca ◽  
Inan Guler
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Single Channel ◽  
Direct Access ◽  
Ecg Signal ◽  
Extended Kalman Filtering ◽  
Real Time System ◽  
Biomedical Device ◽  
Long Term Follow Up ◽  
Conductive Textile

In this paper, a prototype of wearable and wireless electrocardiography (ECG) monitoring system is developed and implemented on DSP and PDA. We present a real-time extended Kalman filtering framework for extracting motion and electromyography (EMG) artifacts from a single-channel ECG in wearable systems as different from other offline studies. Realized prototype is a good example for the usage of the Kalman filter in biomedical real-time system. The average SNR advancement of 9.1430 dB was achieved for denoising, which is average 1 dB more than the other methods such as MABWT, EKF2 by using MIT-BIH database. Additionally, the usability and performances of conductive textile electrodes were evaluated with disposable Ag – AgCl electrodes by using daily activities. A novel textile electrode gave approximately 25.23% better results compared to Ag – AgCl electrodes. Also, UDP, TCP and Web Socket communication protocols have been tested. UDP has been the fastest method for the ECG signal transferring from the patient to the doctor. At the same time, a method is proposed for direct access to the patient by the doctor. The results illustrate that this type of system will submit highly ergonomic solutions among biomedical device technologies. In addition, the usage of such kinds of systems is foreseen for requiring long-term follow-up and disorders.

scholarly journals Adaptive smartphone-based sensor fusion for estimating competitive rowing kinematic metrics

2018 ◽  
Author(s):  
Bryn Cloud ◽  
Britt Tarien ◽  
Richard Crawford ◽  
Jason Moore
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Performance Metrics ◽  
Single Channel ◽  
General Purpose ◽  
Differential Gps ◽  
Elite Rower ◽  
Accuracy And Precision ◽  
Rowing Performance ◽  
Boat Speed

Competitive rowing highly values boat position and velocity data for real-time feedback during training, racing and post-training analysis. The ubiquity of smartphones with embedded position (GPS) and motion (accelerometer) sensors motivates their possible use in these tasks. In this paper, we investigate the use of two real-time digital filters to achieve highly accurate yet reasonably priced measurements of boat speed and distance traveled. Both filters combine acceleration and location data to estimate boat distance and speed; the first using a complementary frequency response-based filter technique, the second with a Kalman filter formalism that includes adaptive, real-time estimates of effective accelerometer bias. The estimates of distance and speed from both filters werevalidated and compared with accurate reference data from a differential GPS system with better than 1 cm precision and a 5 Hz update rate, in experiments using two subjects (an experienced club-level rower and an elite rower) in two different boats on a 300 m course. Compared with single channel (smartphone GPS only) measures of distance and speed, the complementary filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 44%, 42%, and 73%, respectively, while the Kalman filter improved the accuracy and precision of boat speed, boat distance traveled, and distance per stroke by 48%, 22%, and 82%, respectively. Both filters demonstrate promise as general purpose methods to substantially improve estimates of important rowing performance metrics.

scholarly journals A closed-loop stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

2021 ◽  
Author(s):  
Takayuki Onojima ◽  
Keiichi Kitajo
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Autoregressive Model ◽  
Closed Loop ◽  
Time Estimation ◽  
Sensory Stimulation ◽  
Time Signal ◽  
Real Time System ◽  
Phase Dynamics ◽  
Instantaneous Phase

We propose a novel method to estimate the instantaneous oscillatory phase to implement a real-time system for closed-loop sensory stimulation in electroencephalography (EEG) experiments. The method uses Kalman filter-based prediction to estimate current and future EEG signals. We tested the performance of our method in a real-time situation. We demonstrate that the performance of our method shows higher accuracy in predicting the EEG phase than the conventional autoregressive model-based method. A Kalman filter allows us to easily estimate the instantaneous phase of EEG oscillations based on the automatically estimated autoregressive model implemented in a real-time signal processing machine. The proposed method has a potential for versatile applications targeting the modulation of EEG phase dynamics and the plasticity of brain networks in relation to perceptual or cognitive functions.

Design of a nearly linear-phase IIR filter and JPEG compression ECG signal in real-time system

2021 ◽  
Vol 67 ◽  
pp. 102431
Author(s):  
Ngoc Thang Bui ◽  
Thi My Tien Nguyen ◽  
Sumin Park ◽  
Jaeyeop Choi ◽  
Thi Mai Thien Vo ◽  
...  
Keyword(s):  
Real Time ◽  
Jpeg Compression ◽  
Ecg Signal ◽  
Linear Phase ◽  
Time System ◽  
Real Time System ◽  
Iir Filter

scholarly journals Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter

2006 ◽  
Vol 18 (1) ◽  
pp. 80-118 ◽  
Author(s):  
Wei Wu ◽  
Yun Gao ◽  
Elie Bienenstock ◽  
John P. Donoghue ◽  
Michael J. Black
Keyword(s):  
Kalman Filter ◽  
Bayesian Inference ◽  
Real Time ◽  
Probabilistic Model ◽  
Primary Motor Cortex ◽  
Gaussian Model ◽  
Real Time System ◽  
Hand Motion ◽  
Firing Rates ◽  
Motor Cortical

Effective neural motor prostheses require a method for decoding neural activity representing desired movement. In particular, the accurate reconstruction of a continuous motion signal is necessary for the control of devices such as computer cursors, robots, or a patient's own paralyzed limbs. For such applications, we developed a real-time system that uses Bayesian inference techniques to estimate hand motion from the firing rates of multiple neurons. In this study, we used recordings that were previously made in the arm area of primary motor cortex in awake behaving monkeys using a chronically implanted multielectrode microarray. Bayesian inference involves computing the posterior probability of the hand motion conditioned on a sequence of observed firing rates; this is formulated in terms of the product of a likelihood and a prior. The likelihood term models the probability of firing rates given a particular hand motion. We found that a linear gaussian model could be used to approximate this likelihood and could be readily learned from a small amount of training data. The prior term defines a probabilistic model of hand kinematics and was also taken to be a linear gaussian model. Decoding was performed using a Kalman filter, which gives an efficient recursive method for Bayesian inference when the likelihood and prior are linear and gaussian.In off-line experiments, the Kalman filter reconstructions of hand trajectory were more accurate than previously reported results.The resulting decoding algorithm provides a principled probabilistic model of motor-cortical coding, decodes hand motion in real time, provides an estimate of uncertainty, and is straightforward to implement. Additionally the formulation unifies and extends previous models of neural coding while providing insights into the motor-cortical code.

Real-Time System for Sending Data from Three Clients to One Server with MATLAB

2015 ◽  
Vol 2 (1) ◽  
pp. 35-41
Author(s):  
Rivan Risdaryanto ◽  
Houtman P. Siregar ◽  
Dedy Loebis
Keyword(s):  
Information System ◽  
Real Time ◽  
Time System ◽  
Real Time System ◽  
The Real ◽  
Performance Time ◽  
Data Process ◽  
Effectiveness And Efficiency

The real-time system is now used on many fields, such as telecommunication, military, information system, evenmedical to get information quickly, on time and accurate. Needless to say, a real-time system will always considerthe performance time. In our application, we define the time target/deadline, so that the system should execute thewhole tasks under predefined deadline. However, if the system failed to finish the tasks, it will lead to fatal failure.In other words, if the system cannot be executed on time, it will affect the subsequent tasks. In this paper, wepropose a real-time system for sending data to find effectiveness and efficiency. Sending data process will beconstructed in MATLAB and sending data process has a time target as when data will send.

Sign in / Sign up

Export Citation Format

Share Document