Biomedical Signal Compression

2008 ◽  
pp. 157-166 ◽  
Author(s):  
Pedro de A. Berger ◽  
Francisco A. de O. Nascimento ◽  
Leonardo R.A.X. de Menezes ◽  
Adson F. da Rocha ◽  
Joao L.A. Carvalho
Keyword(s):  
Biomedical Engineering ◽  
Ambulatory Monitoring ◽  
Digital Transmission ◽  
Signal Compression ◽  
Biomedical Signal ◽  
Technical Feasibility ◽  
Phone Line ◽  
Biomedical Signals ◽  
Ecg Signals ◽  
Remote Treatment

Digitization of biomedical signals has been used in several areas. Some of these include ambulatory monitoring, phone line transmission, database storage, and several other applications in health and biomedical engineering. These applications have helped in diagnostics, patient care, and remote treatment. One example is the digital transmission of ECG signals, from the patient’s house or ambulance to the hospital. This has been proven useful in cardiac diagnoses. Biomedical signals need to be digitally stored or transmitted with a large number of samples per second, and with a great number of bits per sample, in order to assure the required fidelity of the waveform for visual inspection. Therefore, the use of signal compression techniques is fundamental for cost reduction and technical feasibility of storage and transmission of biomedical signals.

scholarly journals The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

2021 ◽  
Vol 11 (13) ◽  
pp. 5908
Author(s):  
Raquel Cervigón ◽  
Brian McGinley ◽  
Darren Craven ◽  
Martin Glavin ◽  
Edward Jones
Keyword(s):  
Atrial Fibrillation ◽  
Characteristic Curve ◽  
Set Partitioning ◽  
Constrained Systems ◽  
Battery Life ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Signal Compression ◽  
Ecg Signals ◽  
Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

scholarly journals Two Electrode ECG And EOG System For Monitoring Applications

2019 ◽  
Vol 8 (11) ◽  
pp. 262-265
Keyword(s):  
Reference Electrode ◽  
Cost Effective ◽  
Electrode System ◽  
Ecg Signal ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Arm Position ◽  
The Stability ◽  
Electrocardiogram Ecg ◽  
Total Gain

Heart and Eye are two vital organs in the human system. By knowing the Electrocardiogram (ECG) and Electro-oculogram (EOG), one will be able to tell the stability of the heart and eye respectively. In this project, we have developed a circuit to pick the ECG and EOG signal using two wet electrodes. Here no reference electrode is used. EOG and ECG signals have been acquired from ten healthy subjects. The ECG signal is obtained from two positions, namely wrist and arm position respectively. The picked-up biomedical signal is recorded and heart rate information is extracted from ECG signal using the biomedical workbench. The result found to be promising and acquired stable EOG and ECG signal from the subjects. The total gain required for the arm position is higher than the wrist position for the ECG signal. The total gain necessary for the EOG signal is higher than the ECG signal since the ECG signal is in the range of millivolts whereas EOG signal in the range of microvolts. This two-electrode system is stable, cost-effective and portable while still maintaining high common-mode rejection ratio (CMRR).

scholarly journals Noise reduction in ECG Signals for Bio-telemetryb

2019 ◽  
Vol 9 (1) ◽  
pp. 505
Author(s):  
V. Jagan Naveen ◽  
K. Murali Krishna ◽  
K. Raja Rajeswari
Keyword(s):  
Reducing Power ◽  
Ecg Signal ◽  
Noise Power ◽  
Signal Power ◽  
Mean Square ◽  
Rls Algorithm ◽  
Biomedical Signal ◽  
Ecg Signals ◽  
Power Line Interference ◽  
Line Interference

<p><span lang="EN-US">In Biotelemetry, Biomedical signal such as ECG is extremely important in the diagnosis of patients in remote location and is recorded commonly with noise. Considered attention is required for analysis of ECG signal to find the patho-physiology and status of patient. In this paper, LMS and RLS algorithm are implemented on adaptive FIR filter for reducing power line interference (50Hz) and (AWGN) noise on ECG signals .The ECG signals are randomly chosen from MIT_BIH data base and de-noising using algorithms. The peaks and heart rate of the ECG signal are estimated. The measurements are taken in terms of Signal Power, Noise Power and   Mean Square Error.</span></p>

Biomedical Signal Analysis and Its Physio-Clinical Perspectives

2018 ◽  
pp. 1-12
Author(s):  
Kamlesh Jha
Keyword(s):  
Biomedical Engineering ◽  
Signal Analysis ◽  
Clinical Settings ◽  
Engineering Science ◽  
Data Types ◽  
Biomedical Signal ◽  
Biomedical Signal Analysis ◽  
Prime Concern ◽  
Product Delivery ◽  
State Of Art

The field of Biomedical engineering has brought two apparently diagonally placed poles of academia of excellence, i.e., field of medicine and the field of state of art engineering science to a closed proximity. Now a day most if not all of the state of art diagnostics in the field of medicine are almost totally dependent upon biomedical signal analysis. Whole of the biological systems are run by nothing but the bio-signals. The process of signal analysis depends upon the types of signals, recording methods, data types, need of compression and portability and possibility of artifacts. The important areas of the clinician's prime concern are the reliability of the data generated, the utility of the data produced in the real clinical settings in making a diagnosis and interference of the diverse type of equipment's signals with each other and its impact upon the final output. Physiologists act as a bridge between the biomedical engineering and the clinician's need assessment and product delivery process.

Explicit Haar–Schauder multiwavelet filters and algorithms. Part II: Relative entropy-based estimation for optimal modeling of biomedical signals

2019 ◽  
Vol 17 (05) ◽  
pp. 1950038 ◽  
Author(s):  
Makerem Zemni ◽  
Malika Jallouli ◽  
Anouar Ben Mabrouk ◽  
Mohamed Ali Mahjoub
Keyword(s):  
Image Processing ◽  
Relative Entropy ◽  
Simulation Study ◽  
Wavelet Theory ◽  
Biomedical Signal ◽  
New Approach ◽  
Biomedical Signals ◽  
Image Processing And Analysis ◽  
Signal Image ◽  
Optimal Modeling

Biomedical signal/image processing and analysis are always fascinating tasks in scientific researches, both theoretical and practical. One of the powerful tools in such topics is wavelet theory which has been proved to be challenging since its discovery. One of the best measures of the optimality of reconstruction of signals/images is the well-known Shannon’s entropy. In wavelet theory, this is very well known and researchers are familiar with it. In the present work, a step forward is proposed based on more general wavelet tools. New approach is proposed for the reconstruction of signals/images provided with multiwavelets Shannon-type entropy to evaluate the order/disorder of the reconstructed signals/images. Efficiency and accuracy of the approach is confirmed by a simulation study on several models such as ECG, EEG and DNA/Proteins’ signals.

Robust Method for Separation of Noisy Biomedical Signals

2010 ◽  
Vol 26-28 ◽  
pp. 5-8
Author(s):  
Yong Jian Zhao ◽  
Bio Qiang Liu
Keyword(s):  
Extraction Method ◽  
Robust Method ◽  
Biomedical Signals ◽  
Noise Interference ◽  
Ecg Signals ◽  
Blind Source Extraction ◽  
Physiological Processes ◽  
The One ◽  
Source Of Information ◽  
Source Extraction

Biomedical signals are a rich source of information about physiological processes, but they are often contaminated by noise. In order to separate biomedical signals from mixtures effectually, we propose a novel blind source extraction method via independent component analysis (ICA). The robustness with respect to noise of this method lies in two-fold: on the one hand, the method does not lead to biassed estimates and, on the other hand, it minimizes the amount of signal and noise interference on the estimated sources. Preliminary results tested with ECG signals have demonstrated that the proposed method may be promising for blindly separating biomedical signals in the presence of noise and further decompose the mixed signals into subcomponents.

scholarly journals Symbolic Recurrence Analysis of RR Interval to Detect Atrial Fibrillation

2019 ◽  
Vol 8 (11) ◽  
pp. 1840 ◽  
Author(s):  
Jesús Pérez-Valero ◽  
M. Victoria Caballero Pintado ◽  
Francisco Melgarejo ◽  
Antonio-Javier García-Sánchez ◽  
Joan Garcia-Haro ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Ambulatory Monitoring ◽  
Ecg Signals ◽  
Predictive Algorithm ◽  
Predictive Algorithms ◽  
Proposed Model ◽  
Quantification Analysis ◽  
Fitting In ◽  
Electrocardiogram Ecg ◽  
Fold Cross Validation

Atrial fibrillation (AF) is a sustained cardiac arrhythmia associated with stroke, heart failure, and related health conditions. Though easily diagnosed upon presentation in a clinical setting, the transient and/or intermittent emergence of AF episodes present diagnostic and clinical monitoring challenges that would ideally be met with automated ambulatory monitoring and detection. Current approaches to address these needs, commonly available both in smartphone applications and dedicated technologies, combine electrocardiogram (ECG) sensors with predictive algorithms to detect AF. These methods typically require extensive preprocessing, preliminary signal analysis, and the integration of a wide and complex array of features for the detection of AF events, and are consequently vulnerable to over-fitting. In this paper, we introduce the application of symbolic recurrence quantification analysis (SRQA) for the study of ECG signals and detection of AF events, which requires minimal pre-processing and allows the construction of highly accurate predictive algorithms from relatively few features. In addition, this approach is robust against commonly-encountered signal processing challenges that are expected in ambulatory monitoring contexts, including noisy and non-stationary data. We demonstrate the application of this method to yield a highly accurate predictive algorithm, which at optimal threshold values is 97.9% sensitive, 97.6% specific, and 97.7% accurate in classifying AF signals. To confirm the robust generalizability of this approach, we further evaluated its performance in the implementation of a 10-fold cross-validation paradigm, yielding 97.4% accuracy. In sum, these findings emphasize the robust utility of SRQA for the analysis of ECG signals and detection of AF. To the best of our knowledge, the proposed model is the first to incorporate symbolic analysis for AF beat detection.

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