Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management

2008 ◽  
Vol 367 (1887) ◽  
pp. 235-253 ◽  
Author(s):  
Leif Sörnmo ◽  
Martin Stridh ◽  
Daniela Husser ◽  
Andreas Bollmann ◽  
S. Bertil Olsson
Keyword(s):  
Atrial Fibrillation ◽  
Signal Processing ◽  
Clinical Management ◽  
Advanced Characterization ◽  
Non Invasive ◽  
Therapy Efficacy ◽  
Electrocardiogram Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

The analysis of atrial fibrillation in non-invasive ECG recordings has received considerable attention in recent years, spurring the development of signal processing techniques for more advanced characterization of the atrial waveforms than previously available. The present paper gives an overview of different approaches to the extraction of atrial activity in the ECG and to the characterization of the resulting atrial signal with respect to its spectral properties. So far, the repetition rate of the atrial waves is the most studied parameter and its significance in clinical management is briefly considered, including the identification of pathomechanisms and prediction of therapy efficacy.

scholarly journals A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

2020 ◽  
Vol 13 ◽  
pp. 51-73 ◽  
Author(s):  
Radana Kahankova ◽  
Radek Martinek ◽  
Rene Jaros ◽  
Khosrow Behbehani ◽  
Adam Matonia ◽  
...  
Keyword(s):  
Signal Processing ◽  
Non Invasive ◽  
Fetal Electrocardiography ◽  
Processing Techniques ◽  
Signal Processing Techniques

scholarly journals An Overview of Signal Processing Techniques for Remote Health Monitoring Using Impulse Radio UWB Transceiver

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2479 ◽  
Author(s):  
Faheem Khan ◽  
Asim Ghaffar ◽  
Naeem Khan ◽  
Sung Ho Cho
Keyword(s):  
Heart Rate ◽  
Signal Processing ◽  
Health Monitoring ◽  
Vital Signs ◽  
Impulse Radio ◽  
Non Invasive ◽  
Uwb Radar ◽  
Processing Techniques ◽  
Signal Processing Techniques ◽  
Remote Health

Non-invasive remote health monitoring plays a vital role in epidemiological situations such as SARS outbreak (2003), MERS (2015) and the recently ongoing outbreak of COVID-19 because it is extremely risky to get close to the patient due to the spread of contagious infections. Non-invasive monitoring is also extremely necessary in situations where it is difficult to use complicated wired connections, such as ECG monitoring for infants, burn victims or during rescue missions when people are buried during building collapses/earthquakes. Due to the unique characteristics such as higher penetration capabilities, extremely precise ranging, low power requirement, low cost, simple hardware and robustness to multipath interferences, Impulse Radio Ultra Wideband (IR-UWB) technology is appropriate for non-invasive medical applications. IR-UWB sensors detect the macro as well as micro movement inside the human body due to its fine range resolution. The two vital signs, i.e., respiration rate and heart rate, can be measured by IR-UWB radar by measuring the change in the magnitude of signal due to displacement caused by human lungs, heart during respiration and heart beating. This paper reviews recent advances in IR- UWB radar sensor design for healthcare, such as vital signs measurements of a stationary human, vitals of a non-stationary human, vital signs of people in a vehicle, through the wall vitals measurement, neonate’s health monitoring, fall detection, sleep monitoring and medical imaging. Although we have covered many topics related to health monitoring using IR-UWB, this paper is mainly focused on signal processing techniques for measurement of vital signs, i.e., respiration and heart rate monitoring.

scholarly journals Characterization of Brain Stroke Using Image and Signal Processing Techniques

2021 ◽  
Author(s):  
Abdullah Alamoudi ◽  
Yousif Abdallah
Keyword(s):  
Signal Processing ◽  
Brain Injuries ◽  
Cross Sectional ◽  
Manual Contour ◽  
Cross Sectional Imaging ◽  
Brain Stroke ◽  
Processing Techniques ◽  
The Brain ◽  
Signal Processing Techniques

Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods.

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