A Review of Signal Processing Techniques for Electrocardiogram Signal Quality Assessment

2018 ◽  
Vol 11 ◽  
pp. 36-52 ◽  
Author(s):  
Udit Satija ◽  
Barathram Ramkumar ◽  
M. Sabarimalai Manikandan
Keyword(s):  
Signal Processing ◽  
Quality Assessment ◽  
Signal Quality ◽  
Electrocardiogram Signal ◽  
Processing Techniques ◽  
Signal Processing Techniques

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

2014 ◽  
Vol 59 (4) ◽  
Author(s):  
Gert Van Dijck ◽  
Marc M. Van Hulle
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Cell Types ◽  
Signal Quality ◽  
Quality Signals ◽  
Processing Techniques ◽  
Electrode Selection ◽  
Signal Processing Techniques ◽  
Processing Steps ◽  
Selection Of

AbstractRecently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the “usefulness” of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The ever-increasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing “good quality signals” or “signals of interest.” This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on “signal quality.” The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist’s appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature “electronic depth control” in contrast to the mechanical repositioning of the electrode shafts in search of “good quality signals” or “signals of interest.”

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.

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

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