scholarly journals Detection of a Local Slow Potential Preceding the Surface QRS Complex During Non-Preexcited Impulse Propagation

1998 ◽  
Vol 62 (10) ◽  
pp. 760-764 ◽  
Author(s):  
Yoshinori Kobayashi ◽  
Yasushi Miyauchi ◽  
Naomi Kawaguchi ◽  
Kazuko Ohmura ◽  
Hirokazu Saitoh ◽  
...  
2019 ◽  
Vol 21 (1) ◽  
pp. 48 ◽  
Author(s):  
Ljuba Bacharova

The aim of this opinion paper is to point out the knowledge gap between evidence on the molecular level and clinical diagnostic possibilities in left ventricular hypertrophy (LVH) regarding the prediction of ventricular arrhythmias and monitoring the effect of therapy. LVH is defined as an increase in left ventricular size and is associated with increased occurrence of ventricular arrhythmia. Hypertrophic rebuilding of myocardium comprises interrelated processes on molecular, subcellular, cellular, tissue, and organ levels affecting electrogenesis, creating a substrate for triggering and maintaining arrhythmias. The knowledge of these processes serves as a basis for developing targeted therapy to prevent and treat arrhythmias. In the clinical practice, the method for recording electrical phenomena of the heart is electrocardiography. The recognized clinical electrocardiogram (ECG) predictors of ventricular arrhythmias are related to alterations in electrical impulse propagation, such as QRS complex duration, QT interval, early repolarization, late potentials, and fragmented QRS, and they are not specific for LVH. However, the simulation studies have shown that the QRS complex patterns documented in patients with LVH are also conditioned remarkably by the alterations in impulse propagation. These QRS complex patterns in LVH could be potentially recognized for predicting ventricular arrhythmia and for monitoring the effect of therapy.


2002 ◽  
Vol 20 (5) ◽  
pp. 0492-0493 ◽  
Author(s):  
Cem Oktay ◽  
Mustafa Kesapli ◽  
Emre Altekin
Keyword(s):  

2019 ◽  
Vol 47 (7) ◽  
pp. 1-9
Author(s):  
Li Jin ◽  
Xu Li ◽  
Jiamei Lu ◽  
Nianqu Chen ◽  
Lin Cheng ◽  
...  

We investigated emotional conflict in an educational context with an emotional body–word Stroop paradigm, examining whether the N450 (a late fronto-central phasic negative event-related potential signature) and slow potential (SP) effects could be evoked in trainee teachers. The N450 effect is characterized by topography and negative polarity of an incongruent minus congruent difference potential, and the SP effect has positive polarity (incongruent minus congruent difference potential). Positive and negative body language examples were obtained from pupils in an actual school context, and emotional words were selected. Compound stimuli were presented, each comprising a congruent or incongruent word displayed across a body image. Event-related potentials were recorded while participants judged body expression valence. Reaction times were longer and accuracies were lower for the incongruent compared to the congruent condition. The N450 component amplitude in the incongruent condition was more negative than in the congruent condition. Results showed a behavioral interference effect and an N450 effect for trainee teachers in this context, thus indicating that the body–word task was efficient in assessing emotional conflict in an educational context, and trainee teachers' perception of body expressions of students could be influenced by emotional signals. The findings further the understanding of emotional conflict in an educational context.


Circulation ◽  
1997 ◽  
Vol 96 (10) ◽  
pp. 3527-3533 ◽  
Author(s):  
Teresa Alberca ◽  
Jesús Almendral ◽  
Petra Sanz ◽  
Aureliano Almazan ◽  
Jose Luis Cantalapiedra ◽  
...  

Author(s):  
Yuichiro Miyazaki ◽  
Takashi Noda ◽  
Koji Miyamoto ◽  
Satoshi Nagase ◽  
Takeshi Aiba ◽  
...  

2021 ◽  
Vol 11 (3) ◽  
pp. 1125
Author(s):  
Htet Myet Lynn ◽  
Pankoo Kim ◽  
Sung Bum Pan

In this report, the study of non-fiducial based approaches for Electrocardiogram(ECG) biometric authentication is examined, and several excessive techniques are proposed to perform comparative experiments for evaluating the best possible approach for all the classification tasks. Non-fiducial methods are designed to extract the discriminative information of a signal without annotating fiducial points. However, this process requires peak detection to identify a heartbeat signal. Based on recent studies that usually rely on heartbeat segmentation, QRS detection is required, and the process can be complicated for ECG signals for which the QRS complex is absent. Thus, many studies only conduct biometric authentication tasks on ECG signals with QRS complexes, and are hindered by similar limitations. To overcome this issue, we proposed a data-independent acquisition method to facilitate highly generalizable signal processing and feature learning processes. This is achieved by enhancing random segmentation to avoid complicated fiducial feature extraction, along with auto-correlation to eliminate the phase difference due to random segmentation. Subsequently, a bidirectional recurrent neural network (RNN) with long short-term memory (LSTM) deep networks is utilized to automatically learn the features associated with the signal and to perform an authentication task. The experimental results suggest that the proposed data-independent approach using a BLSTM network achieves a relatively high classification accuracy for every dataset relative to the compared techniques. Moreover, it exhibited a significantly higher accuracy rate in experiments using ECG signals without the QRS complex. The results also revealed that data-dependent methods can only perform well for specified data types and amendments of data variations, whereas the presented approach can also be considered for generalization to other quasi-periodical biometric signal-based classification tasks in future studies.


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