Performance evaluation for the sliding area-based T wave detection method on the QT database

2017 ◽  
Author(s):  
Haixia Shang ◽  
Shoushui Wei ◽  
Feifei Liu ◽  
Ling Zhang ◽  
Chengyu Liu
Keyword(s):  
Performance Evaluation ◽  
Detection Method ◽  
Wave Detection ◽  
T Wave

scholarly journals Proposal and Performance Evaluation of a Magneto-Striction Type Torque Sensor Consisting of Small-Sized Coils Connected in Series

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Takahiko Tsujisawa ◽  
Kazuhiro Yamakawa
Keyword(s):  
Performance Evaluation ◽  
Response Time ◽  
Detection Method ◽  
Torque Sensor ◽  
Detection Range ◽  
Performance Targets ◽  
Sensor Drift ◽  
In Series ◽  
Target Performance ◽  
And Performance

We propose a sensor consisting of small-sized coils connected in series and a detection method for the sensor based on the iteration of the periodic time difference. The evaluation results are also presented and show the effectiveness of the proposed system. The target performance of the sensor is as follows: (i) a detection range from 0 to ±100 Nm, (ii) a hysteresis error of less than 1%, (iii) an angular-dependent noise of less than 2%, and (iv) a sensor drift of less than 2%. From the evaluation results, it is clear that these performance targets, as well as a sufficient response time, are realized.

T-Wave Alternans Detection Analysis and Software Design

2013 ◽  
Vol 389 ◽  
pp. 936-940
Author(s):  
Kang Hui Yan ◽  
Xiang Kui Wan ◽  
Lin Dai
Keyword(s):  
Left Ventricular Ejection Fraction ◽  
Ejection Fraction ◽  
Left Ventricular ◽  
Ventricular Ejection ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Wave Detection ◽  
T Wave ◽  
Detection Analysis ◽  
T Wave Alternans

ECG machine, currently on the market are generally not a function of the T-wave alternans detection analysis. Studies have shown that T-wave alternans can accurately predict sudden cardiac death, higher predictive accuracy in the case of combination of left ventricular ejection fraction (Left Ventricular Ejection Fraction, LVEF). This paper presents the design of function modules of T-wave alternans software based on the QT environment. Focus on the data processing module of the software, such as the QRS wave detection, T wave detection, the matrix of the T-wave extraction, T-wave alternans detection.

A REAL-TIME QT INTERVAL DETECTION ALGORITHM

2008 ◽  
Vol 08 (02) ◽  
pp. 251-263 ◽  
Author(s):  
Z. E. HADJ SLIMANE ◽  
F. BEREKSI REGUIG
Keyword(s):  
Real Time ◽  
Qt Interval ◽  
Detection Algorithm ◽  
Qrs Detection ◽  
Signal To Noise ◽  
Ecg Signals ◽  
Wave Detection ◽  
T Wave ◽  
Electrocardiogram Ecg ◽  
Ventricular Depolarization

The QT interval is the electrocardiographic representation of the duration of ventricular depolarization and repolarization. In this paper, we have developed a new real-time QT interval detection algorithm for automatically locating the onset of QRS and the end of the T wave. The algorithm consists of several steps: signal-to-noise enhancement, QRS detection, QRS onset, and T-wave end definition. The detection algorithm is tested on electrocardiogram (ECG) signals from the universal MIT-BIH Arrhythmia Database. The resulting QRS detection algorithm has a sensitivity of 99.79% and a specificity of 99.72%. The QRS onset and T-wave detection algorithm is tested using several data records from the MIT/BIH Arrhythmia Database. The results obtained are shown to be highly satisfactory.

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