Enhancement of a QRS detection algorithm based on the first derivative, using techniques of a QRS detector algorithm based on non-linear transformations

2009 ◽  
pp. 393-396 ◽  
Author(s):  
C. Vidal ◽  
P. Charnay ◽  
P. Arce
Keyword(s):  
Detection Algorithm ◽  
Qrs Detection ◽  
Linear Transformations ◽  
First Derivative ◽  
Non Linear

scholarly journals A Real-time QRS Detector Based on Low-pass Differentiator and Hilbert Transform

2018 ◽  
Vol 175 ◽  
pp. 02008 ◽  
Author(s):  
Daizong Yang ◽  
Yue Zhang
Keyword(s):  
Real Time ◽  
Hilbert Transform ◽  
High Sensitivity ◽  
Detection Algorithm ◽  
Center Frequency ◽  
Qrs Detection ◽  
Threshold Method ◽  
Physiological Signal ◽  
First Derivative ◽  
Adjustable Center

Electrocardiogram(ECG) is an important physiological signal of the human body. It is widely used in identification and arrhythmia detection. The first step of ECG application is signal segmentation, that is, the QRS detection. An effective and real-time QRS detection algorithm is proposed in this paper. A differentiator with adjustable center frequency is used to capture the first derivative information of the frequency band of the electrocardiogram. Then Hilbert transform is used to generate the envelope of the first derivative. After that, a dual threshold method is introduced to decrease FP and FN. Finally, a more precise R wave position is determined based on derivative method. The detector is validated on MIT-BIH arrhythmia database. The result show that the proposed algorithm has a high Sensitivity of 99.87%, Specificity of 99.84%, and the detection error rate is 0.28%. The average execution time of a 30 minutes record is 2.45s.

EFFICIENT QRS COMPLEX DETECTION ALGORITHM IMPLEMENTATION ON SOC-BASED EMBEDDED SYSTEM

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Muhammad Amin Hashim ◽  
Yuan Wen Hau ◽  
Rabia Baktheri
Keyword(s):  
Embedded System ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Moving Windows ◽  
Field Programmable ◽  
Complex Detection ◽  
On Chip

This paper studies two different Electrocardiography (ECG) preprocessing algorithms, namely Pan and Tompkins (PT) and Derivative Based (DB) algorithm, which is crucial of QRS complex detection in cardiovascular disease detection. Both algorithms are compared in terms of QRS detection accuracy and computation timing performance, with implementation on System-on-Chip (SoC) based embedded system that prototype on Altera DE2-115 Field Programmable Gate Array (FPGA) platform as embedded software. Both algorithms are tested with 30 minutes ECG data from each of 48 different patient records obtain from MIT-BIH arrhythmia database. Results show that PT algorithm achieve 98.15% accuracy with 56.33 seconds computation while DB algorithm achieve 96.74% with only 22.14 seconds processing time. Based on the study, an optimized PT algorithm with improvement on Moving Windows Integrator (MWI) has been proposed to accelerate its computation. Result shows that the proposed optimized Moving Windows Integrator algorithm achieves 9.5 times speed up than original MWI while retaining its QRS detection accuracy. 

scholarly journals Transformations of Telegraph Processes and Their Financial Applications

Risks ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 147
Author(s):  
Anatoliy A. Pogorui ◽  
Anatoliy Swishchuk ◽  
Ramón M. Rodríguez-Dagnino
Keyword(s):  
Differential Equation ◽  
Stock Prices ◽  
Option Prices ◽  
Linear Transformations ◽  
Financial Applications ◽  
Telegraph Process ◽  
Put Option ◽  
New Models ◽  
Non Linear ◽  
New Applications

In this paper, we consider non-linear transformations of classical telegraph process. The main results consist of deriving a general partial differential Equation (PDE) for the probability density (pdf) of the transformed telegraph process, and then presenting the limiting PDE under Kac’s conditions, which may be interpreted as the equation for a diffusion process on a circle. This general case includes, for example, classical cases, such as limiting diffusion and geometric Brownian motion under some specifications of non-linear transformations (i.e., linear, exponential, etc.). We also give three applications of non-linear transformed telegraph process in finance: (1) application of classical telegraph process in the case of balance, (2) application of classical telegraph process in the case of dis-balance, and (3) application of asymmetric telegraph process. For these three cases, we present European call and put option prices. The novelty of the paper consists of new results for non-linear transformed classical telegraph process, new models for stock prices based on transformed telegraph process, and new applications of these models to option pricing.

scholarly journals A Novel Wavelet-Based Algorithm for Detection of QRS Complex

2019 ◽  
Vol 9 (10) ◽  
pp. 2142 ◽  
Author(s):  
Chun-Cheng Lin ◽  
Hung-Yu Chang ◽  
Yan-Hua Huang ◽  
Cheng-Yu Yeh
Keyword(s):  
Evaluation Method ◽  
Decision Rules ◽  
Detection Algorithm ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Qrs Complex Detection ◽  
Frequency Components ◽  
Noise Evaluation ◽  
Complex Detection ◽  
Almost All

Accurate QRS detection is an important first step for almost all automatic electrocardiogram (ECG) analyzing systems. However, QRS detection is difficult, not only because of the wide variety of ECG waveforms but also because of the interferences caused by various types of noise. This study proposes an improved QRS complex detection algorithm based on a four-level biorthogonal spline wavelet transform. A noise evaluation method is proposed to quantify the noise amount and to select a lower-noise wavelet detail signal instead of removing high-frequency components in the preprocessing stage. The QRS peaks can be detected by the extremum pairs in the selected wavelet detail signal and the proposed decision rules. The results show the high accuracy of the proposed algorithm, which achieves a 0.25% detection error rate, 99.84% sensitivity, and 99.92% positive prediction value, evaluated using the MIT-BIT arrhythmia database. The proposed algorithm improves the accuracy of QRS detection in comparison with several wavelet-based and non-wavelet-based approaches.

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