scholarly journals R-DECO: An open-source Matlab based graphical user interface for the detection and correction of R-peaks

2019 ◽  
Author(s):  
Jonathan Moeyersons ◽  
Matthew Amoni ◽  
Sabine Van Huffel ◽  
Rik Willems ◽  
Carolina Varon
Keyword(s):  
Heart Rate ◽  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Ecg Signal ◽  
Previous Knowledge ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Data Formats ◽  
Processing Steps

AbstractMany of the existing ECG toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations.In this paper we present R-DECO, an easy-to-use graphical user interface for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by an adaptation of the Pan-Tompkins algorithm. Instead of using all the pre-processing steps of the latter algorithm, the proposed algorithm uses only an envelope-based procedure to flatten the ECG and enhance the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and PPV of 99.69% on the MIT/BIH arrhythmia database.Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the graphical user interface and the analysis results can be exported as Matlab or Excel files. The software is publicly available.Through its easy-to-use-graphical user interface, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

scholarly journals R-DECO: an open-source Matlab based graphical user interface for the detection and correction of R-peaks

2019 ◽  
Vol 5 ◽  
pp. e226 ◽  
Author(s):  
Jonathan Moeyersons ◽  
Matthew Amoni ◽  
Sabine Van Huffel ◽  
Rik Willems ◽  
Carolina Varon
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Detection Algorithm ◽  
Ecg Signal ◽  
Previous Knowledge ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Data Formats ◽  
Electrocardiogram Ecg

Many of the existing electrocardiogram (ECG) toolboxes focus on the derivation of heart rate variability features from RR-intervals. By doing so, they assume correct detection of the QRS-complexes. However, it is highly likely that not all detections are correct. Therefore, it is recommended to visualize the actual R-peak positions in the ECG signal and allow manual adaptations. In this paper we present R-DECO, an easy-to-use graphical user interface (GUI) for the detection and correction of R-peaks. Within R-DECO, the R-peaks are detected by using a detection algorithm which uses an envelope-based procedure. This procedure flattens the ECG and enhances the QRS-complexes. The algorithm obtained an overall sensitivity of 99.60% and positive predictive value of 99.69% on the MIT/BIH arrhythmia database. Additionally, R-DECO includes support for several input data formats for ECG signals, three basic filters, the possibility to load other R-peak locations and intuitive methods to correct ectopic, wrong, or missed heartbeats. All functionalities can be accessed via the GUI and the analysis results can be exported as Matlab or Excel files. The software is publicly available. Through its easy-to-use GUI, R-DECO allows both clinicians and researchers to use all functionalities, without previous knowledge.

STRONG REAL-TIME QRS COMPLEX DETECTION

2017 ◽  
Vol 17 (08) ◽  
pp. 1750111 ◽  
Author(s):  
M. M. BENOSMAN ◽  
F. BEREKSI-REGUIG ◽  
E. GORAN SALERUD
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Trunk Muscles ◽  
Computation Method ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Rr Intervals ◽  
Ecg Signals ◽  
Qrs Complex Detection ◽  
Complex Detection

Heart rate variability (HRV) analysis is used as a marker of autonomic nervous system activity which may be related to mental and/or physical activity. HRV features can be extracted by detecting QRS complexes from an electrocardiogram (ECG) signal. The difficulties in QRS complex detection are due to the artifacts and noises that may appear in the ECG signal when subjects are performing their daily life activities such as exercise, posture changes, climbing stairs, walking, running, etc. This study describes a strong computation method for real-time QRS complex detection. The detection is improved by the prediction of the position of [Formula: see text] waves by the estimation of the RR intervals lengths. The estimation is done by computing the intensity of the electromyogram noises that appear in the ECG signals and known here in this paper as ECG Trunk Muscles Signals Amplitude (ECG-TMSA). The heart rate (HR) and ECG-TMSA increases with the movement of the subject. We use this property to estimate the lengths of the RR intervals. The method was tested using famous databases, and also with signals acquired when an experiment with 17 subjects from our laboratory. The obtained results using ECG signals from the MIT-Noise Stress Test Database show a QRS complex detection error rate (ER) of 9.06%, a sensitivity of 95.18% and a positive prediction of 95.23%. This method was also tested against MIT-BIH Arrhythmia Database, the result are 99.68% of sensitivity and 99.89% of positive predictivity, with ER of 0.40%. When applied to the signals obtained from the 17 subjects, the algorithm gave an interesting result of 0.00025% as ER, 99.97% as sensitivity and 99.99% as positive predictivity.

scholarly journals Development of Graphical User Interface to Classify Cardiac Abnormalities using ECG Signal

2019 ◽  
Vol 10 (3) ◽  
pp. 1621-1625
Author(s):  
Sharanya S ◽  
Sridhar PA ◽  
Suresh MP ◽  
Poorana Mary Monisha W ◽  
Tharadevi R
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Signal Analysis ◽  
Cardiac Activity ◽  
Noise Removal ◽  
Ecg Signal ◽  
Online Database ◽  
Ecg Signals ◽  
Classification Technique ◽  
Electrocardiogram Ecg

Analysis of Electrocardiogram (ECG) signal can lead to better detection of cardiac arrhythmia. The important steps involved in the ECG signal analysis include acquisition of data, pre-processing of signal to remove artefacts, feature extraction of attributes and finally identifying abnormalities. This work proposes an efficient implementation of the R-R interval-based ECG classification technique for detecting abnormalities in heart functioning. ECG signals from an online database (PhysioNet.org) was analysed after noise removal for R-R interval, as R peak has the maximum prominent amplitude in ECG wave. Deviation in the R-R interval values obtained from unhealthy was observed and compared with healthy subjects. This observation of cardiac activity can be visualised in our developed Graphical User Interface (GUI). The GUI platform requires only the input of the ECG signal that is to be analysed for abnormalities, which can provide the clinician with the result of cardiac abnormality classification and can help in diagnosis.  

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

scholarly journals A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification

2019 ◽  
Vol 9 (1) ◽  
pp. 201 ◽  
Author(s):  
Di Wang ◽  
Yujuan Si ◽  
Weiyi Yang ◽  
Gong Zhang ◽  
Tong Liu
Keyword(s):  
Heart Rate ◽  
Principal Component ◽  
Identification Accuracy ◽  
Ecg Signal ◽  
Identification Task ◽  
Short Term ◽  
Ecg Signals ◽  
Subject Identification ◽  
Novel Method ◽  
Electrocardiogram Ecg

In the past decades, the electrocardiogram (ECG) has been investigated as a promising biometric by exploiting the subtle discrepancy of ECG signals between subjects. However, the heart rate (HR) for one subject may vary because of physical activities or strong emotions, leading to the problem of ECG signal variation. This variation will significantly decrease the performance of the identification task. Particularly for short-term ECG signal without many heartbeats, the hardly measured HR makes the identification task even more challenging. This study aims to propose a novel method suitable for short-term ECG signal identification. In particular, an improved HR-free resampling strategy is proposed to minimize the influence of HR variability during heartbeat processing. For feature extraction, the Principal Component Analysis Network (PCANet) is implemented to determine the potential difference between subjects. The proposed method is evaluated using a public ECG-ID database that contains various HR data for some subjects. Experimental results show that the proposed method is robust to HR change and can achieve high subject identification accuracy (94.4%) on ECG signals with only five heartbeats. Thus, the proposed method has the potential for application to systems that use short-term ECG signals for identification (e.g., wearable devices).

scholarly journals Simulation Program for 4th Axis CNC Machining in NX CAM System

2019 ◽  
Vol 16 (3) ◽  
pp. 6971-6986
Author(s):  
M. H. Mohamad ◽  
M. N. O. Zahid
Keyword(s):  
User Interface ◽  
Process Planning ◽  
Graphical User Interface ◽  
Visual Basic ◽  
Cnc Machining ◽  
Simulation Program ◽  
Machining Simulation ◽  
Simulation Process ◽  
Processing Steps ◽  
Cam Systems

This paper presents the development of a simulation program to improve the process planning and simulation for 4th axis machining in NX CAM systems. A customised graphical user interface (GUI) was developed to enhance the simulation process planning and reduce the dependency on the user’s experience while developing the machining program in NX CAM systems. The simulation operation was recorded using a journaling tool available in NX CAM while visual basic programming was utilised to customise the code. The results indicate that the developed programs are capable of optimising the 4th axis machining simulation by reducing the processing steps and time with minimum process planning tasks.

HRV

2020 ◽  
pp. 236-264
Author(s):  
Kirti Rawal ◽  
Gaurav Sethi ◽  
Barjinder Singh Saini ◽  
Indu Saini
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Input Signal ◽  
Medical Tourism ◽  
Ecg Signal ◽  
Rr Intervals ◽  
Experimental Protocols ◽  
Hrv Analysis ◽  
Electrocardiogram Ecg ◽  
Electrical Interference

The most important factor involved in heart rate variability (HRV) analysis is cardiac input signal, which is achieved in the form of electrocardiogram (ECG). The ECG signal is used for identifying many electrical defects associated with the heart. In this chapter, many issues involved while ECG recording such as type of the recording instrument, various sources of noise, artifacts, and electrical interference from surroundings is presented. Most importantly, this chapter comprises the details about the experimental protocols followed while ECG recording. Also, the brief overview of medical tourism as well as various interpolation methods used for pre-processing of RR intervals are presented in this chapter.

Sign in / Sign up

Export Citation Format

Share Document