scholarly journals A new method of electrodes placement to improve QRS detection in real-time stress ECG acquisition

2015 ◽  
Vol 18 (2) ◽  
pp. 159-163
Author(s):  
Dang Cao Le ◽  
Tan Hoang Nguyen ◽  
Nam Hoai Phan ◽  
Quoc Minh Thai
Keyword(s):  
Real Time ◽  
New Method ◽  
Dynamic Threshold ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Threshold Method ◽  
Baseline Drift ◽  
Different Types ◽  
Detecting Method ◽  
Real Time Application

In dynamic threshold method to detect QRS complex from ECG signal, especially in real-time application, there are two main issues: baseline drift and noise. This paper introduces an improved QRS complex detecting method using dynamic threshold algorithm combined with a new method of electrodes placement to minimize baseline drift and different types of noise in real-time ECG acquisition with moving patients. Our method proved to be more effective in detecting QRS complex with less error due to minimized baseline drift and noise in original ECG signal.

Efficient and Robust Approach for Heartbeat Detection of ECG Signal

2019 ◽  
Vol 29 (08) ◽  
pp. 2050133
Author(s):  
Anas Fouad Ahmed ◽  
Mohammed Abdulmunem Ahmed ◽  
Hussain Mustafa Bierk
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Suggested Method ◽  
The Other ◽  
Ecg Signal ◽  
Excellent Performance ◽  
Robust Approach ◽  
Different Types ◽  
Three Stages ◽  
Electrocardiogram Ecg

This paper introduces an efficient and robust method for heartbeat detection based on the calculated angles between the successive samples of electrocardiogram (ECG) signal. The proposed approach involves three stages: filtering, computing the angles of the signal and thresholding. The suggested method is applied to two different types of ECG databases (QTDB and MIT-BIH). The results were compared with the other algorithms suggested in previous works. The proposed approach outperformed the other algorithms, in spite of its simplicity and their fast calculations. These features make it applicable in real-time ECG diagnostics systems. The suggested method was implemented in real-time using a low cost ECG acquisition system and it shows excellent performance.

scholarly journals Real Time Detection of R – Peak in QRS Complex of ECG using Microcontroller

2018 ◽  
Vol 11 (1) ◽  
pp. 372
Author(s):  
Santipriya N ◽  
Venkateswara Rao M ◽  
Arun V ◽  
R Karthik
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Heart Diseases ◽  
Processing Algorithm ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Peak Detector ◽  
Signal Processing Algorithm ◽  
The Subject ◽  
Real Time Detection

<p>Real-time detection of R peaks in QRS complex of ECG signal is the first step in the processing of ECG waveform. Based on this, various other ECG parameters can be extracted. These parameters provide substantial information about various heart diseases. In this paper, we are proposing a method to detect R – peaks of ECG signal dynamically. The most prominent role in the R – peak detector is executed by the microcontroller. This method originates by acquiring signal from the subject and necessary pre-processing is carried out on the signal in order to achieve the denoised signal. Subsequently, this filtered signal is handed over to microcontroller where a pulse is generated for each R – peak that is found in the QRS complex of ECG signal. The microcontroller is embedded with a signal processing algorithm. The algorithm used to determine the R – peaks is double differentiation method which is straightforward and robust.  </p>

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.

Design and Application of Human Movement Respiratory and ECG Signal Acquisition System

2020 ◽  
Vol 10 (4) ◽  
pp. 890-897
Author(s):  
Huayu Zhao
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Human Movement ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Ecg Signal ◽  
Baseline Drift ◽  
Respiratory Signal ◽  
Power Frequency ◽  
Guide System

To realize the design of mobile phone human movement breathing and electrocardiograph (ECG) signal acquisition system based on Bluetooth transmission, the principle of the generation and detection of ECG and respiratory signal and the guide system of signal acquisition are analyzed. Additionally, the hardware of the system is designed, including the hardware of the signal acquisition system, the design of ADS1292R ECG and respiratory signal acquisition module, the design of the main control chip and the design of the Bluetooth module. Then, the digital filtering processing of the ECG and respiratory signals is completed, including the baseline drift filtering and the suppression of the power frequency interference. The results show that the monitoring system runs well and it can effectively collect ECG and respiratory signals, calculate heart rate and respiratory frequency in real time, and display ECG waveform in real time. To sum up, the monitoring system is of great significance for real-time monitoring of the patient's condition.

scholarly journals Discrete Cosine Transform based ECG Signal Analysis and Processing

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 519-539
Author(s):  
Aqeel Mohsin Hamad
Keyword(s):  
Discrete Cosine Transform ◽  
Threshold Value ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Threshold Method ◽  
Basic Part ◽  
Cosine Transform ◽  
Specificity And Sensitivity ◽  
Reconstructed Signal ◽  
Frequency Components

Cardiovascular disease (CADs) is considered the primary leading cause of death. Irregular activity of heart, these disease can be detected and classified by Electrocardiogram (ECG), which is constructed from using electrodes placed on human skin to record the electrical activity of the heart. Because QRS complex represents the basic part of the ECG signal, these components should be recognized in order to analysis the other characteristics of the signal. Different methods and algorithms are proposed to analysis and processing the ECG signal. In this paper, a new QRS complex recognition method are proposed based on discrete cosine transform (DCT) with variable adaptive threshold method, which is used to determine threshold based on characteristic of each ECG signal to detect upper and lower levels of threshold to detect the peak of the signal. At first, the DCT is applied to the ECG signal to isolate it into different coefficients and eliminate or reduce the noises of the signal based on processing of high frequency components of DCT coefficients, which have less information, then the ECG is reconstructed by cropping the most important coefficients to be used in threshold determination. The basic idea is that the reconstructed signal have high differences between the components of the signal, and this facilitates the process of calculating the threshold value, which is used later to find peaks of ECG signal. The proposed method is tested and its performance are determined based on three different datasets, which are MITBIH Arrhythmia dataset, (LTSTDB) and (EDB) and the performance are evaluated using different metrics, which are Detection rate, accuracy, specificity and sensitivity. The experimental results show that the proposed method is performed or outperformed other works, therefore it can be used in peak detection applications.

REAL-TIME HAND DETECTION BY DEPTH IMAGES: A SURVEY

2016 ◽  
Vol 78 (2) ◽  
Author(s):  
Mostafa Karbasi ◽  
Zeeshan Bhatti ◽  
Reza Aghababaeyan ◽  
Sara Bilal ◽  
Abdolvahab Ehsani Rad ◽  
...  
Keyword(s):  
Real Time ◽  
Human Hand ◽  
Hand Detection ◽  
Shape Information ◽  
Future Directions ◽  
Depth Data ◽  
Depth Images ◽  
Detection And Tracking ◽  
Different Types ◽  
Real Time Application

Human hand detection can enable human to communicate with a machine and interact without any external device. Human hands play an important role in different applications such as medical image processing, sign language translator, gesture recognition and augmented reality. A human hand has different length and breadth for male and female. So, it is a complex articulated object consisting many connected parts and joints. Traditional methods for hand detection and tracking used color and shape information from RGB camera. Using a depth camera for hand detection and tracking is a challenging and interesting domain in computer vision. Some research has shown that using depth data for hand detection can improve human computer interaction. Recently, researchers used depth data in different hand detection and tracking methods in real time application. This paper explains different types of methods which are used for human hand detection. Various techniques and methods are explored and analyzed in this survey to determine the shortfalls and future directions in the field of hand detection from depth data.

scholarly journals Detection of Real Time QRS Complex Using Wavelet Transform

2018 ◽  
Vol 8 (5) ◽  
pp. 2857
Author(s):  
Fatima Yasmeen ◽  
Mohammad Arifuddin Mallick ◽  
Yusuf Uzzaman Khan
Keyword(s):  
Wavelet Transform ◽  
Real Time ◽  
Wavelet Denoising ◽  
Ecg Signal ◽  
Qrs Complex ◽  
Qrs Detection ◽  
Third Order ◽  
Qrs Complex Detection ◽  
Novel Method ◽  
Complex Detection

<p><span lang="EN-IN">This paper presents a novel method for QRS detection. To accomplish this task ECG signal was first filtered by using a third order Savitzky Golay filter. The filtered ECG signal was then preprocessed by a Wavelet based denoising in a real-time fashion to minimize the undefined noise level. R-peak was then detected from denoised signal after wavelet denoising. Windowing mechanism was also applied for finding any missing R-peaks. All the 48 records have been used to test the proposed method. During this testing, 99.97% sensitivity and 99.99% positive predictivity is obtained for QRS complex detection.</span></p>

scholarly journals Real Time Arrhythmia Monitoring and Classification Based on Edge Computing and DNN

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mingxin Liu ◽  
Ningning Shao ◽  
Chaoxuan Zheng ◽  
Ji Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Communication Networks ◽  
Network Model ◽  
Network Models ◽  
Ecg Signal ◽  
Wireless Communication Networks ◽  
Accuracy Rate ◽  
Residual Network ◽  
Different Types

In this paper, we investigate how to incorporate intelligence into the human-centric IoT edges to detect arrhythmia, a heart condition often associated with morbidity and even mortality. We propose a classification algorithm based on the intrapatient convolutional neural network model and the interpatient attention residual network model to automatically identify the type of arrhythmia in the edges. As the imbalance categories in the MIT-BIH arrhythmia database which needs to be used in the algorithm, we slice and overlap the original ECG signal to homogenize the heartbeat sets of different types, and then the preprocessed data was used to train the two proposed network models; the results reached an overall accuracy rate of 99.03% and an F1 value of 0.87, respectively. The proposed algorithm model can be used as a real-time diagnostic tool for the remote E-health system in next generation wireless communication networks.

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