scholarly journals Designing an Optimum and Reduced Order Filter for Efficient ECG QRS Peak Detection and Classification of Arrhythmia Data

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hemant Amhia ◽  
A. K. Wadhwani
Keyword(s):  
Peak Detection ◽  
Ecg Signal ◽  
Reduced Order ◽  
Iir Filter ◽  
Order Filter ◽  
Low Pass ◽  
Electrocardiogram Ecg ◽  
Ecg Data

Electrocardiogram (ECG) is commonly used biological signals that show an important role in cardiac analysis. The interpretation and acquisition of QRS complex are significant measures of ECG data dispensation. The R wave has a vital character in the analysis of cardiac rhythm irregularities as well as in the determination of heart rate variability (HRV). This manuscript is proposed to design a new artificial-intelligence-based approach of QRS peak detection and classification of the ECG data. The design of reduced order IIR filter is proposed for the low pass smoothening of the ECG signal data. The min-max optimization is used for optimizing the filter coefficient to design the reduced order filter. In this research paper, elimination of baseline wondering and the power line interferences from the ECG signal is of main attention. The result presented that the accuracy is increased by around 13% over the basic Pan–Tompkins method and around 8% over the existing FIR-filter-based classification rules.

scholarly journals Electrocardiogram (ECG) based stress recognition integrated with different classification of age and gender

2019 ◽  
Vol 15 (1) ◽  
pp. 199
Author(s):  
N. S. Nor Shahrudin ◽  
K. A. Sidek ◽  
A. Z. Jusoh
Keyword(s):  
Data Acquisition ◽  
Stress Level ◽  
Ecg Signal ◽  
Alternative Mechanism ◽  
Age And Gender ◽  
Stress States ◽  
And Gender ◽  
Electrocardiogram Ecg ◽  
Ecg Data

<p class="Abstract"><em><span>Good mental health is important in our daily life. A person commonly finds stress as a barrier to enhance an individual’s performance. Be reminded that not all people have the same level of stress because different people have dissimilar problems in their life. In addition, different level of age and gender will affect unequal amount of stress. Electrocardiogram (ECG) signal is an electrical indicator of the heart that can detect changes of human response which relates to our emotions and reactions. Thus, this research proposed a non-intrusive detector to identify stress level for both gender and different classification of age using the ECG. A total of 30 healthy subjects were involved during the data acquisition stage. Data acquisition which initialize ECG data were divided into two conditions; which are normal and stress states. ECG data for normal state only need the participant to breath in and out normally. In other hand, the participants also need to undergo Stroop Colour word test as a stress inducer to represent ECG in stress state. Then, Sgolay filter was selected in the pre-processing stage to remove artifacts in the signal. The process was followed by feature extraction of the ECG signal and finally classified using RR Interval (RRI), different amplitudes of R peaks and Cardioid graph were used to evaluate the performance of the proposed technique. As a result, Class 5 (age range between 50-59 years old) marks the highest changes of stress level rather than other classes, while women are more affected by stress rather than men by showing tremendous percentage changes between normal and stress level over the proposed classifiers. The result proves that ECG signals can be used as an alternative mechanism to recognize stress more efficiently with the integration of gender and age variabilities.</span></em></p>

scholarly journals An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Qin ◽  
Jianqing Li ◽  
Yinggao Yue ◽  
Chengyu Liu
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Peak Detection Algorithm ◽  
Wavelet Multiresolution Analysis ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Mean Time

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

scholarly journals Classification of Multi Heart Diseases With Android Based Monitoring System

2020 ◽  
pp. 14-22
Keyword(s):  
Feature Extraction ◽  
Heart Diseases ◽  
Confusion Matrix ◽  
Predictive Coding ◽  
Classification Performance ◽  
Support Vector ◽  
Ecg Signal ◽  
Linear Predictive Coding ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) examination via computer techniques that involve feature extraction, pre-processing and post-processing was implemented due to its significant advantages. Extracting ECG signal standard features that requires high processing operation level was the main focusing point for many studies. In this paper, up to 6 different ECG signal classes are accurately predicted in the absence of ECG feature extraction. The corner stone of the proposed technique in this paper is the Linear predictive coding (LPC) technique that regress and normalize the signal during the pre-processing phase. Prior to the feature extraction using Wavelet energy (WE), a direct Wavelet transform (DWT) is implemented that converted ECG signal to frequency domain. In addition, the dataset was divided into two parts , one for training and the other for testing purposes Which have been classified in this proposed algorithm using support vector machine (SVM). Moreover, using MIT AI2 Companion was developed by MIT Center for Mobile Learning, the classification result was shared to the patient mobile phone that can call the ambulance and send the location in case of serious emergency. Finally, the confusion matrix values are used to measure the proposed classification performance. For 6 different ECG classes, an accuracy ration of about 98.15% was recorded. This ratio became 100% for 3 ECG signal classes and decreases to 97.95% by increasing ECG signal to 7 classes.

scholarly journals Classification of Atrial Arrhythmias using Neural Networks

2018 ◽  
Vol 7 (2) ◽  
pp. 90 ◽  
Author(s):  
Jai Utkarsh ◽  
Raju Kumar Pandey ◽  
Shrey Kumar Dubey ◽  
Shubham Sinha ◽  
S. S. Sahu
Keyword(s):  
Normal Sinus Rhythm ◽  
High Accuracy ◽  
Ecg Signal ◽  
Atrial Arrhythmias ◽  
Autoregressive Modelling ◽  
Test Set ◽  
Normal Sinus ◽  
The Neural Network ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) is an important tool used by clinicians for successful diagnosis and detection of Arrhythmias, like Atrial Fibrillation (AF) and Atrial Flutter (AFL). In this manuscript, an efficient technique of classifying atrial arrhythmias from Normal Sinus Rhythm (NSR) has been presented. Autoregressive Modelling has been used to capture the features of the ECG signal, which are then fed as inputs to the neural network for classification. The standard database available at Physionet Bank repository has been used for training, validation and testing of the model. Exhaustive experimental study has been carried out by extracting ECG samples of duration of 5 seconds, 10 seconds and 20 seconds. It provides an accuracy of 99% and 94.3% on training and test set respectively for 5 sec recordings. In 10 sec and 20 sec samples it shows 100% accuracy. Thus, the proposed method can be used to detect the arrhythmias in a small duration recordings with a fairly high accuracy.

EVALUATION OF QRS COMPLEX BASED ON DWT COEFFICIENTS ANALYSIS USING DAUBECHIES WAVELETS FOR DETECTION OF MYOCARDIAL ISCHAEMIA

2010 ◽  
Vol 10 (02) ◽  
pp. 273-290 ◽  
Author(s):  
G. M. PATIL ◽  
K. SUBBA RAO ◽  
U. C. NIRANJAN ◽  
K. SATYANARAYAN
Keyword(s):  
Myocardial Ischaemia ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Daubechies Wavelets ◽  
Detection Rates ◽  
Ecg Signals ◽  
Processing Module ◽  
Data Files ◽  
Electrocardiogram Ecg ◽  
Ecg Data

This paper presents a new approach in the field of electrocardiogram (ECG) feature extraction system based on the discrete wavelet transform (DWT) coefficients using Daubechies Wavelets. Real ECG signals recorded in lead II configuration are chosen for processing. The ECG signal was acquired by a battery operated, portable ECG data acquisition and signal processing module. In the second step the ECG signal was denoised using soft thresholding with Symlet4 wavelet. Further denoising was achieved by removing the corresponding wavelet coefficients at higher levels of decomposition. Later the ECG data files were converted to .txt files and subsequently to. mat files before being imported into the Matlab 7.4.0 environment for the computation of the decomposition coefficients. The QRS complexes were grouped as normal or myocardial ischaemic ones based on these decomposition coefficients. The algorithm developed by us was evaluated with control database comprising 120 records and validated using 60 records making up test database. By using the DWT coefficients, we have successfully achieved the myocardial ischaemia detection rates up to 97.5% with the technique developed by us for control data and up to 100% for validation test data.

Filtration and Classification of ECG Signals

2018 ◽  
pp. 72-94 ◽  
Author(s):  
Satya Ranjan Dash ◽  
Asim Syed Sheeraz ◽  
Annapurna Samantaray
Keyword(s):  
Electrical Activity ◽  
Electrical Conduction ◽  
Diagnostic Tool ◽  
Noise Level ◽  
Medical Professionals ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Electrocardiogram Ecg ◽  
Structure And Functions

Electrocardiogram (ECG) is a kind of process of recording the electrical activity/signals of the heart with respect to the time. ECG conveys a wide amount of information related to the structure and functions of the heart, its electrical conduction processes. ECG is a diagnostic tool that the doctors and medical professionals use to measure patients' heart activity by paying attention to the electric current flowing in the heart. Due to the presence of noises, it needs to carry out the filtration process. Filtration is the process of keeping the components of the signals of desired frequencies by setting up an “fc” value and removing the components apart from the said “fc” frequency. It is required to eliminate the noise level from the ECG signal, such that the resultant ECG signal must be free from noises. All these techniques and algorithms have their advantages and limitations which are discussed in this chapter.

scholarly journals ECG Noise Reduction with the Use of the Ant Lion Optimizer Algorithm

2019 ◽  
Vol 9 (4) ◽  
pp. 4525-4529
Author(s):  
K. H. Hii ◽  
V. Narayanamurthy ◽  
F. Samsuri
Keyword(s):  
Finite Impulse Response ◽  
Median Filter ◽  
Cutoff Frequency ◽  
Ecg Signal ◽  
Ecg Signals ◽  
Ant Lion Optimizer ◽  
Low Pass ◽  
Ant Lion ◽  
Baseline Wander ◽  
Electrocardiogram Ecg

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove that noise in order to support any decision making for automatic heart disorder diagnosis systems. In this paper, the use of Ant Lion Optimizer (ALO) for optimizing and identifying the cutoff frequency of the ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed by a moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent ALO-based system successfully denoised the ECG signals more effectively than the conventional method. The accuracy percentage increased by 2%.

scholarly journals Classification of Arrhythmia Conditions using Neural Networks

2020 ◽  
Vol 9 (8) ◽  
pp. 421-424
Keyword(s):  
Electrical Activity ◽  
Cardiac Muscle ◽  
Human Heart ◽  
Graphical Form ◽  
Ecg Signal ◽  
Healthy Human ◽  
Signal Change ◽  
Heart Functions ◽  
Electrocardiogram Ecg

In this paper, we are discussing about a heart disease called Arrhythmia and how it can be identified using the Electrocardiogram. Electrocardiogram (ECG) is a graphical form for electrical activity of cardiac muscle. A healthy human heart beats, 72 times per minute under normal conditions. For every heartbeat the cardiac muscle undergoes specific electrical activity which identifies the pattern in the ECG signal. It consists of PQRST wave which represents heart functions. The patterns of the ECG signal change due to the abnormalities in the heartbeat. The abnormality in the ECG is called Arrhythmia.

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