Mengurangi Pengaruh Noise Baseline Wander pada Sinyal Electrocardiogram(ECG)

ECG signal is one of the main tools used to make the diagnosis of heart abnormalities and can also be used to determine the steps recovery before a more serious medical treatment. Description and feature extraction just before taking a decision to be the most important in diagnosing patients' heart health. The main steps in the analysis of the ECG signal is to eliminate or reduce the noise from ECG signals using a variety of filtering techniques, detection of cardiac cycle by detecting QRS complex signal, the detection of the main characteristics of the signal to be analyzed and ultimately determine the formula of the characteristic features that have been obtained in the previous section. In this study, the authors will examine the ECG signal by eliminating or reducing noise on the signal ECG wave pattern up and down making it very difficult for the detection and extraction of ECG signals. The current results of testing showed that the ECG signal which initially did not constantly be on the line isolines become ECG signal consistently isolines are on the line so that this condition makes subsequent processing to diagnose cardiac abnormalities in a timely and accurate

Role of independent component analysis in intelligent ECG signal processing

The Electrocardiogram (ECG) reflects the activities and the attributes of the human heart and reveals very important hidden information in its structure. The information is extracted by means of ECG signal analysis to gain insights that are very crucial in explaining and identifying various pathological conditions. The feature extraction process can be accomplished directly by an expert through, visual inspection of ECGs printed on paper or displayed on a screen. However, the complexity and the time taken for the ECG signals to be visually inspected and manually analysed means that it‟s a very tedious task thus yielding limited descriptions. In addition, a manual ECG analysis is always prone to errors: human oversights. Moreover ECG signal processing has become a prevalent and effective tool for research and clinical practices. A typical computer based ECG analysis system includes a signal preprocessing, beats detection and feature extraction stages, followed by classification.Automatic identification of arrhythmias from the ECG is one important biomedical application of pattern recognition. This thesis focuses on ECG signal processing using Independent Component Analysis (ICA), which has received increasing attention as a signal conditioning and feature extraction technique for biomedical application. Long term ECG monitoring is often required to reliably identify the arrhythmia. Motion induced artefacts are particularly common in ambulatory and Holter recordings, which are difficult to remove with conventional filters due to their similarity to the shape of ectopic xiiibeats. Feature selection has always been an important step towards more accurate, reliable and speedy pattern recognition. Better feature spaces are also sought after in ECG pattern recognition applications. Two new algorithms are proposed, developed and validated in this thesis, one for removing non-trivial noises in ECGs using the ICA and the other deploys the ICA extracted features to improve recognition of arrhythmias. Firstly, independent component analysis has been studiedand found effective in this PhD project to separate out motion induced artefacts in ECGs, the independent component corresponding to noise is then removed from the ECG according to kurtosis and correlation measurement.The second algorithm has been developed for ECG feature extraction, in which the independent component analysis has been used to obtain a set of features, or basis functions of the ECG signals generated hypothetically by different parts of the heart during the normal and arrhythmic cardiac cycle. ECGs are then classified based on the basis functions along with other time domain features. The selection of the appropriate feature set for classifier has been found important for better performance and quicker response. Artificial neural networks based pattern recognition engines are used to perform final classification to measure the performance of ICA extracted features and effectiveness of the ICA based artefacts reduction algorithm.The motion artefacts are effectively removed from the ECG signal which is shown by beat detection on noisy and cleaned ECG signals after ICA processing. Using the ICA extracted feature sets classification of ECG arrhythmia into eight classes with fewer independent components and very high classification accuracy is achieved

Design and evaluation of a person-centric heart monitoring system over fog computing infrastructure

Heart disease and stroke are becoming the leading cause of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that helps physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECG have limited accuracy and rely on external resources to analyze the signal and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyze the signal and identify abnormal behavior. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud and (c) the overall power consumption. Based on this concept, the HEART platform is presented that combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate and instant monitoring of the heart. The performance of the system is evaluated concerning the accuracy of detecting abnormal events and the power consumption of the wearable device. Results indicate that a very high percentage of success can be achieved in terms of event detection ratio and the device being operative up to a several days without the need for a recharge

Electrocardiograph (ECG) circuit design and software-based processing using LabVIEW

YesThe efficiency and acquisition of a clean (diagnosable) ECG signal dependent upon the proper selection of electronic components and the techniques used for noise elimination. Given that the human body and the lead cables act as antennas, hence picking up noises from the surroundings, thus a major part in the design of an ECG device is to apply various techniques for noise reduction at the early stage of the transmission and processing of the signal. This paper, therefore, covers the design and development of a Single Chanel 3-Lead Electrocardiograph and a Software-based processing environment. Main design characteristics include reduction of common mode voltages, good protection for the patient, use of the ECG device for both monitoring and automatic extraction (measurements) of the ECG components by the software. The hardware consisted of a lead selection stage for the user to select the bipolar lead for recording, a pre-amplification stage for amplifying the differential potentials while rejecting common mode voltages, an electrical isolation stage from three filtering stages with different bandwidths for noise attenuation, a power line interference reduction stage and a final amplification stage. A program in LabVIEW was developed to further improve the quality of the ECG signal, extract all its features and automatically calculate the main ECG output waveforms. The program had two main sections: The filtering section for removing power line interference, wideband noises and baseline wandering, and the analysis section for automatically extracting and measuring all the features of the ECG in real time. A Front Panel Environment was, therefore, developed for the user interface. The present system produced ECG tracings without the influence of noise/artefacts and provided accurate detection and measurement of all the components of the ECG signal

Electrocardiograph (ECG) circuit design and software-based processing using LabVIEW

The efficiency and acquisition of a clean (diagnosable) ECG signal dependent upon the proper selection of electronic components and the techniques used for noise elimination. Given that the human body and the lead cables act as antennas, hence picking up noises from the surroundings, thus a major part in the design of an ECG device is to apply various techniques for noise reduction at the early stage of the transmission and processing of the signal. This paper, therefore, covers the design and development of a Single Chanel 3-Lead Electrocardiograph and a Software-based processing environment. Main design characteristics include reduction of common mode voltages, good protection for the patient, use of the ECG device for both monitoring and automatic extraction (measurements) of the ECG components by the software. The hardware consisted of a lead selection stage for the user to select the bipolar lead for recording, a pre-amplification stage for amplifying the differential potentials while rejecting common mode voltages, an electrical isolation stage from three filtering stages with different bandwidths for noise attenuation, a power line interference reduction stage and a final amplification stage. A program in LabVIEW was developed to further improve the quality of the ECG signal, extract all its features and automatically calculate the main ECG output waveforms. The program had two main sections: The filtering section for removing power line interference, wideband noises and baseline wandering, and the analysis section for automatically extracting and measuring all the features of the ECG in real time. A Front Panel Environment was, therefore, developed for the user interface. The present system produced ECG tracings without the influence of noise/artefacts and provided accurate detection and measurement of all the components of the ECG signal

Wavelet-Based Kernel Construction for Heart Disease Classification

© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio