Implementation of Neural Network and Feature Extraction to Classify ECG Signals
01 Jan 2019-pp 317-326
TL;DR: In this article, Pan Tompkins algorithm is used for feature extraction on electrocardiography (ECG) signals, while neural networks help in detection and classification of the signal into four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmmia and Long-Term Atrial Fibrillation (AF) and normal heart beat.
Abstract: This paper presents an efficient approach for distinguishing ECG signals based on certain diseases by implementing Pan Tompkins algorithm and neural networks. Pan Tompkins algorithm is used for feature extraction on electrocardiography (ECG) signals, while neural networks help in detection and classification of the signal into four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long-Term Atrial Fibrillation (AF) and normal heart beat. The paper also presents a new approach towards signal classification using the existing neural networks classifiers.
Citations
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TL;DR: In this paper, an improved independent component analysis (ICA) algorithm is used to extract pure ECG components from the ECG mixtures before the signals are applied to machine learning classifiers for accurate detection and classification of ECG signals.
Abstract: Electrocardiogram (ECG) analysis is a conventional way of finding heart abnormality. It is a clinical procedure in which the electrical activity of the heart is measured during every cardiac cycle and checked for healthiness of the heart. It is approximated in this industrialized world that millions of people expire every 12 months because of various coronary heart diseases and short of prompt detection of uncharacteristic heart rhythms. To detect these abnormalities promptly, the ECG measures should provide the cardiac signals without any mixtures or other disturbances. Though accurate classification of ECG is a challenging task as it varies with time and also with persons of different ages, it is the need of the hour. In this proposed research work, an improved independent component analysis (ICA) algorithm is used to extract pure ECG components from the ECG mixtures before the signals are applied to machine learning classifiers for accurate detection and classification of ECG signals. These machine learning models are applied after the signals are preprocessed to reduce the dimensionality and the training time. This work also uses deep learning convolution neural network (CNN) model with different optimizers for ECG classification and analysis. Classification performance of these algorithms is improved when classification is done after extracting the features using ICA technique.
5 citations
01 Jan 2022
TL;DR: In this paper , a convolutional neural network (CNN) is used to classify the phonocardiogram (PCG) signals for evaluation, convert it to spectrogram images, and train a CNN model to predict the outcome.
Abstract: Cardiac arrhythmia refers to a group of conditions that causes the heart to beat too slow or too fast. It is one of the major problems of the heart which needs to be diagnosed at the earliest, as it takes more time for doctors to detect and provide medication. We find different types of arrhythmias; for slow heartbeat, it is called bradycardia; for fast heartbeat, it is called tachycardia. During initial stages of cardiac arrhythmia, doctors need to carefully examine the heartbeats precisely from different locations of the body. Manually evaluating these fundamental heart sounds (FHSs) for each and every patient is time consuming. Thus, automating the procedure by using machine learning techniques to classify heart sound recordings would help in overcoming this problem. The objective is to take the phonocardiogram (PCG) signals for evaluation, convert it to spectrogram images, and train a convolutional neural network model to predict the outcome. Then given a new PCG recording, it will be able to classify as normal or abnormal. Hence, the process of detecting arrhythmia is simplified and saves people's lives.
1 citations
TL;DR: Wang et al. as discussed by the authors presented a novel approach for TB diagnosis from X-ray using deep learning methods, which used an ensemble of two pre-trained neural networks, namely EfficientNetB0 and Densenet201, for feature extraction.
Abstract: BACKGROUND
Tuberculosis (TB) is a highly infectious disease that mainly affects the human lungs. The gold standard for TB diagnosis is Xpert Mycobacterium tuberculosis/ resistance to rifampicin (MTB/RIF) testing. X-ray, a relatively inexpensive and widely used imaging modality, can be employed as an alternative for early diagnosis of the disease. Computer-aided techniques can be used to assist radiologists in interpreting X-ray images, which can improve the ease and accuracy of diagnosis.
OBJECTIVE
To develop a computer-aided technique for the diagnosis of TB from X-ray images using deep learning techniques.
METHODS
This research paper presents a novel approach for TB diagnosis from X-ray using deep learning methods. The proposed method uses an ensemble of two pre-trained neural networks, namely EfficientnetB0 and Densenet201, for feature extraction. The features extracted using two CNNs are expected to generate more accurate and representative features than a single CNN. A custom-built artificial neural network (ANN) called PatternNet with two hidden layers is utilized to classify the extracted features.
RESULTS
The effectiveness of the proposed method was assessed on two publicly accessible datasets, namely the Montgomery and Shenzhen datasets. The Montgomery dataset comprises 138 X-ray images, while the Shenzhen dataset has 662 X-ray images. The method was further evaluated after combining both datasets. The method performed exceptionally well on all three datasets, achieving high Area Under the Curve (AUC) scores of 0.9978, 0.9836, and 0.9914, respectively, using a 10-fold cross-validation technique.
CONCLUSION
The experiments performed in this study prove the effectiveness of features extracted using EfficientnetB0 and Densenet201 in combination with PatternNet classifier in the diagnosis of tuberculosis from X-ray images.
01 Jul 2022
TL;DR: In this article , the authors presented preliminary results on the main challenge associated with the detection of premature ventricular contractions (PVCs) identifying common patterns, using a graph-based structure to graphically represent and explore cluster elements in this work.
Abstract: Premature ventricular contractions (PVCs) are abnormal heartbeats that begin in the lower ventricles or pumping chambers and disrupt the normal heart rhythm. The electrocardiogram (ECG) is the most often used tool for detecting abnormalities in the heart's electrical activity. PVCs are very frequent and usually harmless, but they can be extremely harmful in patients with significant heart problems. As a result, appropriate prevention combined with adequate treatment can improve patients' lives. This paper presents preliminary results on the main challenge associated with the detection of PVCs: identifying common patterns. The images used were extrapolated from the MIT-BIH Arrhythmia Database and then pre-processed to remove any signal noise before creating a distance matrix based on the wave distances of each pair of analyzed images. Finally, we clustered the distance into four groups using clustering algorithms such as K-means. We used a graph-based structure to graphically represent and explore cluster elements in this work. Preliminary results suggest the presence of four distinct patterns.
References
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TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
6,686 citations
19 Mar 2015
TL;DR: A survey of ECG classification into arrhythmia types is presented and a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues are presented.
Abstract: Classification of electrocardiogram (ECG) signals plays an important role in diagnoses of heart diseases An accurate ECG classification is a challenging problem This paper presents a survey of ECG classification into arrhythmia types Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient Different classifiers are available for ECG classification Amongst all classifiers, artificial neural networks (ANNs) have become very popular and most widely used for ECG classification This paper discusses the issues involved in ECG classification and presents a detailed survey of preprocessing techniques, ECG databases, feature extraction techniques, ANN based classifiers, and performance measures to address the mentioned issues Furthermore, for each surveyed paper, our paper also presents detailed analysis of input beat selection and output of the classifiers
203 citations
01 Aug 1997
TL;DR: A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper, which makes the fusion of numerical data and linguistic information in the filter possible.
Abstract: A new kind of nonlinear adaptive filter, the adaptive neural fuzzy filter (ANFF), based upon a neural network's learning ability and fuzzy if-then rule structure, is proposed in this paper. The ANFF is inherently a feedforward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. The adaptation here includes the construction of fuzzy if-then rules (structure learning), and the tuning of the free parameters of membership functions (parameter learning). In the structure learning phase, fuzzy rules are found based on the matching of input-output clusters. In the parameter learning phase, a backpropagation-like adaptation algorithm is developed to minimize the output error. There are no hidden nodes (i.e., no membership functions and fuzzy rules) initially, and both the structure learning and parameter learning are performed concurrently as the adaptation proceeds. However, if some linguistic information about the design of the filter is available, such knowledge can be put into the ANFF to form an initial structure with hidden nodes. Two major advantages of the ANFF can thus be seen: 1) a priori knowledge can be incorporated into the ANFF which makes the fusion of numerical data and linguistic information in the filter possible; and 2) no predetermination, like the number of hidden nodes, must be given, since the ANFF can find its optimal structure and parameters automatically.
84 citations
24 Sep 1997
TL;DR: In this paper, it is shown that a better conditioned minimization problem can be obtained if the problem is separated with respect to the linear parameters, which will increase the convergence speed of the minimization.
Abstract: Neural network minimization problems are often ill-conditioned and in this contribution two ways to handle this will be discussed. It is shown that a better conditioned minimization problem can be obtained if the problem is separated with respect to the linear parameters. This will increase the convergence speed of the minimization. The Levenberg-Marquardt minimization method is often concluded to perform better than the Gauss-Newton and the steepest descent methods on neural network minimization problems. The reason for this is investigated and it is shown that the Levenberg-Marquardt method divides the parameters into two subsets. For one subset the convergence is almost quadratic like that of the Gauss-Newton method, and on the other subset the parameters do hardly converge at all. In this way a fast convergence among the important parameters is obtained.
79 citations
23 Jun 2010
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.
Abstract: Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice More recently, artificial neural network techniques theory have been receiving increasing attention The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field
39 citations