Hybrid SVM for Multiclass Arrhythmia Classification
01 Nov 2009-pp 287-290
TL;DR: This paper proposes a Holder-SVM detection algorithm using a novel hybrid arrangement of binary and multiclass SVMs designed to take care of class imbalance rampant in biomedical signals, and significantly reduces the number of false negatives.
Abstract: Automatically classifying ECG recordings for Malignant Ventricular Arrhythmia is fraught with several difficulties. Even normal ECG signals exhibit only quasi-periodic nature, and contain various irregularities. The key to more accurate detection is the use of position, and amount of local singularities in the signals.In this paper, we propose a Holder-SVM detection algorithm using a novel hybrid arrangement of binary and multiclass SVMs designed to take care of class imbalance rampant in biomedical signals. As a result, we significantly reduce the number of false negatives – patients falsely classified as normal. We used the MIT-BIH Arrhythmia database for even different arrhythmias. We compare our hybrid SVM with a suitable conventional SVM, and show better results.We also use the new arrangement for features proposed earlier, and demonstrate the gain in accuracy. Our concept of hybrid SVM is applicable to a wide variety of multiclass classification problems.
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TL;DR: A comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes, which showed high accuracy in correct classification of Atrial Fibrillation, Supraventricular ECTopic Beats, and Ventricular Ectopic Beats using the GRU, CNN, and LSTM, respectively.
Abstract: Deep Learning (DL) has recently become a topic of study in different applications including healthcare, in which timely detection of anomalies on Electrocardiogram (ECG) can play a vital role in patient monitoring. This paper presents a comprehensive review study on the recent DL methods applied to the ECG signal for the classification purposes. This study considers various types of the DL methods such as Convolutional Neural Network (CNN), Deep Belief Network (DBN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). From the 75 studies reported within 2017 and 2018, CNN is dominantly observed as the suitable technique for feature extraction, seen in 52% of the studies. DL methods showed high accuracy in correct classification of Atrial Fibrillation (AF) (100%), Supraventricular Ectopic Beats (SVEB) (99.8%), and Ventricular Ectopic Beats (VEB) (99.7%) using the GRU/LSTM, CNN, and LSTM, respectively.
211 citations
Cites background from "Hybrid SVM for Multiclass Arrhythmi..."
...Finding a generalized framework along with the 30 related standards to be functional for general population is problematic (Ceylan & Özbay, 2007; Joshi et al., 2009)....
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TL;DR: It is predicted that large-scale data-driven analytics could lead to huge benefits in health care; in the United States, where healthcare spending is 18% of gross domestic product, up to US$600 per person could be saved annually.
Abstract: W ith the digitization of all records and processes, and prevalence of cloud-driven services and Internet of Things, today’s era can truly be considered as an era of data. Machine learning (ML) and artificial intelligence (AI) skills are among the most sought-after skills today. McKinsey Global Institute research suggests that 45% of workplace activities in corporations could be automated with current technologies; 80% of that is attributable to existing ML capabilities, and breakthroughs in natural language processing could further the impact. Gartner forecasts that large-scale data-driven analytics could lead to huge benefits in health care; in the United States, where healthcare spending is 18% of gross domestic product, up to US$600 per person could be saved annually. Gartner also forecasts that data-driven insights for demand-supply matching could create an economic impact of $850 billion to $2.5 trillion. International Data Corporation forecasts that spending on AI and ML will grow to $79.2 billion by 2022, with a compound annual growth rate of 38% between the 2018 and 2022 period.
73 citations
TL;DR: The comparison of different techniques in terms of their performance for arrhythmia detection and its suitability for hardware implementation toward body-wearable devices is discussed in this work.
Abstract: Signals obtained from a patient, i.e., bio-signals, are utilized to analyze the health of patient. One such bio-signal of paramount importance is the electrocardiogram (ECG), which represents the functioning of the heart. Any abnormal behavior in the ECG signal is an indicative measure of a malfunctioning of the heart, termed an arrhythmia condition. Due to the involved complexities such as lack of human expertise and high probability to misdiagnose, long-term monitoring based on computer-aided diagnosis (CADiag) is preferred. There exist various CADiag techniques for arrhythmia diagnosis with their own benefits and limitations. In this work, we classify the arrhythmia detection approaches that make use of CADiag based on the utilized technique. A vast number of techniques useful for arrhythmia detection, their performances, the involved complexities, and comparison among different variants of same technique and across different techniques are discussed. The comparison of different techniques in terms of their performance for arrhythmia detection and its suitability for hardware implementation toward body-wearable devices is discussed in this work.
34 citations
Cites background or methods from "Hybrid SVM for Multiclass Arrhythmi..."
...Publication date: March 2019. training data but has reduced performance when tested on different patients (Joshi et al. 2009; Ceylan and Özbay 2007)....
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...Hence, a hybrid method of SVM called the holderSVM detection algorithm is introduced in Joshi et al. (2009), which is designed to take care of the imbalance rampant in bio-signals with a hybrid arrangement of binary and multi-class SVMs. ECG classification is performed as follows: noise patterns…...
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...This is one of the reasons why CADiag arrhythmia detection systems perform well on the training data, but has reduced performance when tested on different patients [Joshi et al. 2009; Ceylan and Özbay 2007]....
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01 Nov 2015
TL;DR: This paper proposes ECG signal analysis and classification method using wavelet energy histogram method and support vector machine (SVM), which performs well with an average sensitivity of 100, specificity of 99, positive prediction of 99%, false prediction of 0.0033, and average classification rate of 99.75%.
Abstract: The Electrocardiogram (ECG) is most widely used techniques to detect cardiac diseases. In this paper we propose ECG signal analysis and classification method using wavelet energy histogram method and support vector machine (SVM). The classification of cardiac arrhythmia in the ECG signal consists of three stages including ECG signal preprocessing, feature extraction and heartbeats classification. The discrete wavelet transform is used as preprocessing tool for signal denoising and feature extraction such as R point location, QRS complex detection. Morphological features extracted from the QRS complex are employed as input to the classifier. Binary SVM is used as a classifier to classify the input ECG beat into four classes i.e. Normal, Left bundle branch block, Right bundle branch block and Premature ventricular contraction. MIT-BIH arrhythmia database is used for performance analysis. The proposed classifier performs well with an average sensitivity of 100%, specificity of 99.66%, positive prediction of 99%, false prediction of 0.0033, and average classification rate of 99.75%.
19 citations
TL;DR: In this paper, a thorough overview of the new DL approaches used for classification purposes to the ECG signal is provided, which explores different types of DL techniques, such as ResNet, InceptionV3, Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM).
16 citations
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01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
"Hybrid SVM for Multiclass Arrhythmi..." refers background in this paper
...Please refer to (7), (8), (9) for further details....
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TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Abstract: An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of oversampling the minority (abnormal)cla ss and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space)tha n only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space)t han varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC)and the ROC convex hull strategy.
17,313 citations
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.
15,696 citations
TL;DR: In this article, a method of over-sampling the minority class involves creating synthetic minority class examples, which is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
Abstract: An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of "normal" examples with only a small percentage of "abnormal" or "interesting" examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
11,512 citations
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
Abstract: —The newly inaugurated Research Resource for Complex Physiologic Signals, which was created under the auspices of the National Center for Research Resources of the National Institutes of He...
11,407 citations