ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM

Raj, Sandeep; Ray, Kailash Chandra

doi:10.1109/tim.2016.2642758

Cited by 201 publications

(64 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally, for signal classification, more than one signal is required, because every movement is originated from different parts of the muscle and depends on a number of different muscles; therefore, the use of different channels helps to extract as much information as possible from the action(s) performed by the muscle(s). Among the various studies that have been done, it is common to work with four [1,9,13,23,29,38,39], six [19,40,41], or eight [2,7,11,22,30] channels for the acquisition of the signal; some research papers even work with a smaller number of channels [26,42]. Table 3 depicts an abridgement of the number of channels used by different studies and Table 4 summarizes the electrode type used and the place of electrode placement body.…”

Section: Referencementioning

confidence: 99%

See 1 more Smart Citation

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

View full text Add to dashboard Cite

This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

show abstract

Section: Referencementioning

confidence: 99%

“…Raj and Ray [42] differentiated arrhythmias using PCA to reduce features in time-frequency space. These features were obtained from ECG by means of the Discrete Orthonormal Stockwell Transform (DOST) concatenated with morphological features.…”

Section: Other Applications Of Svm-based Classifiersmentioning

confidence: 99%

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

View full text Add to dashboard Cite

show abstract

“…ECG signal processing techniques for real time analysis are implemented in Raj at al. [18] and Varatharajan et al [19], They use the Support Vector Machine algorithm for pattern recognition. These methods can be used for screening and pathological classifications, as well as a weighted kernel to identify Q, R, and S waves at the ECG signal input to classify the pulse level.…”

Section: Related Workmentioning

confidence: 99%

Mobile Device ECG Classification using quantized Neural Networks

Santos

Almeida

Costa

et al. 2020

Preprint

View full text Add to dashboard Cite

In this paper, a novel method for classifying electrocardiogram signals in mobile devices is proposed, which classifies different arrhythmias according to the Association for the Advancement of Medical Instrumentation standard EC57. A convolutional neural network has been constructed, trained and validated with the MIT-BIH Arrhythmia Dataset, which has 5 different classes: normal beat, supraventricular premature beat, premature ventricular contraction, fusion of ventricular and normal beat, unclassifiable beat. Once trained and validated, the model is subjected to a post-training quantization stage using the TensorFlow Lite conversion method. The obtained results were satisfactory, before and after the quantization, the convolutional neural network obtained an accuracy of 98.5%. With the quantization technique it was possible to obtain a significant reduction in model size, thus enabling the development of the mobile application, this reduction was approximately 90% compared to the original model size.

show abstract

“…Because of the redundant and discrete-time characteristics of ECG and VCG signals, numerous methods with combination of time and frequency domains and nonlinear analysis have been developed to handle the problem [47]. Especially for the nonlinear analysis, nonlinear parameters extracted through different types of entropies [48], Lyapunov exponent [49], local fractal dimension [50], higher order spectra (HOS) cumulants [51], recurrence quantification analysis (RQA) [52] and Hurst exponent [53], have been employed for automatic detection of abnormal ECG or VCG signals.…”

Section: Introductionmentioning

confidence: 99%

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

Zeng

Lin

Yuan

2020

Preprint

View full text Add to dashboard Cite

Nowadays cardiovascular diseases ( CVD ) is one of the prime causes of human mortality, which has received tremendous and elaborative research interests regarding the prevention of CVD . Myocardial ischemia is a kind of CVD which will lead to myocardial infarction (MI). The diagnostic criterion of MI is supplemented with clinical judgment and several electrocardiographic (ECG) or vectorcardiographic ( VCG ) programs. However the visual inspection of ECG or VCG signals by cardiologists is tedious, laborious and subjective. To overcome such disadvantages, numerous MI detection techniques including signal processing and artificial intelligence tools have been developed. In this study we propose a novel technique for automatic detection of MI based on disparity of cardiac system dynamics and synthesis of the standard 12-lead and Frank XYZ leads. First, 12-lead ECG signals are reduced to 3-dimensional VCG signals, which are synthesized with Frank XYZ leads to build a hybrid 4-dimensional cardiac vector. This vector is decomposed into a series of proper rotation components ( PRCs ) by using the intrinsic time-scale decomposition ( ITD ) method. Second, four levels discrete wavelet transform ( DWT ) is employed to decompose the predominant PRCs into different frequency bands, in which third-order Daubechies ( db3 ) wavelet function is selected as reference variable for analysis. Third, phase space of the reference variable is reconstructed based on db3 , in which the properties associated with the nonlinear cardiac system dynamics are preserved. Three-dimensional ( 3D ) phase space reconstruction ( PSR ) together with Euclidean distance (ED) has been utilized to derive features. Fourth, neural networks are then used to model, identify and classify cardiac system dynamics between normal (healthy) and MI cardiac vector signals. Finally, experiments are carried out on the PhysioNet PTB database to assess the effectiveness of the proposed method, in which conventional 12-lead and Frank XYZ leads ECG signal fragments from 148 patients with MI and 52 healthy controls were extracted. By using the 10-fold cross-validation style, the achieved average classification accuracy is reported to be 98.20 % . The result verifies the effectiveness of the proposed method which can serve as a potential candidate for the automatic detection of MI in the clinical application.

show abstract

ECG Signal Analysis Using DCT-Based DOST and PSO Optimized SVM

Cited by 201 publications

References 29 publications

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Mobile Device ECG Classification using quantized Neural Networks

Myocardial infarction detection using artificial intelligence and nonlinear features based on synthesis of the standard 12-lead and Frank XYZ leads

Contact Info

Product

Resources

About