Classifying multichannel ECG patterns with an adaptive neural network

Barro, Senén; Fernández-Delgado, Manuel; Sobrino, Xosé Antón Vila; Regueiro, Carlos V.; Sánchez, Eduardo

doi:10.1109/51.646221

Cited by 64 publications

(13 citation statements)

References 14 publications

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“…Modern diagnostic systems are a compromise between algorithms that require significant computational costs to achieve high accuracy of diagnostic, and hardware with limited performance. It has been established that the accuracy of arrhythmia recognition in existing solutions does not often exceed 80% [2,5,[10][11][12][13]17,20,25,38].…”

Section: Analysis Of Automatic Arrhythmia Diagnosis Methodsmentioning

confidence: 99%

Neuro-Fuzzy Model for Arrhythmia Diagnostic System

Nikonov

Vulfin

Gayanova³

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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Сardiovascular diseases are one of the leading causes of death worldwide. People suffering from or at high risk of such diseases need constant supervision, early diagnosis and timely assistance. It is shown that the achievement of high accuracy performance in real-time arrhythmias recognition is associated with significant hardware costs. Detection accuracy of arrhythmias recognition does not exceed 80%. An approach, which is offered to solve the problem of high-precision arrhythmia diagnosis on the basis of electrocardiosignal is based on the data mining methods. Application of such methods is necessary for processing large amounts of data with complex structure of the features. Determination of the arrhythmia type with the use of fuzzy inference tools needs to specify the technique of the original data preprocessing. Feature selection, formalization and coding is considered in this paper. The issue of the knowledge base constructioncoding, generation and selection of the features (database) as well as the construction of the rules base ¬ as the part of the neuro-fuzzy diagnostic system is also considered. The research goal is to improve the intelligent systems of arrhythmia diagnostics on the basis of neural network classifiers by developing a solution explanation subsystem based on neuro-fuzzy models.

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Section: Analysis Of Automatic Arrhythmia Diagnosis Methodsmentioning

confidence: 99%

Neuro-Fuzzy Model for Arrhythmia Diagnostic System

Nikonov

Vulfin

Gayanova³

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

View full text Add to dashboard Cite

show abstract

“…In the field of ECG processing various algorithms have been reported specifically for the QRS complex such: Differentiation algorithms [2] Digital filters [5][6][7][8][9] Neural networks [10][11][12] In fact, most of the presented algorithms have a fundamental problem known as sensitivity to noise [13]. The problem of sensitivity to noise in ECG signal in itself is complex and because of that, in this paper, it is chosen six algorithms for preprocessing the electrocardiography in order to reduce noise before detecting R wave.…”

Section: Fig 1: Sample Ecg From Mit-bih Databasementioning

confidence: 99%

A New Wavelet-based Algorithm for R-peak Detection in ECG and it’s a Comparison with the Currently Existing Algorithms

Dahwah¹,

Leukhin²

2019

IJCA

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The purpose of this paper is to develop a new approach for Rpeak detection in ECG and compare it with the most effective and existing algorithms. The proposed approach is based on DWT and envelope for the first stage of preprocessing and decision or detecting it was achieved by adaptive thresholding. The proposed algorithm is compared with Pan & Tompkins, Savitzky-Golay smoothing filter, Hilbert and wavelet transforms as well as fast Fourier transform algorithm to investigate these techniques of R peak detection and evaluate the wavelet-based algorithm comparing with them. The algorithms are evaluated in the experimental section using ECG signals from the MIT-BIH database. The results of detection algorithms show that the proposed wavelet-based algorithm gets the highest sensitivity by 99.9% with higher reliability compared to other algorithms, also by analyzing the precision of them, it's come to light that FFT improved the highest precision with 99.7%.

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“…This method of obtaining an electrocardiogram-derived respiration (EDR) signal is perhaps the most straightforward to implement; yet as outlined by Boyle et al (2009), it offers similar levels of performance to more complex techniques based on characteristics of beat morphology such as area under the QRS complex (Moody et al, 1985), amplitude of the R-wave (Khaled and Farges, 1992) and amplitude of the R-and S-waves (Mason and Tarassenko, 2001 The electrocardiogram-derived respiration (EDR) algorithm used here thus requires two main steps: detection of the QRS complexes and calculation of the variation in the R-R intervals between these QRS complexes. QRS detection is a well-established research area with popular algorithms based upon the derivative of the ECG signal (Pan and Tompkins, 1985), wavelets (Afonso et al, 1999), neural networks (Barro et al, 1998) amongst others. In this work, the wavelet-based QRS detection algorithm by Afonso et al (1999) was utilised using the Biosig toolbox in Matlab (Vidaurre et al, 2011).…”

Section: Electrocardiogram-derived Respiration Signalmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Classifying multichannel ECG patterns with an adaptive neural network

Cited by 64 publications

References 14 publications

Neuro-Fuzzy Model for Arrhythmia Diagnostic System

Neuro-Fuzzy Model for Arrhythmia Diagnostic System

A New Wavelet-based Algorithm for R-peak Detection in ECG and it’s a Comparison with the Currently Existing Algorithms

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

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