Classification of Electrocardiogram Signals with RS and Quantum Neural Networks

Tang, Xiao; Shu, Liang

doi:10.14257/ijmue.2014.9.2.37

Cited by 63 publications

(20 citation statements)

References 22 publications

(24 reference statements)

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“…The fiducial-based methods extract time domain characteristic, amplitude, area, and angle from an ECG signal. An example of feature points that can be extracted are such as the amplitudes of the P, R, and T waves, the temporal distance between wave boundaries (onset and offset of the P, Q, R, S, and T waves), the area of the waves, and the slope information [11]. The non fiducial-based methods do not use the characteristic point as features, instead it dwells on features like wavelet coefficients and power spectral density.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

TuerxunWaili

Nor

Rahman

et al. 2016

Recent Advances in Information and Communication Technology 2016

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This paper presents a Multilayer Perception Neural Network developed to identify human subjects using electrocardiogram (ECG) signals. We use the amplitude values of Q, R and S as a features for our experiments. In this study, a total of 87 dataset were collected among 14 subjects from the Physikalisch-Technische Bundesanstalt (PTB) database. Out of the 14 subjects, Q-R-S feature points were taken from different day and time sessions to perform classification with MLP. Out of this data, 66 % is used as training dataset while the remaining 34 % is used for testing. Our method yields 96 % accuracy and demonstrates that the use of three fiducial points is sufficient to identify a subject despite the common practice of taking more feature points.

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Section: Introductionmentioning

confidence: 99%

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

TuerxunWaili

Nor

Rahman

et al. 2016

Recent Advances in Information and Communication Technology 2016

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show abstract

“…The dataset is taken from Physionet [19,23] and the proposed technique is evaluated using Machine learning classifier Naïve Bayes algorithm in Rapid Miner software package. The source of the ECGs of MIT-BIH Arrhythmia was obtained by the Beth Israel Hospital Arrhythmia Laboratory.…”

Section: Resultsmentioning

confidence: 99%

“…Eduardo José da S. Luz [16] proposed ECG classification based on supervised graph based pattern recognition technique optimum-path forest classifier, compared the performance with three well known experts -SVM, Bayes classifier and Multilayer artificial Neural Network. Tang [17,18] has proposed ECG signal classification based on RS and Quantum neural Networks and compared the performance against Back Propagation (BP) and RBF for the selected ECG signals from MIT/BIH database.…”

Section: Introductionmentioning

confidence: 99%

Naïve Bayes Classifier for ECG Abnormalities Using Multivariate Maximal Time Series Motif

Padmavathi¹,

Ramanujam²

2015

Procedia Computer Science

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Analyze or diagnose of Cardiovascular activity under abnormal heart beat is extremely an intricate and vital job to the medical experts, made more complicated to a novice persons. Electrocardiogram is a way to measure or diagnose for research on human beings to spot heart disease by abnormal heart rhythms. These streaming medical signals can be well analyzed or diagnosed only with the prior knowledge. This paper proposes the methodology; Multivariate Maximal Time Series Motif with Naïve Bayes Classifier to classify the ECG abnormalities. The proposed model of predicting Time Series Motif is evaluated with the dataset contains the collection of ECG signals of patients recorded using Holter Monitor. The efficiency of the proposed work is proved by comparing the precision of existing with various Feature extraction Techniques.

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“…Tang and L. Shu [18] proposed classifier based on QNN (Quantum Neural Network). Quantum Neural Network (QNN) is a youthful and energetic science built upon the combination of quantum computing and artificial neural network.…”

Section: Abnormalitiesmentioning

confidence: 99%

Ecg Signal Analysis

Malge¹,

Vanjerkhede²,

Bhyri³

2015

Icrbit - 2015

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Abstract-Accurate analysis of ECG signal is of utmost importance as the amplitudes and intervals value of ECG provide information about proper functioning of heart of every human. Therefore there have been numerous researches going on for analysis of ECG signal. In this paper we discuss about various methods that are used to extract features of ECG signal and further classify them into various disorders based on the features extracted. We also discuss about various preprocessing methods to remove base line wander and other contaminants from the ECG signal. Most researchers take input signal from MIT-BIH Data base. Performance of preprocessing is measured using Signal to Noise Ratio (SNR) and performance of feature extraction methods and classification is measured using Sensitivity and Positive Predictivity.keywords-ECG signal, MIT-BIH Data base, feature extraction, abnormalities.

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Classification of Electrocardiogram Signals with RS and Quantum Neural Networks

Cited by 63 publications

References 22 publications

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

Electrocardiogram Identification: Use a Simple Set of Features in QRS Complex to Identify Individuals

Naïve Bayes Classifier for ECG Abnormalities Using Multivariate Maximal Time Series Motif

Ecg Signal Analysis

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