Correlation analysis for abnormal ECG signal features extraction

Ramli, Azliana; Ahmad, P.A.

doi:10.1109/nctt.2003.1188342

Cited by 22 publications

(9 citation statements)

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“…ECG beats were classified in normal and abnormal heartbeat patterns from ECG records reported as regular and irregular cardiac rhythm. Lead V1 was chosen for the analysis, because it has the highest ratio of atrial to ventricular signal amplitude and, therefore, offers more representative characteristics for identifying the common heart diseases [ 44 , 45 ]. To avoid overfitting and improve the generalization capability of the NN approach, we added simulated ECG data with artificial corruption, using a Gaussian white-noise model [ 46 ], to generate 110 normal and 72 abnormal virtual ECG tracings.…”

Section: Methodsmentioning

confidence: 99%

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Mena¹,

Felix²,

Ochoa

et al. 2018

Computational and Mathematical Methods in Medicine

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Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.

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Section: Methodsmentioning

confidence: 99%

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Mena¹,

Felix²,

Ochoa

et al. 2018

Computational and Mathematical Methods in Medicine

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show abstract

“…For the testing purpose, we considered an unbalanced dataset in favor of arrhythmia data (76.5%) to improve the testing generalization capabilities of the NN classifier to recognizing cardiac abnormalities. Lead V1 was chosen for the whole analysis; because it has the largest ratio of atrial to ventricular signal amplitude and therefore can offer more representative characteristics for identifying the common heart diseases [37,38]. The final test set consisted of 884 ECG traces built from 4 heartbeats per individual.…”

Section: Datasetmentioning

confidence: 99%

Noise-tolerant modular neural network system for classifying ECG signal

Ochoa¹,

Mena

Felix³

et al. 2019

IJCAI

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Millions of electrocardiograms (ECG) are interpreted every year, requiring specialized training for accurate interpretation. Because automated and accurate classification ECG signals will improve early diagnosis of heart condition, several neural network (NN) approaches have been proposed for classifying ECG signals. Current strategies for a critical step, the preprocessing for noise removal, are still unsatisfactory. We propose a modular NN approach based on artificial noise injection, to improve the generalization capability of the resulting model. The NN classifier initially performed a fairly accurate recognition of four types of cardiac anomalies in simulated ECG signals with minor, moderate, severe, and extreme noise, with an average accuracy of 99.2%, 95.1%, 91.4%, and 85.2% respectively. Ultimately we discriminated normal and abnormal heartbeat patterns for single lead of raw ECG signals, obtained 95.7% of overall accuracy and 99.5% of Precision. Therefore, the propose approach is a useful tool for the detection and diagnosis of cardiac abnormalities.Povzetek: V članku je opisana metoda modularnih nevronskim mrež za prepoznavanje šumnih ECG signalov. Materials and methodsThe NN classification comprises five stages: (i) simulation of ECG signal, (ii) extraction of features that indicate cardiac abnormalities, (iii) computer generation of normal and abnormal heartbeat patterns, (iv) artificial noise injection, and (v) cardiac rhythm classification on simulated and real ECG signals.

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“…Correlation analysis for abnormal ECG signal features extraction was proposed by A. B. Ramli, and P. A. Ahmad [6] for finding important features of ECG signal by using Cross-Correlation signal analysis technique.…”

Section: Literature Surveymentioning

confidence: 99%

“…An important characteristic of Walsh functions is sequence which is determined from the number of zerocrossings per unit time interval. Every Walsh function has a unique sequence value [6]. WHT based transformed ECG signal is shown in Fig.…”

Section: )mentioning

confidence: 99%

Feature extraction of ECG signals for early detection of heart arrhythmia

Akilandeswari¹,

Sathya²

2014

International Journal of Advanced Research in Computer and Comm

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In today's world, irrespective of age, cardiac arrhythmia (heart abnormalities) is common among people, which must be detected and treated at the very earliest stage. Electrocardiograph (ECG) plays a vital role in detecting various cardiac arrhythmias. ECG waveform provides entire information about the electrical activity of the heart. In this paper, we are mainly focusing on the primary attributes P, Q, R, S, T, their segments and intervals of ECG Signals. Walsh-Hadamard transformation (WHT) and Fast Fourier Transformation (FFT) are performed to extract the required features from ECG signal.

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Correlation analysis for abnormal ECG signal features extraction

Cited by 22 publications

References 0 publications

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Noise-tolerant modular neural network system for classifying ECG signal

Feature extraction of ECG signals for early detection of heart arrhythmia

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