Highlighting the Current Issues with Pride Suggestions for Improving the Performance of Real Time Cardiac Health Monitoring

Bashir, Mohamed Ezzeldin A.; Lee, Dong Gyu; Akasha, Makki; Yi, Gyeong Min; Cha, Eun‐Jong; Bae, Jang‐Whan; Cho, Myeong Chan; Ryu, Keun Ho

doi:10.1007/978-3-642-15020-3_21

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…Since the frequency content of the QRS complex falls below half of this sampling frequency, the Shannon-Nyquist sampling theorem tells us that the signal can be reconstructed for all time by bandlimited interpolation (Dodson, 1992;Proakis and Monolakis, 1996;Bashir et al, 2010). Therefore, the interpolation in step 1 is performed by bandlimited interpolation.…”

Section: Obtaining the True Rr Seriesmentioning

confidence: 99%

Investigation of the Minimum Conditions for Reliable Estimation of Clinically Relevant HRV Measures - Introducing a Novel Approach to the Validation of HRV Measurement Systems

Ahrens

Sørensen

Langberg

et al. 2015

Proceedings of the 3rd International Congress on Cardiovascular Technologies

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The R-peak localization error (jitter) of a heart rate variability (HRV) system has a great impact on the values of the HRV measures. Only a few studies have analyzed this subject and purely done so from the aspect of choice of sampling frequency. In this study we provide an overview of the various factors that comprise the jitter of a system. We propose a method inspired by the field of signal averaged electrocardiography (SAECG) that allows for a quantification of the jitter of any HRV system that records and stores the raw ECG signal. Furthermore, with this method the differences between the HRV measures of the system and HRV measures corresponding to the physiological truth can be quantified. The method is used to obtain the physiologically true R-peak locations of subjects from Physionet's 'Normal Sinus Rhythm Database'. The effects of jitter are then analyzed via mathematical modelling for short-term and long-term HRV for various HRV measures. The effects of abnormal beats and missed and false detections are analyzed as well.

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Section: Obtaining the True Rr Seriesmentioning

confidence: 99%

Investigation of the Minimum Conditions for Reliable Estimation of Clinically Relevant HRV Measures - Introducing a Novel Approach to the Validation of HRV Measurement Systems

Ahrens

Sørensen

Langberg

et al. 2015

Proceedings of the 3rd International Congress on Cardiovascular Technologies

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“…(2) ECG signal classi cation model based on DL. We can identify symptoms of cardiac disease processes (detect abnormal heart rhythm or cardiac abnormalities) by examining changes in normal ECG signals [2][12] [13]. The ECG of normal hearts has a characteristic shape.…”

Section: Introductionmentioning

confidence: 99%

Electrocardiogram Signal Classification Based on Deep Learning Techniques

Ahmed¹

2023

Preprint

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One of the most often used diagnostic tools in medicine and healthcare is the electrocardiogram (ECG). When it comes to healthcare prediction problems requiring ECG data, deep learning techniques seem promising. This paper aims to apply deep learning techniques to classify MIT-BIH arrhythmias on publicly available datasets. A new electrocardiogram classification for employing a spectrogram of signals algorithm is proposed. The proposed model depends on convolutional neural networks to automatically learn the characteristics of features and has used convolutional neural networks to detect normal and abnormal ECG heartbeats, with an average detection accuracy of 99.22%.

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“…Early and accurate detection of ECG arrhythmia helps doctors to detect various heart diseases. ECG beat classification is a challenging problem due to following reasons (Singh et al, 2012;Bashir et al, 2010;Jambukia et al, 2015): variability in normal ECG waveform of each person, dissimilar signs for one disease on different patients' ECG waveform, two distinct diseases may have approximately similar effects on different patients' ECG waveform, lack of standardisation of ECG features, and non-existence of optimal classification rules for ECG beat classification.…”

Section: Introductionmentioning

confidence: 99%

ECG beat classification using machine learning techniques

Jambukia

Dabhi

Prajapati

2018

IJBET

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An arrhythmia is an abnormality in the heart rhythm, or heartbeat pattern. ECG beats can be classified into six different arrhythmia beat types (left bundle branch block, right bundle branch block, paced beats, premature ventricular contradiction, atrial premature beats, and normal rhythm). Early and accurate detection of arrhythmia types is important in detecting heart diseases and choosing appropriate treatment for a patient. This paper proposes a combination of Particle Swarm Optimisation (PSO) and Feed Forward Neural Network (FFNN) for ECG beat classification. We have used MIT-BIH arrhythmia database for data collection and prepared three different datasets. Features, such as R peak sample number and QRS complex, are extracted using Pan-Tompkins algorithm. The extracted features are used as inputs to three different classifiers: Multi-Layer Perceptron Neural Network (MLPNN), Support Vector Machine (SVM), and PSO-FFNN. Results show high classification accuracy of over 97% with either of these three classifiers. The performance comparison of these classifiers is carried out using three measures: sensitivity, specificity, and accuracy. The results suggest that PSO-FFNN shows slightly better performance than MLPNN and SVM in terms of accuracy on all datasets.

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Highlighting the Current Issues with Pride Suggestions for Improving the Performance of Real Time Cardiac Health Monitoring

Cited by 14 publications

References 13 publications

Investigation of the Minimum Conditions for Reliable Estimation of Clinically Relevant HRV Measures - Introducing a Novel Approach to the Validation of HRV Measurement Systems

Investigation of the Minimum Conditions for Reliable Estimation of Clinically Relevant HRV Measures - Introducing a Novel Approach to the Validation of HRV Measurement Systems

Electrocardiogram Signal Classification Based on Deep Learning Techniques

ECG beat classification using machine learning techniques

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