Prediction of Sudden Cardiac Death using support vector machine

Sheela, C. Jenefar; Vanitha, L.

doi:10.1109/iccpct.2014.7054771

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…The first classifier tested was the linear classifier, which utilized linear discriminant analysis. The best accuracy was yielded at 8 min prior to SCA onset and was found to correlate with literature findings [33,34]. However, the values obtained in this study were slightly lower than the values obtained in similarly-conducted studies (72.80% vs. 74.36%) [33].…”

Section: Discussioncontrasting

confidence: 45%

“…SVM classification was then conducted on the dataset and there was an improvement in the accuracy values obtained for both the linear and non-linear SVM classifiers, both of which yielding higher accuracy rates compared to the linear classifier (72.8% vs. 78.9% both obtained at the 8 min prior time-frame). The values obtained from the non-linear SVM classifier were found to correlate with findings obtained from a similar study conducted using the same two ECG database, although the findings obtained for accuracy (83.9% vs. 88.0%), sensitivity (91.5% vs. 92.0%), and specificity (82.5% vs. 84.0%) were slightly lower compared to the literature [34]. This slight variation in values may be due to interpatient variation, as in the literature only five patients each were used for the normal and SCA cohort, whereas the cohort numbers in this study were almost double for both the normal and SCA population.…”

Section: Discussionmentioning

confidence: 42%

“…Thus, more frequent PVCs at 2 min prior would result in larger compensatory pauses which, in turn, affects the HRV features of patients at risk of SCA, resulting in higher accuracies in classifying SCA and normal patients. A similar conclusion was made across various similarly conducted studies which also discovered that the 2-min time window provided the most useful information for SCA detection [19,[33][34][35]. However, it is important to note that the overall aim of SCA detection is to identify patients at risk as early as possible in order to prevent serious repercussions, such as irreversible neurological damage, and even death.…”

Section: Discussionmentioning

confidence: 61%

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Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers

2017

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Featured Application: This study explores the potential use of supervised machine learning for the early detection of sudden cardiac arrest using electrocardiographic markers, such as heart rate, R-R interval duration, and heart rate variability.Abstract: Early detection of sudden cardiac arrest (SCA) is critical to prevent serious repercussion such as irreversible neurological damage and death. Currently, the most effective method involves analyzing electrocardiogram (ECG) features obtained during ventricular fibrillation. In this study, data from 10 normal patients and 10 SCA patients obtained from Physiobank were used to statistically compare features, such as heart rate, R-R interval duration, and heart rate variability (HRV) features from which the HRV features were then selected for classification via linear discriminant analysis (LDA) and linear and fine Gaussian support vector machines (SVM) in order to determine the ideal time-frame in which SCA can be accurately detected. The best accuracy was obtained at 2 and 8 min prior to SCA onset across all three classifiers. However, accuracy rates of 75-80% were also obtained at time-frames as early as 50 and 40 min prior to SCA onset. These results are clinically important in the field of SCA, as early detection improves overall patient survival.

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Section: Discussioncontrasting

confidence: 45%

Section: Discussionmentioning

confidence: 42%

Section: Discussionmentioning

confidence: 61%

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Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers

2017

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“…ECG has been recognized equally effective in predicting the SCD event as compared to the other invasive techniques [5]. A few studies of ECG-based SCD detection have demonstrated the possibility of SCD to be detected as early as 30 minutes or 1 hour before it happens, using heart rate variability (HRV) as its main feature [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

et al. 2019

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Heart disease remains the main leading cause of death globally and around 50% of the patients died due to sudden cardiac death (SCD). Early detection and prediction of SCD have become an important topic of research and it is crucial for cardiac patient’s survival. Electrocardiography (ECG) has always been the first screening method for patient with cardiac complaints and it is proven as an important predictor of SCD. ECG parameters such as RR interval, QT duration, QRS complex curve, J-point elevation and T-wave alternan are found effective in differentiating normal and SCD subjects. The objectives of this paper are to give an overview of SCD and to analyze multiple important ECG-based SCD detection and prediction models in terms of processing techniques and performance wise. Detail discussions are made in four major stages of the models developed including ECG data, signal pre-processing and processing techniques as well as classification methods. Heart rate variability (HRV) is found as an important SCD predictor as it is widely used in detecting or predicting SCD. Studies showed the possibility of SCD to be detected as early as one hour prior to the event using linear and non-linear features of HRV. Currently, up to 3 hours of analysis has been carried out. However, the best prediction models are only able to detect SCD at 6 minutes before the event with acceptable accuracy of 92.77%. A few arguments and recommendation in terms of data preparation, processing and classification techniques, as well as utilizing photoplethysmography with ECG are pointed out in this paper so that future analysis can be done with better accuracy of SCD detection accuracy.

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“…Sheela et.al [6] have used HRV signals and support vector machines for predicting SCA by classifying subjects as normal or abnormal. They obtained an accuracy of 88%.…”

Section: Introductionmentioning

confidence: 99%

Application of Empirical Mode Decomposition And Support Vector Machine Classifiers for Prediction of Sudden Cardiac Arrest in Susceptible Cases

D.¹,

Venkatesh²

2016

IJERT

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One of the primary causes of Sudden CardiacArrest (SCA) is Ventricular Fibrillation (V.F.). It is a serious arrhythmic condition wherein the heart beats chaotically rather than pumping blood. It is essential for the subject to get instant medical attention when ventricular fibrillation occurs. In this paper, a novel approach is presented where the Hilbert Huang Transform is used for prediction of such an event. The ECG signals are pre-processed for removal of noises, artifacts. The empirical mode decomposition (EMD), which is a part of Hilbert Huang Transform is applied to the de-noised ECG signal. Three features were extracted from the first IMF (Intrinsic Mode Function) obtained after EMD, viz; spectral entropy, lyapunov exponent and weighted frequency. Support Vector Machine (SVM) classifier was used to classify and predict the normal people and patients prone to sudden cardiac arrest, based on the extracted features from both the cases. By applying this technique, the results show that average classification accuracy of 95% was achieved in predicting an unknown signal, labelled as 'Normal' or 'Abnormal ECG signal'. A second SVM classifier was used for predicting the occurrence of V.F. in ECG signals prone to cardiac arrest within 15 minutes, which resulted in an accuracy of 55.5 %.

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Prediction of Sudden Cardiac Death using support vector machine

Cited by 15 publications

References 13 publications

Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers

Computational Algorithms Underlying the Time-Based Detection of Sudden Cardiac Arrest via Electrocardiographic Markers

ECG-based Detection and Prediction Models of Sudden Cardiac Death: Current Performances and New Perspectives on Signal Processing Techniques

Application of Empirical Mode Decomposition And Support Vector Machine Classifiers for Prediction of Sudden Cardiac Arrest in Susceptible Cases

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