Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

Asl, Babak Mohammadzadeh; Setarehdan, Seyed Kamaledin; Mohebbi, Maryam

doi:10.1016/j.artmed.2008.04.007

Cited by 312 publications

(133 citation statements)

References 35 publications

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“…LS-SVM is a well-known classifier and is used in the classification of many biomedical signals, like electrocardiograph (ECG) signals [70], heart rate variability signals [71], cardiac sound signals [72], brain MRI classification [73], EEG signals [74,75], etc. In this work, the classification performance of the LS-SVM classifier is evaluated by employing the ten-fold cross-validation procedure.…”

Section: Discussionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

283

175

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The brain is a complex structure made up of interconnected neurons, and its electrical activities can be evaluated using electroencephalogram (EEG) signals. The characteristics of the brain area affected by partial epilepsy can be studied using focal and non-focal EEG signals. In this work, a method for the classification of focal and non-focal EEG signals is presented using entropy measures. These entropy measures can be useful in assessing the nonlinear interrelation and complexity of focal and non-focal EEG signals. These EEG signals are first decomposed using the empirical mode decomposition (EMD) method to extract intrinsic mode functions (IMFs). The entropy features, namely, average Shannon entropy (ShEn Avg ), average Renyi's entropy (RenEn Avg ), average approximate entropy (ApEn Avg ), average sample entropy (SpEn Avg ) and average phase entropies (S1 Avg and S2 Avg ), are computed from different IMFs of focal and non-focal EEG signals. These entropies are used as the input feature set for the least squares support vector machine (LS-SVM) classifier to classify into focal and non-focal EEG signals. Experimental results show that our proposed method is able to differentiate the focal and non-focal EEG signals with an average classification accuracy of 87% correct.

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Section: Discussionmentioning

confidence: 99%

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Sharma

Pachori

Acharya

2015

Entropy

283

175

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“…However, machine-learning techniques have been used extensively in medicine, 21 in gene expression studies, [22][23][24] for classification of cardiac arrhythmias, 25 for predicting morbidity after coronary artery bypass surgery, 26 and for predicting when weaning from ventilator support should begin. 27 Gaussian processes have been applied in adults with ALI to model the pressure-volume curve to titrate PEEP.…”

Section: Discussionmentioning

confidence: 99%

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

et al. 2013

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BACKGROUND: Ventilator management for children with hypoxemic respiratory failure may benefit from ventilator protocols, which rely on blood gases. Accurate noninvasive estimates for pH or P aCO 2 could allow frequent ventilator changes to optimize lung-protective ventilation strategies. If these models are highly accurate, they can facilitate the development of closed-loop ventilator systems. We sought to develop and test algorithms for estimating pH and P aCO 2 from measures of ventilator support, pulse oximetry, and end-tidal carbon dioxide pressure (P ETCO 2 ). We also sought to determine whether surrogates for changes in dead space can improve prediction. METHODS: Algorithms were developed and tested using 2 data sets from previously published investigations. A baseline model estimated pH and P aCO 2 from P ETCO 2 using the previously observed relationship between P ETCO 2 and P aCO 2 or pH (using the Henderson-Hasselbalch equation). We developed a multivariate gaussian process (MGP) model incorporating other available noninvasive measurements. RESULTS: The training data set had 2,386 observations from 274 children, and the testing data set had 658 observations from 83 children. The baseline model predicted P aCO 2 within ؎ 7 mm Hg of the observed P aCO 2 80% of the time. The MGP model improved this to ؎ 6 mm Hg. When the MGP model predicted P aCO 2 between 35 and 60 mm Hg, the 80% prediction interval narrowed to ؎ 5 mm Hg. The baseline model predicted pH within ؎ 0.07 of the observed pH 80% of the time. The MGP model improved this to ؎ 0.05. CONCLUSIONS: We have demonstrated a conceptual first step for predictive models that estimate pH and P aCO 2 to facilitate clinical decision making for children with lung injury. These models may have some applicability when incorporated in ventilator protocols to encourage practitioners to maintain permissive hypercapnia when using high ventilator support. Refinement with additional data may improve model accuracy.

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“…The hybrid model constructed uses intelligent medical information system with decision making support system of Atrial Fibrillation, different knowledge based systems of Atrial Fibrillation prepared in an expert system with ar-tificial intelligence techniques such as neural networks and other technologies used by Knowledge Engineers. The future extension of this paper includes developing some new trends, dependencies of the medical information data for Atrial Fibrillation [22,23]. This will deal with assumptions on performance of various other data mining techniques integrated with knowledge driven approach applied in mathematical formulation.…”

Section: Resultsmentioning

confidence: 99%

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

Kaur¹,

Alam²

2013

AJIBM

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In this paper, we are describing role of knowledge engineering in the development of a hybrid knowledge based medical information system. Knowledge engineering plays important role in development of various technologies such as: expert systems, neural network, artificial intelligence, hybrid intelligent systems, data mining, decision support systems, and knowledge based systems etc. The hybrid medical information system mainly consists of medical information system and medical knowledge base systems. These poly techniques of knowledge engineering when integrated with hybrid techniques of intelligent systems for designing, implementing knowledge bases to deal with medical informational data of Atrial Fibrillation. Atrial Fibrillation is the most common heart rhythm disorder which increases the risk of mortality and morbidity.

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Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

Cited by 312 publications

References 35 publications

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

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Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal

Cited by 312 publications

References 35 publications

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Application of Entropy Measures on Intrinsic Mode Functions for the Automated Identification of Focal Electroencephalogram Signals

Algorithms to Estimate PaCO2and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Role of Knowledge Engineering in the Development of a Hybrid Knowledge Based Medical Information System for Atrial Fibrillation

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Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure