An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features

Acharya, U. Rajendra; Fujita, Hamido; Sudarshan, Vidya K.; Sree, Vinitha; Eugene, Lim Wei Jie; Ghista, Dhanjoo N.; Tan, Ru San

doi:10.1016/j.knosys.2015.03.015

Cited by 122 publications

(57 citation statements)

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“…We show that one can predict ventricular tachyarrhythmia with moderate accuracy using modern machine learning algorithms and a combination of features from HRV signals in time, frequency, and nonlinear domains in the same group of patients with heart failure. While there has been research on the prediction of SCD using HRV signals,[13–18] most of these previous studies use HRV signals from healthy subjects as the baseline to predict SCDs in patients with heart disease. Also, while there is a previous study on the prediction of ventricular tachycardia using pre-ventricular-arrhythmic rhythms and control rhythms from the same group of patients, the sample has 41 subjects and 104 recordings.…”

Section: Discussionmentioning

confidence: 99%

“…[12] In particular, HRV signals have been used with machine learning to predict the occurrences of sudden cardiac death (SCD) in the past. [13–18] However, most of these studies compared pre-ventricular-tachyarrhythmia signals from human subjects at risk of SCD with HRV signals from healthy human subjects. While those findings show that there are significant differences between pre-ventricular-arrhythmia HRV signals from patients with heart disease and HRV signals from healthy subjects, we cannot conclude how practical it is to predict ventricular tachyarrhythmia in patients who received ICDs.…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

et al. 2018

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Implantable-cardioverter defibrillators (ICD) detect and terminate life-threatening ventricular tachyarrhythmia with electric shocks after they occur. This puts patients at risk if they are driving or in a situation where they can fall. ICD’s shocks are also very painful and affect a patient’s quality of life. It would be ideal if ICDs can accurately predict the occurrence of ventricular tachyarrhythmia and then issue a warning or provide preventive therapy. Our study explores the use of ICD data to automatically predict ventricular arrhythmia using heart rate variability (HRV). A 5 minute and a 10 second warning system are both developed and compared. The participants for this study consist of 788 patients who were enrolled in the ICD arm of the Sudden Cardiac Death–Heart Failure Trial (SCD-HeFT). Two groups of patient rhythms, regular heart rhythms and pre-ventricular-tachyarrhythmic rhythms, are analyzed and different HRV features are extracted. Machine learning algorithms, including random forests (RF) and support vector machines (SVM), are trained on these features to classify the two groups of rhythms in a subset of the data comprising the training set. These algorithms are then used to classify rhythms in a separate test set. This performance is quantified by the area under the curve (AUC) of the ROC curve. Both RF and SVM methods achieve a mean AUC of 0.81 for 5-minute prediction and mean AUC of 0.87–0.88 for 10-second prediction; an AUC over 0.8 typically warrants further clinical investigation. Our work shows that moderate classification accuracy can be achieved to predict ventricular tachyarrhythmia with machine learning algorithms using HRV features from ICD data. These results provide a realistic view of the practical challenges facing implementation of machine learning algorithms to predict ventricular tachyarrhythmia using HRV data, motivating continued research on improved algorithms and additional features with higher predictive power.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

et al. 2018

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“…Therefore, electrical measurements of the heart, in the form of ECG signals, can provide a holistic assessment of health [103]. However, a fundamental problem with such general indicators of health is the complexity of interpretation.…”

Section: Discussionmentioning

confidence: 99%

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Faust

Acharya

et al. 2016

J. Mech. Med. Biol.

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Humans need sleep. It is important for physical and psychological recreation. During sleep our consciousness is suspended or least altered. Hence, our ability to avoid or react to disturbances is reduced. These disturbances can come from external sources or from disorders within the body. Obstructive Sleep Apnea (OSA) is such a disorder. It is caused by obstruction of the upper airways which causes periods where the breathing ceases. In many cases, periods of reduced breathing, known as hypopnea, precede OSA events. The medical background of OSA is well understood, but the traditional diagnosis is expensive, as it requires sophisticated measurements and human interpretation of potentially large amounts of physiological data. Electrocardiogram (ECG) measurements have the potential to reduce the cost of OSA diagnosis by simplifying the measurement process. On the down side, detecting OSA events based on ECG data is a complex task which requires highly skilled practitioners. Computer algorithms can help to detect the subtle signal changes which indicate the presence of a disorder. That approach has the following advantages: computers never tire, processing resources are economical and progress, in the form of better algorithms, can be easily disseminated as updates over the internet. Furthermore, Computer-Aided Diagnosis (CAD) reduces intra- and inter-observer variability. In this review, we adopt and support the position that computer based ECG signal interpretation is able to diagnose OSA with a high degree of accuracy.

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“…Their results presented accuracy of 83.96% for the fourth 1 min before SCD onset. Rajendra et al [48] proposed a method using discrete wavelet transform and nonlinear analysis of ECG signals. They segmented the ECG signal 4 min before SCD occurrence.…”

Section: Lmentioning

confidence: 99%

Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals

Houshyarifar¹,

Amirani²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper we present a method to predict sudden cardiac death (SCD) based on the heart rate variability (HRV) signal and recurrence plots and Poincaré plot-extracted features. This work is a challenge since it is aimed to devise a method to predict SCD 5 min before its onset. The method consists of four steps: preprocessing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram signal and then the HRV signal is extracted. In the second step, the recurrence plot of the HRV signal and Poincaré plot-extracted features are obtained. Four features from the recurrence plot and three features from the Poincaré plot are extracted. The features are recurrence rate, determinism, entropy and averaged diagonal line length, and SD1, SD2, and SD1/SD2. In the next step, these features are reduced to one feature by the linear discriminant analysis technique.Finally, K-nearest neighbor and support vector machine-based classifiers are used to classify the HRV signals. We use two databases, the MIT/BIH Sudden Cardiac Death Database and PhysioBank Normal Sinus Rhythm Database. We manage to predict SCD occurrence 5 min before the SCD with accuracy of over 92%.

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An integrated index for detection of Sudden Cardiac Death using Discrete Wavelet Transform and nonlinear features

Cited by 122 publications

References 50 publications

Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data

A Review of Ecg-Based Diagnosis Support Systems for Obstructive Sleep Apnea

Early detection of sudden cardiac death using Poincaré plots and recurrence plot-based features from HRV signals

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