Nonlinear short-term heart rate variability prediction of spontaneous ventricular tachyarrhythmia

Prediction of paroxysmal atrial fibrillation using new heart rate variability features

Paroxysmal atrial fibrillation (PAF) is a cardiac arrhythmia that can eventually lead to heart failure or stroke if left untreated. Early detection of PAF is therefore crucial to prevent any further complications and avoid fatalities. An implantable defibrillator device could be used to both detect and treat the condition though such devices have limited computational capability. With this constraint in mind, this paper presents a novel set of features to accurately predict the presence of PAF. The method is evaluated using ECG signals from the widely used atrial fibrillation prediction database (AFPDB) from PhysioNet. We analysed 106 signals from 53 pairs of ECG recordings. Each pair of signals contains one 5-min ECG segment that ends just before the onset of a PAF event and another 5-min ECG segment at least 45 min distant from the PAF event, to represent a non-PAF event. Seven novel features are extracted through the Poincaré representation of R-R interval signals, and are prioritised through feature ranking schemes. The features are used with four standard classification techniques for PAF prediction and compared to the existing state of the art from the literature. Using only the seven proposed features, classification performance outperforms those of the classical state-of-the-art feature set, registering sensitivity and specificity measurements of over 96%. The results further improve when the features are combined with several of the classical features, with an accuracy increasing to 98% using a linear kernel SVM. The results show that the proposed features provide a useful representation of the PAF condition and achieve good prediction with off-the-shelf classification techniques that would be suitable for ICU deployment.

Prediction of Sudden Cardiac Death in Implantable Cardioverter Defibrillators: A Review and Comparative Study of Heart Rate Variability Features

Over the last four decades, implantable cardioverter defibrillators (ICDs) have been widely deployed to reduce sudden cardiac death (SCD) risk in patients with a history of life-threatening arrhythmia. By continuous monitoring of the heart rate, ICDs can use decision algorithms to distinguish normal cardiac sinus rhythm or supra-ventricular tachycardia from abnormal cardiac rhythms like ventricular tachycardia and ventricular fibrillation and deliver appropriate therapy such as an electrical stimulus. Despite the success of ICDs, more research is still needed, particularly in decision-making algorithms. Because of low specificity in practical devices, patients with ICDs still receive inappropriate shocks, which may lead to inadvertent mortality and reduction of quality of life. At the same time, higher sensitivity can lead to the use of newer tiered therapies. The purpose of this study is to review the literature on common signal features used in detection algorithms for abnormal cardiac sinus rhythm, as well as reviewing datasets used for algorithm development in previous studies. More than 50 different features to address heart rate changes before SCD have been reviewed and general methodology on this area proposed based on variety of studies on ICDs functionality. A comparative study on the prediction performance of these features, using a common database, is also presented. By combining these features with a support vector machine classifier, achieved results have compared well with other studies.

A naturalistic study of the effect of acupuncture on heart-rate variability

OBJECTIVES

To study the effect of acupuncture on heart rate variability (HRV) by using linear and non-linear methods of analysis.

METHODS

40 patients were recruited consecutively, including patients with insomnia, stomachache, diarrhea, dizziness, cervical syndrome, lower back pain, gonarthritis, peripheral facial paralysis, post-traumatic organic brain syndrome and urinary retention. Different acupoint prescriptions were used, according to the textbook for 5-years' education on traditional Chinese medicine specialty, which is used in Chinese Universities. HRV was recorded before, during, and after acupuncture.

RESULTS

Acupuncture substantially reduced variability, causing a 41% reduction in the standard deviation. Using a Fourier analysis, the variances both in the low frequency (LF) and the high frequency (HF) ranges were markedly reduced, but the LF/HF ratio (an indication of sympatho-vagal balance) was not altered. The HR was unchanged. The sample entropy, which is a measure of the complexity of time series, was significantly increased (+35%).

CONCLUSIONS

Acupuncture produced a pattern of changes different from that seen in pathological conditions, where increased variability and reduced complexity is expected.