Multipole Analysis of Heart Rate Variability as a Predictor of Imminent Ventricular Arrhythmias in ICD Patients

Abstract

The multipole method of HRV analysis emerges as a highly specific, possible predictor of imminent VTA, providing an early warning allowing to prepare for an arrhythmic episode.

Ventricular Tachyarrhythmia Prediction based on Heart Rate Variability and Genetic Algorithm

“…With 5 minutes of HRV signal length prior to VTA onset, our method achieves 79.41% of accuracy, which is higher than the accuracy reported by Joo, et al, [11] (best previous work) and Wollman, et al, [14] respectively. Although the accuracy levels were not reported by Thong & Raitt [12] and Rozen, et al, [13], their prediction sensitivity and specificity were not balanced with low sensitivity rate (53 and 50%). It shows that their prediction methods achieved poor performance in predicting the VTA onset successfully.…”

“…With the additional types of HRV features and GA based optimization process, prediction performance of our method outperforms all previous works with 77.94%, 80.88% and 79.41 % for sensitivity, specificity and accuracy respectively even though we use stricter approach to evaluate our method. Prediction sensitivity and specificity of our method are more balanced when compared to previous works [12][13][14]. Furthermore, in contrast to most of previous works [12][13][14] that used more than 10 minutes of HRV signal, our method only uses 5 minutes HRV signal, which end immediately prior to VTA onset, for feature extraction.…”

“…Prediction sensitivity and specificity of our method are more balanced when compared to previous works [12][13][14]. Furthermore, in contrast to most of previous works [12][13][14] that used more than 10 minutes of HRV signal, our method only uses 5 minutes HRV signal, which end immediately prior to VTA onset, for feature extraction. Finally, optimal HRV feature subset selected by GA for prediction is also reported for reference in future works.…”

“…Furthermore, their method used significantly shorter HRV signal (5 minutes only) to achieve higher prediction accuracy. In contrast, other previous works [12][13][14] utilized more than 10 minutes of HRV signal in length prior to VTA event for prediction. The main reasons for such higher prediction performance were utilization of more comprehensive types of HRV features than other previous works and employment of more advanced supervised classifier -artificial neural network (ANN).…”

“…By using heart rate pattern, Thong and Raitt [12] achieved prediction performance with 53% of sensitivity and 91% of specificity. With decision rule based system based on multipole analysis, Rozen, et al, [13] achieved 50% of sensitivity and 91.6% of specificity. Wollman, et al, [14] proposed a method, which performed the VTA onset prediction based on time domain features and regression tree classifiers, obtained 70.9% of accuracy.…”