Name
Title | ||
|---|---|---|
Sudden cardiac death (SCD) prediction based on nonlinear heart rate variability features and SCD index. |
Abstract | ||
|---|---|---|
SCD is predicated using SVM classifier and sudden cardiac death index (SCDI).Nonlinear features are extracted from HRV signals.SVM predicts SCD with 94.7% accuracy four minutes before its onset.SCDI predicts SCD accurately. In our previous work, we have developed a sudden cardiac death index (SCDI) using electrocardiogram (ECG) signals that could effectively predict the occurrence of SCD four minutes before the onset. Thus, the prediction of SCD before its onset by using heart rate variability (HRV) signals is a worthwhile task for further investigation. Therefore, in this paper, a new novel methodology to automatically classify the HRV signals of normal and subjects at risk of SCD by using nonlinear techniques has been presented. In this study, we have predicted SCD by analyzing four-minutes of HRV signals (separately for each one-minute interval) prior to SCD occurrence by using nonlinear features such as Renyi entropy (REnt), fuzzy entropy (FE), Hjorth's parameters (activity, mobility and complexity), Tsallis entropy (TEnt), and energy features of discrete wavelet transform (DWT) coefficients. All the clinically significant features obtained are ranked using their t-value and fed to classifiers such as K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). In this work, we have achieved an accuracy of 97.3%, 89.4%, 89.4%, and 94.7% for prediction of SCD one, two, three, and four minutes prior to the SCD onset respectively using SVM classifier. Furthermore, we have also developed a novel SCD Index (SCDI) by using nonlinear HRV signal features to classify the normal and SCD prone HRV signals. Our proposed technique is able to identify the person at risk of developing SCD four minutes earlier, thereby providing sufficient time for the clinicians to respond with treatment in Intensive Care Units (ICU). Thus, this proposed technique can thus serve as a valuable tool for increasing the survival rate of many cardiac patients. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.asoc.2016.02.049 | Appl. Soft Comput. |
Keywords | Field | DocType |
Sudden cardiac death,Ventricular fibrillation,ECG,Heart rate,Nonlinear methods | Rényi entropy,Sudden cardiac death,Artificial intelligence,Heart rate,Ventricular fibrillation,Internal medicine,Heart rate variability,Support vector machine,Cardiology,Speech recognition,Tsallis entropy,Intensive care,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
43 | C | 1568-4946 |
Citations | PageRank | References |
17 | 1.03 | 25 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hamido Fujita | 1 | 2644 | 185.03 |
| Rajendra Acharya U | 2 | 4666 | 296.34 |
| Vidya Sudarshan | 3 | 208 | 14.19 |
| Dhanjoo N Ghista | 4 | 102 | 11.91 |
| Subbhuraam Vinitha Sree | 5 | 482 | 23.22 |
| Wei Jie Eugene Lim | 6 | 101 | 5.06 |
| Joel E. W. Koh | 7 | 266 | 19.06 |