Handbook of Neural Network Signal Processing
Handbook of Neural Network Signal Processing
Signal Processing - Content-based image and video retrieval
Time-frequency analysis of heart rate variability for neonatal seizure detection
EURASIP Journal on Applied Signal Processing
Investigation of heart rate variability in patients under local anaesthesia
BIEN '07 Proceedings of the fifth IASTED International Conference: biomedical engineering
Efficient solution for frequency band decomposition problem using wavelet packet in HRV
Digital Signal Processing
Expert Systems with Applications: An International Journal
Detection of obstructive sleep apnoea using dynamic filter-banked features
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Heart Rate Variability (HRV) is an efficient tool for assessment of Sympathovagal Balance (SB) and classification of cardiac disturbances. However, its index may be not enough for classification and evaluation of some disease. This study presents 32 new sub-bands over LF and HF base-bands that are accepted in the literature. Moreover, it determines dominant sub-bands over both base-bands in VTA database. These sub-bands are obtained using Wavelet Packet Transform (WPT) and evaluated using Multilayer Perceptron Neural Networks (MLPNN). Results are compared with obtained results from normal datasets. The domination effects of these sub-bands are assessed according to comparison of each other related to MLPNN training and test accuracy percentages by selecting different width of windows. As a result, obtained results showed that the LF zone including LF"1, LF"2 and LF"3 sub-bands on 0.0390625-0.0859375Hz frequency range is the most dominant over the LF base-band and, the HF zone including HF1, HF2 and HF3 on 0.1953125-0.28125Hz frequency range is the most dominant over the HF base-band. In normal datasets, distinctive domination effect has not been determined.