## 3 Results and Discussion
### Discussion  
NNW analysis has been used for objective, definitive or quick decision making in a wide range of fields such as traveling sales [27], business, and in the medical field for applications including electroencephalography [28], histology [29], brain function analysis [30], brain tumor detection [31], smear categorization [32], visual field studies [33], response to fault [34], coronary artery disease [35], HIV studies [36], prediction of the duration of the first stage of labor [37], fetal weight estimation [38], and fetal monitoring [12; 13; 14; 23].  
Figure 3: The neural index [26]. Neural index values in every 5 minutes form a curve in a case of prolonged fetal monitoring for 230 minutes, where the final value of the curve was +37% and the neonatal status was normal. The neonates showed a depressed state when the neural index was negative immediately before birth, and no neonatal asphyxia was noted when the final neural index value was positive.  
A back-propagation system trained by typical teacher data is common in the NNW system. A NNW computer is composed of the input layer, intermediate hidden layer and output layer. Each layer is composed of multiple active units and connected with each other. Our NNW computer is composed of an intermediate layer with 30 active units, because a high number of active units are essential for accurate function. The NNW software package is usually a back-propagation system that feeds error signals back to the network, effectively increasing the coupling weight of appropriate units, and thus minimizing decision errors after repeated training.  
The use of complete FHR data in the NNW computer is time consuming for system training and diagnosis. Since automated FHR analysis is utilized in the centralized system for the multiple simultaneous monitoring with time sharing, the analysis time should be as short as possible. For this reason, the hybrid system with limited parameters is ideal and was the chosen method for analysis in the present study. Although sinusoidal FHR is rare in obstetrical practice, it should be automatically diagnosed from FHR changes within a short time frame. We identified the physiological false positive sinusoidal FHR by frequency analysis and cases with pathological sinusoidal FHR were identified by the NNW system. The 20 cases used for training the NNW computer were selected to include indispensable FHR changes, such as severe variable deceleration, late deceleration, the loss of baseline variability, bradycardia, and sinusoidal FHR.  
Although the ratio of coincidence to clinical findings was 86% over 50 minutes of monitoring, the long interval between FHR analysis and birth may influence the accuracy rate. Shorter analysis times, such as 15 minutes of monitoring evaluated every 5 minutes, as in the present study, may improve the accuracy rate.  
Although the trend gram was studied in previous report [23], the neural index curve may be a better indicator of fetal status in prolonged monitoring [26]. Since the A/B ratio of an actocardiogram clearly shows fetal status, computerized evaluations of actocardiograms may be introduced into automated monitoring in the future, although the present NNW diagnosis depends only on FHR changes and not on fetal movements.  
The recent progress in the automated fetal evaluation is the application of wavelet analysis [40] and entropy system [39; 41] in the computerized studies on the fetal heart rate. The fetal state is analyzed for the fetal well-being with the wavelet [40], and for the detection of fetal distress by the entropy system [39]. The fetal ECG masking maternal ECG signal is rejected on the abdominal wall in order to improve fetal heart signals for the precise beat-to-beat variability using neuro-fuzzy inference system [42]. Fetal study will be further progressed by the improved analysis of fetal heart rate by introducing the new techniques.