Context The interpretations of cardiotocography (CTG) tracings are indeed vital to monitor fetal well-being both during pregnancy and childbirth. Currently, many studies are focusing on feature extraction and CTG classification using computer vision approach in determining the most accurate diagnosis as well as monitoring the fetal well-being during pregnancy. Additionally, a fetal monitoring system would be able to perform detection and precise quantification of fetal heart rate patterns. Objective This study aimed to perform a systematic review to describe the achievements made by the researchers, summarizing findings that have been found by previous researchers in feature extraction and CTG classification, to determine criteria and evaluation methods to the taxonomies of the proposed literature in the CTG field and to distinguish aspects from relevant research in the field of CTG. Methods Article search was done systematically using three databases: IEEE Xplore digital library, Science Direct, and Web of Science over a period of 5 years. The literature in the medical sciences and engineering was included in the search selection to provide a broader understanding for researchers. Results After screening 372 articles, and based on our protocol of exclusion and inclusion criteria, for the final set of articles, 50 articles were obtained. The research literature taxonomy was divided into four stages. The first stage discussed the proposed method which presented steps and algorithms in the pre-processing stage, feature extraction and classification as well as their use in CTG (20/50 papers). The second stage included the development of a system specifically on automatic feature extraction and CTG classification (7/50 papers). The third stage consisted of reviews and survey articles on automatic feature extraction and CTG classification (3/50 papers). The last stage discussed evaluation and comparative studies to determine the best method for extracting and classifying features with comparisons based on a set of criteria (20/50 articles). Discussion This study focused more on literature compared to techniques or methods. Also, this study conducts research and identification of various types of datasets used in surveys from publicly available, private, and commercial datasets. To analyze the results, researchers evaluated independent datasets using different techniques. Conclusions This systematic review contributes to understand and have insight into the relevant research in the field of CTG by surveying and classifying pertinent research efforts. This review will help to address the current research opportunities, problems and challenges, motivations, recommendations related to feature extraction and CTG classification, as well as the measurement of various performance and various data sets used by other researchers.
Two field experiments were conducted out during 2012 and 2013 seasons to estimate combining ability, heterosis for six inbred lines (Three American inbreds: P97, B73and Oh.43 and three Egyptian inbred lines: R39, Inb.1021 and Inb.1004) and its F1 crosses. The most important results obtained from this investigation can be summarized as follows: The differences among means of parental inbreds and also among means of crosses were significant or highly significant for all studied traits. Mean squares of crosses were highly significant for all studied traits, indicating wide range of genetic variability among the studied crosses and this is primary requirement for further computation. Both general and specific combining abilities mean squares were found to be highly significant for all studied traits. GCA/SCA variances ratios were found to be lower than unity for six traits i.e. time to tassel emergency, time to silk emergency, number of rows/ear, number of kernels/row, grain yield/plant and shelling percentage and higher than unity for plant height and 100-Kernel weigh. Significant positive general combining ability (GCA) effects were found for most studied traits. The best combiners were P2 (P79) and P4 (Inb.1021) for earliness traits; P5 (Inb.1004) and P6 (Oh.43) for plant height; P5 (Inb.1004) for number of rows/ear; P1 (R39) for number of grains/row; P2 (P97), P3 (B73) and P4 (Inb.1021) for 100-grain weight; P1 (R39) for grain yield/plant; P1 (R39) and P2 (P97) for shelling percentage. Significant positive specific combining ability (SCA) effects were found for most studied traits. The best cross combinations P3×P4 for number of rows/ear; P1×P5 for number of grains/row; P1×P3, P1×P6, P2×P3, P2×P5, P3×P5 and P4×P6 for 100-Grain weight; P1×P2, P1×P5, P2×P5, P3×P6 and P4×P5 for grain yield/plant; P1×P5, P3×P6 and P4×P5 for shelling percentage. Results showed significant or highly significant heterosis over mid-parents and better parents for all studied traits. The best crosses over mid and better parents were (P1×P5) for number of rows/ear; (P1×P6) for number of grains/row; (P2×P5) for100-grain weight;(P1×P6)for grain yield and (P1×P4) for shelling percentage. The study recommends using inbred line P3 (B73) and crosses P1×P2, P1×P3 and P3×P6 in breeding program of maize to improve the yield and its components where they recorded the highest value of the grain yield/plant and gave a better combining ability.
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