Ovarian torsion is a rare entity and the diagnosis is commonly missed. Here we present a series of two cases of ovarian torsion. First case followed the in vitro fertilization treatment, along with ovarian hyperstimulation syndrome, where even with timely intervention and laparoscopy, we had to compromise one ovary. Second case followed the ovulation induction and intrauterine insemination – where timely intervention helped us to save the ovary.

Pregnancy in a non-communicating rudimentary horn is rare and such a pregnancy culminating in the delivery of a live fetus is even rarer. Despite advances in ultrasonography, the accuracy of ultrasound in diagnosing rudimentary horn pregnancy at advanced gestation remains elusive. Confirmatory diagnosis is made only at laparotomy. We report a multigravidae who presented at 37 weeks with transverse lie oligoamnios and decreased perception of fetal movement since quickening. Laparotomy for placenta accreta suspected on ultrasound revealed non-communicating unruptured rudimentary horn pregnancy with a live fetus and placenta percreta. Successful extraction of a term live fetus weighing 2.7 kg with excision of the rudimentary horn was carried out.

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In this paper, a methodology for sleep apnea detection based on ECG signal analysis using Hilbert transform is proposed. The proposed work comprises a sequential procedure of preprocessing, QRS complex detection using Hilbert Transform, feature extraction from the detected QRS complex and the feature reduction using principal component analysis (PCA). Finally, the classification of the ECG signal recordings has been done using two different artificial neural networks (ANN), one trained with Levenberg-Marquardt (LM) algorithm and the other trained with Scaled Conjugate Gradient (SCG) method guided by K means clustering. The result of classification of the input ECG record is as either belonging to Apnea or Normal category. The performance measures of classification using the two classification algorithms are compared. The experimental results indicate that the SCG algorithm guided by K means clustering (ANN-SCG) has outperformed the LM algorithm (ANN-LM) by attaining accuracy, sensitivity and specificity values as 99.2%, 96% and 97% respectively, besides the saving achieved in terms of reduced number of principal components. Profiling time and mean square error of the ANN classifier trained with SCG algorithm is significantly reduced by 58% and 83%, respectively, as compared to LM algorithm.

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