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456 results on '"Bajaj, Varun"'

157. Effect of Palonosetron monotherapy versus Palonosetron with Dexamethasone combination therapy for prevention of post operative nausea vomiting in children undergoing strabismus surgery

Author

Bajaj, Varun, Singh, Shalendra, Sharma, Ritesh, Taank, Priya, Dwivedi, Deepak, Bajaj, Varun, Singh, Shalendra, Sharma, Ritesh, Taank, Priya, and Dwivedi, Deepak

Abstract

Background: Postoperative nausea and vomiting (PONV) is the most distressing symptom reported in 40 88% of patients subsequent to strabismus surgery. Newer antiemetic 5-HT3 receptor antagonist drugs palonosetron and its combination with dexamethasone for prevention PONV in chemotherapy patients having promising results, but has not been studied in children undergoing strabismus surgery. Methodology: 120 patients were registered in this prospective, randomized double blind study. Group A received palonosetron 1.5 mcgkg-1 and Group B received an additional dose of Inj dexamethasone 0.25 mgkg-1 prior to anaesthesia. Post operative data of PONV and postoperative vomiting (POV) was recorded at pre defined intervals in different age groups. Results: There is no difference of POV in both groups at different age groups. The incidence of post operative nausea in Group A was 44% and Group B was 11% (p=0.02).Considering the incidence of PONV in all age groups, our results showed that the overall incidence of PONV in Group A was found to be more than that with Group B (p 0.05). Also in age group 3 year to 9 year, the incidence of PONV was higher in Group A than Group B (p =0.01).The incidence of PONV was found to be 17% (6 out of 36), 21% (14 out of 68) and 6% (1 out of 16) when one, two and three extra ocular muscles were operated respectively(p=0.39). Conclusion: Palonosetron with dexamethasone combination therapy is recommended to be used in children age > 3years undergoing strabismus surgery.

Published
2019

158. Comparative Study of Venous Pressure Obtained From Central and Peripheral Venous Catheter

Author

Radhakrishna, Nayani, Singh, Shalendra, Sharma, Ritesh, Bajaj, Varun, Taank, Priya, Radhakrishna, Nayani, Singh, Shalendra, Sharma, Ritesh, Bajaj, Varun, and Taank, Priya

Abstract

Background: Venous pressure can be measured by placing Central venous catheter (CVC) and Peripheral venous catheter (PVC). Venous pressure recording obtained from CVC is called central venous pressure (CVP) and that obtained from PVC is called peripheral venous pressure (PVP). PVC is easy to place and free of complication and sometimes preferable in some cases. CVC though used commonly is not free from complications. We investigate the feasibility and accuracy of measuring PVP and precise correlation between PVP and CVP. Methods : In this prospective, observational study, a total of 2522 readings of CVP and PVP were obtained from 60 patients in varying positions before and after 500 ml of fluid challenge intraoperatively. Results: Throughout the study period, PVP persistently showed a positive trend with a significantly higher value than CVP (P 0.001). Correlation for the entire data showed a significant consistency of CVP-PVP difference [r = 0.89 (95% CI: 0.87 to 0.90); p 0.0001)]. Tests of correlation revealed that whenever CVP trends changed rapidly so did PVP trends, and in synchrony with each other. The results revealed strong correlation between CVP and PVP (r2=0.79). Conclusion: Our study suggests that CVP and PVP change similarly due to changes in intravascular volume in a given patient. The CVP-PVP gradient is not similar between patients; therefore it is not possible to predict CVP from PVP unless the gradient in the individual patient is known beforehand.

Published
2019

198. An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals

Author

Siuly, Siuly, Bajaj, Varun, Sengur, Abdulkadir, and Zhang, Yanchun

Abstract

This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram (EEG) data in an automatic way. This study introduces an optimum allocation based sampling (OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes (e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods (decision table, support vector machine (SVM), k-nearest neighbor (k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD (i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to 9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals.

Published
2019
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199. A novel pectoral muscle segmentation from scanned mammograms using EMO algorithm

Author

Avuti, Santhos Kumar, Bajaj, Varun, Kumar, Anil, and Singh, Girish Kumar

Abstract

Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur’s and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.

Published
2019
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200. Computer-aided diagnosis of breast cancer using bi-dimensional empirical mode decomposition.

Author

Bajaj, Varun, Pawar, Mayank, Meena, Vinod Kumar, Kumar, Mukesh, Sengur, Abdulkadir, and Guo, Yanhui

Subjects
*HILBERT-Huang transform, *CANCER diagnosis, *FEATURE extraction, *SUPPORT vector machines, *LEAST squares, *BREAST cancer, *DIAGNOSIS methods
Abstract

Breast cancer is a serious disease for women in the world and ranks the second cancer for women in many countries. Computer-aided diagnosis provides a second view to aid for radiologists to detect and diagnose breast cancer. In this paper, we present a novel approach of textural features extraction from mammograms using bi-dimensional empirical mode decomposition (BEMD) method and classification for diagnosis of breast cancer. Preprocessing techniques such as noise removal, artifacts and background suppression and contrast enhancement are performed before features extraction stage. Gray-level co-occurrence matrices-based features are extracted from 2-D intrinsic mode functions obtained by applying BEMD method on mammographic images. Finally, these features are given as an input to least squares support vector machine for classification of mammogram as normal or abnormal. The experimental results show that the proposed method achieves 95% accuracy, which is better than as compared to other published methods in the Mini-MIAS database for diagnosis of breast cancer. The proposed method can be used as automatic, accurate and noninvasive method for breast cancer diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Published
2019
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