Prevalence of neurological malformation in newborns at a tertiary care center in Rajasthan, India

Abstract

Introduction: Many newborns die every year due to various congenital anomalies and those who survive, suffer from long-term morbidity. To deal with birth defects, a large proportion of health resources and workforce is required. Among the various congenital anomalies, neurological birth defects are the leading type. Objective: The objective of the study was to document the epidemiological features and prevalence of congenital neurological anomalies in rural areas. Materials and Methods: This retrospective study was conducted in the department of pediatric of a medical college of Rajasthan. The study population included live births born in our institution between 1990 and 2018. A register was maintained after delivery to document the particulars of neonates with a congenital birth defect and their mothers. We had taken neonatal record from this register and analyzed the data of the past 28 years, retrospectively. Results: Of 241,848 live births, 6623 cases were identified with a primary diagnosis of one or more congenital anomalies giving a prevalence rate of 274/10,000 live births (95% confidence interval [CI]: 262.2–285.8). Anomalies of the nervous system were the second common defects, accounting for 19.95% of the birth defects just after the heart disease anomalies (21.65%). Neurological anomalies were diagnosed in 1321 neonates. The prevalence of neurological anomalies was 54.62/10,000 live births (95% CI: 49.75–59.49). The most common neurological anomaly was myelomeningocele/meningocele (spina bifida cystic) found in 64.87% of cases followed by Chiari malformation (7.72%), encephalocele (6.89%), microcephaly (6.88%), hydrocephalous (1.43%), and spina bifida occulta (1.43%). Conclusion: Neurological anomalies were among common congenital anomalies and considerable cause of mortality and morbidity. Myelomeningocele/meningocele (spina bifida cystic) was the most common neurological anomaly.

Bengali Longhand Character Recognition using Fourier Transform and Euclidean Distance Metric

“…In a Fourier transformed image each point represents a particular frequency contained in the spatial domain. The Discrete Fourier Transform (DFT) is the sampled Fourier Transform containing only a set of samples which is large enough to fully describe the spatial domain image [3].…”

“…Euclidean Distance Metric (EDM) provides good recognition accuracy. From the transformed images comparing the Euclidean distance that is found from the input dataset and the printed dataset, the minimum distance value that is obtained defines the character or digit that we are comparing to or trying to find out [3].…”