Sneh Anand

Bio: Sneh Anand is an academic researcher from Indian Institutes of Technology. The author has contributed to research in topics: Electrical impedance tomography & Transdermal. The author has an hindex of 25, co-authored 204 publications receiving 2676 citations. Previous affiliations of Sneh Anand include STMicroelectronics & Indian Institute of Technology Delhi.

Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography.

Abstract: Heart rate variability (HRV) is traditionally derived from RR interval time series of electrocardiography (ECG). Photoplethysmography (PPG) also reflects the cardiac rhythm since the mechanical activity of the heart is coupled to its electrical activity. Thus, theoretically, PPG can be used for determining the interval between successive heartbeats and heart rate variability. However, the PPG wave lags behind the ECG signal by the time required for transmission of pulse wave. In this study, finger-tip PPG and standard lead II ECG were recorded for five minutes from 10 healthy subjects at rest. The results showed a high correlation (median = 0.97) between the ECG-derived RR intervals and PPG-derived peak-to-peak (PP) intervals. PP variability was accurate (0.1 ms) as compared to RR variability. The time domain, frequency domain and Poincare plot HRV parameters computed using RR interval method and PP interval method showed no significant differences (p < 0.05). The error analysis also showed insignificant differences between the HRV indices obtained by the two methods. Bland-Altman analysis showed high degree of agreement between the two methods for all the parameters of HRV. Thus, HRV can also be reliably estimated from the PPG based PP interval method.

L-asparaginase: a promising chemotherapeutic agent.

Abstract: This article comprises detailed information about L-asparaginase, encompassing topics such as microbial and plant sources of L-asparaginase, treatment with L-asparaginase, mechanism of action of L-...

A novel robust diagnostic model to detect seizures in electroencephalography

Abstract: A robust method is proposed for efficient detection of seizures in EEG.Dual tree-complex wavelet transform is used for feature extraction.General regression neural network is employed to classify extracted features.The proposed technique is giving ceiling level performance.The model can be used for fast and accurate diagnosis of epilepsy. Identifying seizure patterns in complex electroencephalography (EEG) through visual inspection is often challenging, time-consuming and prone to errors. These problems have motivated the development of various automated seizure detection systems that can aid neurophysiologists in accurate diagnosis of epilepsy. The present study is focused on the development of a robust automated system for classification against low levels of supervised training. EEG data from two different repositories are considered for analysis and validation of the proposed system. The signals are decomposed into time-frequency sub-bands till sixth level using dual-tree complex wavelet transform (DTCWT). All details and last approximation coefficients are used to calculate features viz. energy, standard deviation, root-mean-square, Shannon entropy, mean values and maximum peaks. These feature sets are passed through a general regression neural network (GRNN) for classification with K-fold cross-validation scheme under varying train-to-test ratios. The current model yields ceiling level classification performance (accuracy, sensitivity & specificity) in all combinations of datasets (ictal vs non-ictal) in less than 0.028s. The proposed scheme will not only maximize hit-rate and correct rejection rate but also will aid neurophysiologists in the fast and accurate diagnosis of seizure onset.

Expert model for detection of epileptic activity in EEG signature

Abstract: Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and high accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet transform (DWT) and energy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN) for classification. Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till the sixth-level using DWT. Approximate energy (EDA) values of the wavelet coefficients at all nodes of the down sampled tree were used as a feature vector to characterize the predictability of the epileptic activity within the records of EEG data. In order to demonstrate the classification accuracy of the proposed probabilistic neural network, tenfold cross-validation was implemented in the expert model. Clinical EEG data recorded from normal as well as epileptic subjects were used to test the performance of this new scheme. It was found that with the proposed scheme, the detection is 99.33% accurate with sensitivity and specificity as 99.6% and 99%, respectively. The proposed model can be widely used in developing countries where there is an acute shortage of trained neurologist.

Overcoming the challenges in administering biopharmaceuticals: formulation and delivery strategies

Abstract: The formulation and delivery of biopharmaceutical drugs, such as monoclonal antibodies and recombinant proteins, poses substantial challenges owing to their large size and susceptibility to degradation. In this Review we highlight recent advances in formulation and delivery strategies — such as the use of microsphere-based controlled-release technologies, protein modification methods that make use of polyethylene glycol and other polymers, and genetic manipulation of biopharmaceutical drugs — and discuss their advantages and limitations. We also highlight current and emerging delivery routes that provide an alternative to injection, including transdermal, oral and pulmonary delivery routes. In addition, the potential of targeted and intracellular protein delivery is discussed.

Advances in male contraception.

Abstract: Despite significant advances in contraceptive options for women over the last 50 yr, world population continues to grow rapidly. Scientists and activists alike point to the devastating environmental impacts that population pressures have caused, including global warming from the developed world and hunger and disease in less developed areas. Moreover, almost half of all pregnancies are still unwanted or unplanned. Clearly, there is a need for expanded, reversible, contraceptive options. Multicultural surveys demonstrate the willingness of men to participate in contraception and their female partners to trust them to do so. Notwithstanding their paucity of options, male methods including vasectomy and condoms account for almost one third of contraceptive use in the United States and other countries. Recent international clinical research efforts have demonstrated high efficacy rates (90-95%) for hormonally based male contraceptives. Current barriers to expanded use include limited delivery methods and perceived regulatory obstacles, which stymie introduction to the marketplace. However, advances in oral and injectable androgen delivery are cause for optimism that these hurdles may be overcome. Nonhormonal methods, such as compounds that target sperm motility, are attractive in their theoretical promise of specificity for the reproductive tract. Gene and protein array technologies continue to identify potential targets for this approach. Such nonhormonal agents will likely reach clinical trials in the near future. Great strides have been made in understanding male reproductive physiology; the combined efforts of scientists, clinicians, industry and governmental funding agencies could make an effective, reversible, male contraceptive an option for family planning over the next decade.