Journal Articles
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Item Analysis of cortical rhythms in intracranial EEG by temporal difference operators during epileptic seizures(Elsevier Ltd, 2016) Malali, A.; Chaitanya, G.; Gowda, S.; Majumdar, K.Brain oscillations have traditionally been studied by time-frequency analysis of the electrophysiological signals. In this work we demonstrated the usefulness of two nonlinear combinations of differential operators on intracranial EEG (iEEG) recordings to study abnormal oscillations in human brain during intractable focal epileptic seizures. Each one dimensional time domain signal was visualized as the trajectory of a particle moving in a force field with one degree of freedom. Modeling of the temporal difference operators to be applied on the signals was inspired by the principles of classical Newtonian mechanics. Efficiency of one of the nonlinear combinations of the operators in distinguishing the seizure part from the background signal and the artifacts was established, particularly when the seizure duration was long. The resultant automatic detection algorithm is linear time executable and detects a seizure with an average delay of 5.02 s after the electrographic onset, with a mean 0.05/h false positive rate and 94% detection accuracy. The area under the ROC curve was 0.959. Another nonlinear combination of differential operators detects spikes (peaks) and inverted spikes (troughs) in a signal irrespective of their shape and size. It was shown that in a majority of the cases simultaneous occurrence of all the spikes and inverted spikes across the focal channels was more after the seizure offset than during the seizure, where the duration after the offset was taken equal to the duration of the seizure. It has been explained in terms of GABAergic inhibition of seizure termination. © 2016 Elsevier Ltd. All rights reserved.Item Laser assisted zona hatching does not lead to immediate impairment in human embryo quality and metabolism(Taylor and Francis Ltd healthcare.enquiries@informa.com, 2016) Uppangala, S.; D’Souza, F.; Pudakalakatti, S.; Atreya, H.S.; Raval, K.; Kalthur, G.; Adiga, S.K.Laser assisted zona hatching (LAH) is a routinely used therapeutic intervention in assisted reproductive technology for patients with poor prognosis. However, results are not conclusive in demonstrating the benefits of zona hatching in improving the pregnancy rate. Recent observations on LAH induced genetic instability in animal embryos prompted us to look into the effects of laser assisted zona hatching on the human preimplantation embryo quality and metabolic uptake using high resolution nuclear magnetic resonance (NMR) technology. This experimental prospective study included fifty embryos from twenty-five patients undergoing intra cytoplasmic sperm injection. Embryo quality assessment followed by profiling of spent media for the non-invasive evaluation of metabolites was performed using NMR spectroscopy 24 hours after laser treatment and compared with that of non-treated sibling embryos. Both cell number and embryo quality on day 3 of development did not vary significantly between the two groups at 24 hours post laser treatment interval. Time lapse monitoring of the embryos for 24 hours did not reveal blastomere fragmentation adjacent to the point of laser treatment. Similarly, principal component analysis of metabolites did not demonstrate any variation across the groups. These results suggest that laser assisted zona hatching does not affect human preimplantation embryo morphology and metabolism at least until 24 hours post laser assisted zona hatching. However, studies are required to elucidate laser induced metabolic and developmental changes at extended time periods. Abbreviations: AH: assisted hatching; ART: assisted reproductive technology; DNA: deoxy-ribo nucleic acid; LAH: laser assisted hatching; MHz: megahertz; NMR: nuclear magnetic resonance; PCA: principal component analysis; PGD: preimplantation genetic diagnosis; TLM: time lapse monitoring © 2016 Taylor & Francis.Item Metabolomic profiling of doxycycline treatment in chronic obstructive pulmonary disease(Elsevier B.V., 2017) Singh, B.; Jana, S.K.; Ghosh, N.; Das, S.K.; Joshi, M.; Bhattacharyya, P.; Chaudhury, K.Serum metabolic profiling can identify the metabolites responsible for discrimination between doxycycline treated and untreated chronic obstructive pulmonary disease (COPD) and explain the possible effect of doxycycline in improving the disease conditions. 1H nuclear magnetic resonance (NMR)-based metabolomics was used to obtain serum metabolic profiles of 60 add-on doxycycline treated COPD patients and 40 patients receiving standard therapy. The acquired data were analyzed using multivariate principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA), and orthogonal projection to latent structure with discriminant analysis (OPLS-DA). A clear metabolic differentiation was apparent between the pre and post doxycycline treated group. The distinguishing metabolites lactate and fatty acids were significantly down-regulated and formate, citrate, imidazole and L-arginine upregulated. Lactate and folate are further validated biochemically. Metabolic changes, such as decreased lactate level, inhibited arginase activity and lowered fatty acid level observed in COPD patients in response to add-on doxycycline treatment, reflect the anti-inflammatory action of the drug. Doxycycline as a possible therapeutic option for COPD seems promising. © 2016 Elsevier B.V.Item Automatic seizure detection by modified line length and Mahalanobis distance function(Elsevier Ltd, 2018) Pathak, A.; Ramesh, A.; Mitra, A.; Majumdar, K.Automatic seizure detection with high accuracy and in linear time has profound implications on therapeutic intervention mechanisms. In this work taking into account 12 popular seizure detection algorithms we have shown that line length is one feature that is extractable in linear time from EEG signals and capable of automatic seizure onset detection with highest accuracy among linear time extractable features. Also line length is less prone to give false positives. The detection accuracy has been ascertained by ROC curve analysis on Freiburg Seizure Prediction Project data containing intracranial EEG recordings of 87 seizures from 21 patients with sufficient interictal signals. Next, we have modified the classical line length feature extraction algorithm to improve its accuracy without any additional computational burden. Finally, we have applied both classical line length (LL) and modified line length (MLL) on all focal channels and detected seizures on multidimensional focal channel signals by Mahalanobis distance function (MDF). Both detected 73 out of 87 seizures. Area under the ROC curve (AUC), detection delay and false positive for LL and MLL are 0.951, 11.903 s, 0.201/h and 0.954, 11.698 s, 0.198/h respectively. Since LL has already been incorporated into an FDA approved commercially available closed loop intervention system, even this minute improvement may have significant healthcare implications. © 2018 Elsevier LtdItem Automatic detection and localization of Focal Cortical Dysplasia lesions in MRI using fully convolutional neural network(Elsevier Ltd, 2019) Bijay Dev, K.M.; Pawan, P.S.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.Focal cortical dysplasia (FCD) is the leading cause of drug-resistant epilepsy in both children and adults. At present, the only therapeutic approach in patients with drug-resistant epilepsy is surgery. Hence, the quantification of FCD via non-invasive imaging techniques helps physicians to decide on surgical interventions. The properties like non-invasiveness and capability to produce high-resolution images makes magnetic resonance imaging an ideal tool for detecting the FCD to an extent. The FCD lesions vary in size, shape, and location for different patients and make the manual detection time consuming and sensitive to the experience of the observer. Automatic segmentation of FCD lesions is challenging due to the difference in signal strength in images acquired with different machines, noise, and other kinds of distortions such as motion artifacts. Most of the methods proposed in the literature use conventional machine learning and image processing techniques in which their accuracy relies on the trained features. Hence, feature extraction should be done more precisely which requires human expertise. The ability to learn the appropriate features/representations from the training data without any human interventions makes the convolutional neural network (CNN) the suitable method for addressing these drawbacks. As far as we are aware, this work is the first one to use a CNN based model to solve the aforementioned problem using only MRI FLAIR images. We customized the popular U-Net architecture and trained the proposed model from scratch (using MRI images acquired with 1.5T and 3T scanners). FCD detection rate (recall) of the proposed model is 82.5 (33/40 patients detected correctly). © 2019Item Evaluation of implant properties, safety profile and clinical efficacy of patient-specific acrylic prosthesis in cranioplasty using 3D binderjet printed cranium model: A pilot study(Churchill Livingstone, 2021) Basu, B.; Bhaskar, N.; Barui, S.; Sharma, V.; Das, S.; Govindarajan, N.; Hegde, P.; Perikal, P.J.; Antharasanahalli Shivakumar, M.; Khanapure, K.; Jagannatha, A.There exists a significant demand to develop patient-specific prosthesis in reconstruction of cranial vaults after decompressive craniectomy. we report here, the outcomes of an unicentric pilot study on acrylic cranial prosthesis fabricated using a 3D printed cranium model with its clinically relevant mechanical properties. Methods: The semi-crystalline polymethyl methacrylate (PMMA) implants, shaped to the cranial defects of 3D printed cranium model, were implanted in 10 patients (mean age, 40.8 ± 14.8 years). A binderjet 3D printer was used to create patient-specific mould and PMMA was casted to fabricate prosthesis which was analyzed for microstructure and properties. Patients were followed up for allergy, infection and cosmesis for a period of 6 months. Results: As-cast PMMA flap exhibited hardness of 15.8 ± 0.24Hv, tensile strength of 30.7 ± 3.9 MPa and elastic modulus of 1.5 ± 0.1 GPa. 3D microstructure of the semi-crystalline acrylic implant revealed 2.5–15 µm spherical isolated pores. The mean area of the calvarial defect in craniectomy patients was 94.7 ± 17.4 cm2. We achieved a cranial index of symmetry (CIS -%) of 94.5 ± 3.9, while the average post-operative Glasgow outcome scale (GOS) score recorded was 4.2 ± 0.9. Conclusions: 3D printing based patient-specific design and fabrication of acrylic cranioplasty implant is safe and achieves acceptable cosmetic and clinical outcomes in patients with decompressive craniectomy. Our study ensured clinically acceptable structural and mechanical properties of implanted PMMA, suggesting that a low cost 3D printer based PMMA flap is an affordable option for cranioplasty in resource constrained settings. © 2021 Elsevier LtdItem Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images(Institute of Electrical and Electronics Engineers Inc., 2021) Thomas, E.; Pawan, S.J.; Kumar, S.; Horo, A.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis. © 2013 IEEE.Item Automated Molecular Subtyping of Breast Carcinoma Using Deep Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2023) Niyas, S.; Bygari, R.; Naik, R.; Viswanath, B.; Ugwekar, D.; Mathew, T.; Kavya, J.; Kini, J.R.; Rajan, J.Objective: Molecular subtyping is an important procedure for prognosis and targeted therapy of breast carcinoma, the most common type of malignancy affecting women. Immunohistochemistry (IHC) analysis is the widely accepted method for molecular subtyping. It involves the assessment of the four molecular biomarkers namely estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and antigen Ki67 using appropriate antibody reagents. Conventionally, these biomarkers are assessed manually by a pathologist, who finally combines individual results to identify the molecular subtype. Molecular subtyping necessitates the status of all the four biomarkers together, and to the best of our knowledge, no such automated method exists. This paper proposes a novel deep learning framework for automatic molecular subtyping of breast cancer from IHC images. Methods and procedures: A modified LadderNet architecture is proposed to segment the immunopositive elements from ER, PR, HER2, and Ki67 biomarker slides. This architecture uses long skip connections to pass encoder feature space from different semantic levels to the decoder layers, allowing concurrent learning with multi-scale features. The entire architecture is an ensemble of multiple fully convolutional neural networks, and learning pathways are chosen adaptively based on input data. The segmentation stage is followed by a post-processing stage to quantify the extent of immunopositive elements to predict the final status for each biomarker. Results: The performance of segmentation models for each IHC biomarker is evaluated qualitatively and quantitatively. Furthermore, the biomarker prediction results are also evaluated. The results obtained by our method are highly in concordance with manual assessment by pathologists. Clinical impact: Accurate automated molecular subtyping can speed up this pathology procedure, reduce pathologists' workload and associated costs, and facilitate targeted treatment to obtain better outcomes. © 2013 IEEE.Item Computational assessment on the impact of collagen fiber orientation in cartilages on healthy and arthritic knee kinetics/kinematics(Elsevier Ltd, 2023) Raju, V.; Koorata, P.K.Background: The inhomogeneous distribution of collagen fiber in cartilage can substantially influence the knee kinematics. This becomes vital for understanding the mechanical response of soft tissues, and cartilage deterioration including osteoarthritis (OA). Though the conventional computational models consider geometrical heterogeneity along with fiber reinforcements in the cartilage model as material heterogeneity, the influence of fiber orientation on knee kinetics and kinematics is not fully explored. This work examines how the collagen fiber orientation in the cartilage affects the healthy (intact knee) and arthritic knee response over multiple gait activities like running and walking. Methods: A 3D finite element knee joint model is used to compute the articular cartilage response during the gait cycle. A fiber-reinforced porous hyper elastic (FRPHE) material is used to model the soft tissue. A split-line pattern is used to implement the fiber orientation in femoral and tibial cartilage. Four distinct intact cartilage models and three OA models are simulated to assess the impact of the orientation of collagen fibers in a depth wise direction. The cartilage models with fibers oriented in parallel, perpendicular, and inclined to the articular surface are investigated for multiple knee kinematics and kinetics. Findings: The comparison of models with fiber orientation parallel to articulating surface for walking and running gait has the highest elastic stress and fluid pressure compared with inclined and perpendicular fiber-oriented models. Also, the maximum contact pressure is observed to be higher in the case of intact models during the walking cycle than for OA models. In contrast, the maximum contact pressure is higher during running in OA models than in intact models. Additionally, parallel-oriented models produce higher maximum stresses and fluid pressure for walking and running gait than proximal-distal-oriented models. Interestingly, during the walking cycle, the maximum contact pressure with intact models is approximately three times higher than on OA models. In contrast, the OA models exhibit higher contact pressure during the running cycle. Interpretation: Overall, the study indicates that collagen orientation is crucial for tissue responsiveness. This investigation provides insights into the development of tailored implants. © 2023 IPEMItem Automatic selection of IMFs to denoise the sEMG signals using EMD(Elsevier Ltd, 2023) Koppolu, P.K.; Chemmangat, K.Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples. © 2023 Elsevier Ltd