Faculty Publications

Now showing 1 - 4 of 4

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.
Fully automatic ROI extraction and edge-based segmentation of radius and ulna bones from hand radiographs
(PWN-Polish Scientific Publishers bbe@ibib.waw.pl, 2017) Simu, S.; Lal, S.; Nagarsekar, P.; Naik, A.
Bone age is a reliable measure of person's growth and maturation of skeleton. The difference between chronological age and bone age indicates presence of endocrinological problems. The automated bone age assessment system (ABAA) based on Tanner and Whitehouse method (TW3) requires monitoring the growth of radius, ulna and short bones (phalanges) of left hand. In this paper, a detailed analysis of two bones in the bone age assessment system namely, radius and ulna is presented. We propose an automatic extraction method for the region of interest (ROI) of radius and ulna bones from a left hand radiograph (RUROI). We also propose an improved edge-based segmentation technique for those bones. Quantitative and qualitative results of the proposed segmentation technique are evaluated and compared with other state-of-the-art segmentation techniques. Medical experts have also validated the qualitative results of proposed segmentation technique. Experimental results reveal that these proposed techniques provide better segmentation accuracy as compared to the other state-of-the-art segmentation techniques. © 2017 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
A study about evolutionary and non-evolutionary segmentation techniques on hand radiographs for bone age assessment
(Elsevier Ltd, 2017) Simu, S.; Lal, S.
In this paper, a study and performance comparison of various evolutionary and non-evolutionary segmentation techniques on digital hand radiographs for bone age assessment is presented. The segmented hand bones are of vital importance in process of automated bone age assessment (ABAA). Bone age assessment is a technique of checking the skeletal development and detecting growth disorder in a person. However, it is very difficult to segment out the bone from the soft tissue. The problem arises from overlapping pixel intensities between bone region and soft tissue region and also between soft tissue region and background. Thus there is a requirement for a robust segmentation technique for hand bone segmentation. Taking this into consideration we make a comparison between non-evolutionary and evolutionary segmentation algorithms implemented on hand radiographs to recognize bone borders and shapes. The simulation and experimental results demonstrate that multiplicative intrinsic component optimization (MICO) algorithm provides better results as compared to other existing evolutionary and non-evolutionary algorithms. © 2016 Elsevier Ltd
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 Ltd

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