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In a general scenario, the brain images acquired from magnetic resonance imaging (MRI) may experience tilt, distorting brain MR images. The tilt experienced by the brain MR images may result in misalignment during image registration for medical applications. Manually correcting (or estimating) the tilt on a large scale is time-consuming, expensive, and needs brain anatomy expertise. Thus, there is a need for an automatic way of performing tilt correction in three orthogonal directions (X, Y, Z). The proposed work aims to correct the tilt automatically by measuring the pitch angle, yaw angle, and roll angle in X-axis, Z-axis, and Y-axis, respectively. For correction of the tilt around the Z-axis (pointing to the superior direction), image processing techniques, principal component analysis, and similarity measures are used. Also, for correction of the tilt around the X-axis (pointing to the right direction), morphological operations, and tilt correction around the Y-axis (pointing to the anterior direction), orthogonal regression is used. The proposed approach was applied to adjust the tilt observed in the T1- and T2-weighted MR images. The simulation study with the proposed algorithm yielded an error of 0.40 ± 0.09°, and it outperformed the other existing studies. The tilt angle (in degrees) obtained is ranged from 6.2 ± 3.94, 2.35 ± 2.61, and 5 ± 4.36 in X-, Z-, and Y-directions, respectively, by using the proposed algorithm. The proposed work corrects the tilt more accurately and robustly when compared with existing studies.

Background: Epilepsy is a neurological disorder which is characterised by recurrent and involuntary seizures. Magnetoencephalography (MEG) is clinically used as a presurgical tool in locating the epileptogenic zone by localising either interictal epileptic discharges (IEDs) or ictal activities. The localisation of ictal onset provides reliable and more accurate seizure onset zones rather than localising the IEDs. Ictals or seizures are presently detected during MEG analysis by manually inspecting the recorded data. This is laborious when the duration of recordings is longer. Methods: We propose a novel method which uses statistical features such as short-time permutation entropy (STPE), gradient of STPE (GSTPE), short-time energy (STE) and short-time mean (STM) extracted from the ictal and interictal MEG data of drug resistant epilepsy patients group. Since the data is heavily skewed, the RUSBoost algorithm with k-fold cross-validation is used to classify the data into ictal and interictal by using the four feature vectors. This method is further used for localising the epileptogenic region using region-specific classifications by means of the RUSBoost algorithm. Results: The accuracy obtained for seizure detection is 93.4%. The specificity and sensitivity for the same are 93%. The localisation accuracies for each lobe are in the range of 88.1-99.1%. Discussion: Through this ictus detection method, the current scenario of laborious inspection of the ictal MEG can be reduced. The proposed system, thus, can be implemented in real-time as a better and more efficient method for seizure detection and further it can prove to be highly beneficial for patients and health-care professionals during real-time MEG recording. Furthermore, the identification of the epileptogenic lobe can provide clinicians with useful insights, and a pre-cursor for source localisation.

In patients with medically refractory epilepsy, resective surgery is the mainstay of therapy to achieve seizure freedom. However, ∼20–50% of cases have intractable seizures post-surgery due to the imprecise determination of epileptogenic zone. Recent intracranial studies suggest that high frequency oscillations between 80 and 200 Hz could serve as one of the consistent epileptogenicity biomarkers for localization of the epileptogenic zone. However, these high frequency oscillations are not adopted in the clinical setting because of difficult non-invasive detection. Here, we investigated non-invasive detection and localization of high frequency oscillations and its clinical utility in accurate pre-surgical assessment and post-surgical outcome prediction. We prospectively recruited 52 patients with medically refractory epilepsy who underwent standard pre-surgical workup including magnetoencephalography (MEG) followed by resective surgery after determination of the epileptogenic zone. The post-surgical outcome was assessed after 22.14 ± 10.05 months. Interictal epileptic spikes were expertly identified, and interictal epileptic oscillations across the neural activity frequency spectrum from 8 to 200 Hz were localized using adaptive spatial filtering methods. Localization results were compared with epileptogenic zone and resected cortex for congruence assessment and validated against the clinical outcome. The concordance rate of high frequency oscillations sources (80–200 Hz) with the presumed epileptogenic zone and the resected cortex were 75.0% and 78.8%, respectively, which is superior to that of other frequency bands and standard dipole fitting methods. High frequency oscillation sources corresponding with the resected cortex, had the best sensitivity of 78.0%, positive predictive value of 100% and an accuracy of 78.84% to predict the patient’s surgical outcome, among all other frequency bands. If high frequency oscillation sources were spatially congruent with resected cortex, patients had an odds ratio of 5.67 and 82.4% probability of achieving a favourable surgical outcome. If high frequency oscillations sources were discordant with the epileptogenic zone or resection area, patient has an odds ratio of 0.18 and only 14.3% probability of achieving good outcome, and mostly tended to have an unfavourable outcome (χ(2) = 5.22; P = 0.02; φ = −0.317). In receiver operating characteristic curve analyses, only sources of high-frequency oscillations demonstrated the best sensitivity and specificity profile in determining the patient’s surgical outcome with area under the curve of 0.76, whereas other frequency bands indicate a poor predictive performance. Our study is the first non-invasive study to detect high frequency oscillations, address the efficacy of high frequency oscillations over the different neural oscillatory frequencies, localize them and clinically validate them with the post-surgical outcome in patients with medically refractory epilepsy. The evidence presented in the current study supports the fact that HFOs might significantly improve the presurgical assessment, and post-surgical outcome prediction, where it could widely be used in a clinical setting as a non-invasive biomarker.

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.

Purpose Magnetoencephalography (MEG) was used to record and localize the sources of the epileptiform discharges, in absence epilepsy, at three different time intervals to infer the sources of involvement during generation and propagation. Methods Twenty patients with absence epilepsy (M:F = 1:1; age: 10.2 ± 3.4 years), which included 12 patients with childhood absence epilepsy (CAE) and 8 patients with juvenile absence epilepsy (JAE), were recruited in this prospective MEG based study. MEG epileptiform discharges were divided into three sub-groups based on the duration viz., 1 s (very short),>1–9.9 s (short) and ≥10 s (long) and the discharges of each group were averaged independently in each patient. MEG source analysis was performed on these averaged discharges, of each of the subgroups, at the onset, during middle and offset. Results The source locations obtained, in lobar and gyri levels, were compared across these three groups of varying duration of discharges and in the CAE and JAE subjects. It was observed that the most frequent location of sources from the sublobar, limbic and frontal lobes in all the discharge groups at different time intervals. Also, it was noted that there were only subtle and variable degree of the differences of source localization of epileptic discharges among CAE and JAE subgroups. Conclusion The study provided novel findings regarding origin and propagation of sources of epileptiform discharges in patients with childhood and juvenile absence epilepsies. Such analysis further improves the understanding of network involvement of subcortical and cortical regions in these patients.

Introduction. The activating role of non–rapid eye movement (NREM) sleep on epileptic cortex and conversely, the seizure remission brought about by antiepileptic medications, has been attributed to their effects on neuronal synchrony. This study aims to understand the role of neural synchrony of NREM sleep in promoting interictal epileptiform discharges (IEDs) in patients with epilepsy (PWE) by assessing the peri-IED phase synchrony during awake and sleep states. It also studies the role played by antiepileptic drugs (AEDs) on EEG desynchronization in the above cohort. Methods. A total of 120 PWE divided into 3 groups (each n = 40; juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE]. and extratemporal lobe epilepsy [Ex-TLE]) were subjected to overnight polysomnography. Each patient group was subdivided into drug-naive and on treatment (Each n = 20). EEG phase synchronization analysis was performed to compare peri-IED phase synchronization indices (SI) during awake and sleep stages and between drug naïve and on treatment groups in 4 frequency bands, namely delta, theta, alpha, and beta. The mean ± SD of peri-IED SI among various subgroups was compared employing a multilevel mixed effects modeling approach. Results. Patients with JME had increased peri-IED cortical synchrony in N3 sleep stage, whereas patients with partial epilepsy had increased IED cortical synchrony in N1 sleep stage. On the other hand, peri-IED synchrony was lower during wake and REM sleep. We also found that peri-IED synchronization in patients with JME was higher in drug-naive patients compared with those on sodium valproate monotherapy in theta, alpha, and beta bands. Conclusion. The findings of this study suggest that sleep stages can alter cortical synchrony in patients with JME and focal epilepsy, with NREM IEDs being more synchronized and wake/REM IEDs being less synchronized. Furthermore, it also suggests that AEDs alleviate seizures in PWE by inhibiting cortical synchrony.

Specificity of ictal high-frequency oscillations (HFOs) in identifying epileptogenic abnormality is significant, compared to the spikes and interictal HFOs. The objectives of the study were to detect and to localize ictal HFOs by magnetoencephalography (MEG) for identifying the seizure onset zone (SOZ), evaluate the cortical excitability from preictal to ictal transition, and establish HFO concordance rates with other modalities and postsurgical resection.Sixty-seven patients with drug-resistant epilepsy had at least 1 spontaneous seizure each during MEG acquisition, and analysis was carried out on 20 seizures from 20 patients. Ictal MEG data were bandpass filtered (80-200 Hz) to visualize, review, and analyze the HFOs co-occurring with ictal spikes. Source montages were generated on both hemispheres, mean fast Fourier transform was computed on virtual time series for determining the preictal to ictal spectral power transition, and source reconstruction was performed with sLORETA and beamformers. The concordance rates of ictal MEG HFOs (SOZ) was estimated with 4 reference epileptogenic regions.In each subject, transient bursts of high-frequency oscillatory cycles, distinct from the background activity, were observed in the periictal continuum. Time-frequency analysis showed significant spectral power surge (85-160 Hz) during ictal state (P .05) compared to preictal state, but there was no variation in the peak HFO frequencies (P .05) for each subgroup and at each source montage. HFO source localization was consistent between algorithms (k = 0.857 ± 0.138), with presumed epileptogenic zone (EZ) comparable to other modalities. In patients who underwent surgery (n = 6), MEG HFO SOZ was concordant with the presumed EZ and the surgical resection site (100%), and all were seizure-free during follow-up.HFOs could be detected in the MEG periictal state, and its sources were accurately localized. During preictal to ictal transition, HFOs exhibited dynamic augmentation in intrinsic epileptogenicity. Spatial overlap of ictal HFO sources was consistent with EZ determinants and the surgical resection area.

Excessive cortical synchrony within neural ensembles has been implicated as an important mechanism driving epileptiform activity. The current study measures and compares background electroencephalographic (EEG) phase synchronization in patients having various types of epilepsies and healthy controls during awake and sleep stages.A total of 120 patients with epilepsy (PWE) subdivided into 3 groups (juvenile myoclonic epilepsy [JME], temporal lobe epilepsy [TLE], and extra-temporal lobe epilepsy [Ex-TLE]; n = 40 in each group) and 40 healthy controls were subjected to overnight polysomnography. EEG phase synchronization (SI) between the 8 EEG channels was assessed for delta, theta, alpha, sigma, and high beta frequency bands using ensemble measure on 10-second representative time windows and compared between patients and controls and also between awake and sleep stages. Mean ± SD of SI was compared using 2-way analysis of variance followed by pairwise comparison ( P ≤ .05).In both delta and theta bands, the SI was significantly higher in patients with JME, TLE, and Ex-TLE compared with controls, whereas in alpha, sigma, and high beta bands, SI was comparable between the groups. On comparison of SI between sleep stages, delta band: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3, whereas REM (rapid eye movement) was comparable to wake; theta band: decreased SI during N2 and increase during N3; alpha band: SI was highest in wake and lower in N1, N2, N3, and REM; and sigma and high beta bands: progressive increase in SI from wake ⇒ N1 ⇒ N2 ⇒ N3; however, sigma band showed lower SI during REM.This study found an increased background cortical synchronization in PWE compared with healthy controls in delta and theta bands during wake and sleep. This background hypersynchrony may be an important property of epileptogenic brain circuitry in PWE, which enables them to effortlessly generate a paroxysmal EEG depolarization shift.

Objective The purpose of this study is to localize the sources of epileptiform discharges (EDs), in juvenile myoclonic epilepsy (JME) using Magnetoencephalography (MEG), at three different time instances and analyze the propagation of EDs, from onset to offset, for inferring the cortical and subcortical region of involvement. Methods Twenty patients (age 23.5±6.3years old) with JME were recruited in this prospective study. MEG source analysis was performed on the independently collected EDs of each patient. The distributed source model was employed for source localization using low resolution electromagnetic brain tomography (LORETA). In each EDs, the onset (leading edge of the spike from baseline), peak and offset (trailing edge of the spike), with time window of 8ms, were subjected for source localization in order to study the propagation of the EDs. The obtained source location coordinates, from each individual MRI, were transformed in Talairach space and the distribution of region of source involvement was analysed. Results The frequency pattern of lobar distribution at onset, peak and offset respectively suggest that discharges most commonly localized at onset from sublobar region, at peak from frontal lobe and at offset from the sublobar region. It was observed that the maximum involvement of sources from the sublobar, limbic and frontal lobes at different time instances. It indicates that the restricted cortical-subcortical involvement during the generation and propagation of EDs in JME. Significance This MEG study supported the cortical-subcortical region of involvement and provided further insights in our understanding the network involvement in generation and propagation of EDs in JME.

Purpose To assess the role of P300 in patients with temporal lobe epilepsy (TLE) with unilateral hippocampal sclerosis (HS) using magnetoencephalography (MEG) based auditory and visual oddball tasks, and to assess its correlation with neuropsychological tests. Methods Thirty-patients (M:F-17:13, onset-11.77 ± 8.75 years, duration-16.10 ± 9.61 years) with TLE-HS (Left:15, Right:15) and fifteen-healthy age, gender and years of education matched controls (M:F-10:5, age-28.13 ± 4.76 years) underwent auditory and visual oddball tasks in MEG and cognition assessment using Indian Council of Medical Research (ICMR)-cognitive test battery. Independent component analysis (ICA) was applied to the magnetic evoked field responses for the detection of the P300 component. Source localization of P300 was performed with Classical LORETA Analysis Recursively Applied (CLARA). The latency and amplitude of P300 were estimated and subsequently correlated with cognitive scores. Results The visual P300 amplitude in the TLE group was lower when compared to the control group. In subgroup comparison (controls vs. right HS vs. left HS), visual P300 amplitudes were lower in the right HS group compared to both left HS and control groups (p-value = 0.014). On the other hand, no significant difference for auditory P300 latency or amplitude was noted between patients and controls as well as between subgroups. A negative correlation found between the MEG visual P300 amplitude and Indian Trial Making Test (TMT)-B duration in the patient group. Conclusion Patients with TLE-HS have decreased visual-P300 amplitude. A significant correlation found between visual P300 amplitude and cognitive tests of visuospatial attention and working memory. Overall, MEG based visual P300 amplitude can be further explored with large sample size studies to establish as a complementary objective test for cognitive assessment in TLE.

A novel methodology for ocular artifact suppression in EEG data using EEMD with PCA.The proposed method eliminates the ocular artifacts from the measured EEG without using reference electrooculogram channel.The proposed method exhibits effective suppression of ocular artifact with low distortion compared to wavelet approach. Display Omitted Signals associated with eye blinks (230-350źmicro-volts) are orders of magnitude larger than electric potentials (7-20źmicro-volts) generated on the scalp because of cortical activity. These and other such non-cortical biological artifacts spread across the scalp and contaminate the Electroencephalogram (EEG). We present here a novel approach for efficient detection and effective suppression of these artifacts using single channel EEG data by combining Ensemble Empirical Mode Decomposition (EEMD) along with Principal Component Analysis (PCA). We present a methodology for ocular artifact suppression, by performing EEMD on the contaminated EEG data segment to get the intrinsic mode functions (IMFs) and subsequent elimination of artifacts by automatic selection of particular principal components, which capture ocular artifact features after using PCA on IMFs.

To study the genesis and propagation patterns of periodic complexes (PCs) associated with myoclonic jerks in sub-acute sclerosing pan-encephalitis (SSPE) using magnetoencephalography (MEG) and electroencephalography (EEG). Simultaneous recording of MEG (306 channels) and EEG (64 channels) in five patients of SSPE (M:F = 3:2; age 10.8 ± 3.2 years; symptom-duration 6.2 ± 10 months) was carried out using Elekta Neuromag(®) TRIUX™ system. Qualitative analysis of 80-160 PCs per patient was performed. Ten isomorphic classical PCs with significant field topography per patient were analysed at the 'onset' and at 'earliest significant peak' of the burst using discrete and distributed source imaging methods. MEG background was asymmetrical in 2 and slow in 3 patients. Complexes were periodic (3) or quasi-periodic (2), occurring every 4-16 s and varied in morphology among patients. Mean source localization at onset of bursts using discrete and distributed source imaging in magnetic source imaging (MSI) was in thalami and or insula (50 and 50 %, respectively) and in electric source imaging (ESI) was also in thalami and or insula (38 and 46 %, respectively). Mean source localization at the earliest rising phase of peak in MSI was in peri-central gyrus (49 and 42 %) and in ESI it was in frontal cortex (52 and 56 %). Further analysis revealed that PCs were generated in thalami and or insula and thereafter propagated to anterolateral surface of the cortices (viz. sensori-motor cortex and frontal cortex) to same side as that of the onset. This novel MEG-EEG based case series of PCs provides newer insights for understanding the plausible generators of myoclonus in SSPE and patterns of their propagation.

A signal processing methodology is proposed in this paper for effective reconstruction of ultrasonic signals in coarse grained high scattering austenitic stainless steel. The proposed methodology is comprised of the Ensemble Empirical Mode Decomposition (EEMD) processing of ultrasonic signals and application of signal minimisation algorithm on selected Intrinsic Mode Functions (IMFs) obtained by EEMD. The methodology is applied to ultrasonic signals obtained from austenitic stainless steel specimens of different grain size, with and without defects. The influence of probe frequency and data length of a signal on EEMD decomposition is also investigated. For a particular sampling rate and probe frequency, the same range of IMFs can be used to reconstruct the ultrasonic signal, irrespective of the grain size in the range of 30-210 μm investigated in this study. This methodology is successfully employed for detection of defects in a 50mm thick coarse grain austenitic stainless steel specimens. Signal to noise ratio improvement of better than 15 dB is observed for the ultrasonic signal obtained from a 25 mm deep flat bottom hole in 200 μm grain size specimen. For ultrasonic signals obtained from defects at different depths, a minimum of 7 dB extra enhancement in SNR is achieved as compared to the sum of selected IMF approach. The application of minimisation algorithm with EEMD processed signal in the proposed methodology proves to be effective for adaptive signal reconstruction with improved signal to noise ratio. This methodology was further employed for successful imaging of defects in a B-scan.

In selected patients with drug-resistant focal epilepsies (DRFE), who otherwise are likely to be excluded from epilepsy surgery (ES) because of the absence of a magnetic resonance imaging (MRI)-demonstrable lesion or discordant anatomo-electro-clinical (AEC) data, magnetoencephalography (MEG) may help to generate an AEC hypothesis and stereo-electroencephalography (SEEG) may help to verify the hypothesis and proceed with ES. The sensitivity of MEG is much better in localizing the spiking zone in relation to lateral temporal and extratemporal cortical regions compared to the mesial temporal structures. MEG has a dominant role in the presurgical evaluation of patients with MRI-negative DRFEs, insular epilepsies, and recurrent seizures after failed epilepsy surgeries, and in guiding placement of invasive electrodes. Moreover, postoperative seizure freedom is better if MEG spike source localized cortical region is included in the resection. When compared to subdural grid electrode recording, SEEG is less invasive and safer. Those who are otherwise destined to suffer from uncontrolled seizures and their consequences, SEEG guided ES is a worthwhile and a cost-effective option. Depending on the substrate pathology, there is80-90% chance of undergoing ES and 60-80% chance of becoming seizure-free following SEEG. Recent noninvasive techniques aimed at better structural imaging, delineating brain connectivity and recording specific intracerebral EEG patterns such as high frequency oscillations might decrease the need for SEEG; but more importantly, make SEEG exploration more goal-directed and hypothesis-driven.

We report a method of inferring the dipole location using iterative nonlinear least square optimization based on Levenberg–Marquardt algorithm, wherein, we use different sets of pseudo-random numbers as initial parameter values. The method has been applied to (i) the simulated data representing the calculated magnetic field distribution produced by a point dipole placed at a known position, (ii) the experimental data from SQUID based measurements of the magnetic field distribution produced by a source coil carrying current, and (iii) the actual experimentally measured magnetocardiograms of human subjects using a SQUID based system.

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).

We describe some of the challenging aspects in the indigenous development of the whole head multichannel magnetoencephalography (MEG) system at IGCAR, Kalpakkam. These are: i) fabrication and testing of a helmet shaped sensor array holder of a polymeric material experimentally tested to be compatible with liquid helium temperatures, ii) the design and fabrication of the PCB adapter modules, keeping in mind the inter-track cross talk considerations between the electrical leads used to provide connections from SQUID at liquid helium temperature (4.2K) to the electronics at room temperature (300K) and iii) use of high resistance manganin wires for the 86 channels (86×8 leads) essential to reduce the total heat leak which, however, inevitably causes an attenuation of the SQUID output signal due to voltage drop in the leads. We have presently populated 22 of the 86 channels, which include 6 reference channels to reject the common mode noise. The whole head MEG system to cover all the lobes of the brain will be progressively assembled when other three PCB adapter modules, presently under fabrication, become available. The MEG system will be used for a variety of basic and clinical studies including localization of epileptic foci during pre-surgical mapping in collaboration with neurologists.

We present the results of experiments carried out using high resistive electrical leads for routing the SQUID output signal from the sensor at low temperatures (4.2K) to the preamplifier at room temperature. This exercise was carried out as a part of the ongoing development of a whole head multichannel magnetoencephalography (MEG) system. The conflicting requirements are the need to minimize the heat leak into the cryostat that requires the use of high resistive manganin leads and the signal transport with low loss that requires the use of low resistive copper leads. The use of high resistive leads significantly reduces the modulation depth of the SQUID as seen at the preamplifier and adversely impacts the output noise as compared to the low resistive copper leads. However, our experiments show that the use of an intermediate cooled matching transformer helps in restoring the modulation depth seen at the preamplifier and results in a vastly reduced noise even when high resistive manganin wires are used.

We report the development of a thirty seven channel SQUID based Magnetocardiography (MCG) system for the measurement of biomagnetic fields originating from the human heart. These fields are extremely weak and can be non-invasively measured only by using SQUID sensors. The system can simultaneously record biomagnetic signals at thirty seven spatial locations on the chest with a total coverage area of 300 cm2. The typical noise level of the system is measured to be about 2.5 fTrms/cm/√Hz for most gradiometer channels and around 7.3 fTrms/√Hz for magnetometer channels. The measurement of Magnetocardiogram (MCG) from human heart carried out using this system is shown.

A method is presented for reconstructing the source parameters of an equivalent current dipole assumed to be responsible for the observed magnetic field distribution. We solve the inverse problem using an iterative nonlinear least square optimization technique. Since the inverse problem is ill‐posed and its solution is non‐unique, the solution may be trapped in a local minimum if the initial values are far from the exact solution. This necessitates an estimate of the source parameters to be available as a prior knowledge for choosing the initial values. Here, we assume a set of pseudorandom numbers as the initial values for the source parameters and reconstruct the solution by imposing appropriate constraints such that the position coordinates of the dipole are within the boundary occupied by the heart. The method is shown to provide the solution with reasonable accuracy even in the presence of about 15% additive random noise in a study involving simulated data. The method is used for the analysis of magn...

We examine the propagation of ultra-short optical light pulses in dispersion-managed birefringent fiber transmission systems, in which the pulse dynamics is governed by the coupled higher-order nonlinear Schrodinger equations with higher-order linear and nonlinear optical effects. We derive the equations of motion in terms of pulse parameters such as amplitude, temporal position, width, chirp, frequency and phase, using a projection operator method, and we obtain the spatial dynamical behavior of picosecond and femtosecond pulse parameters. From our detailed analysis, we show that the stimulated Raman scattering has a strong impact on the pulse dynamics.