Search

This paper describes a method for decomposing a signal into the sum of an oscillatory component and a transient component. The process uses the tunable Q-factor wavelet transform (TQWT): The oscillatory component is modeled as a signal that can be sparsely denoted by high Q-factor TQWT; similarly, the transient component is modeled as a piecewise smooth signal that can be sparsely denoted using low Q-factor TQWT. Since the low and high Q-factor TQWT has low coherence, the morphological component analysis (MCA) can effectively decompose the signal into oscillatory and transient components. The corresponding optimization problem of MCA is resolved by the split augmented Lagrangian shrinkage algorithm (SALSA). The applications of the proposed method to speech, electroencephalo-graph (EEG), and electrocardiograph (ECG) signals are included.

In the present work, EEG signals of different classes are analysed in tunable-Q wavelet transform (TQWT) framework. The TQWT decomposes the EEG signals into subbands and arrange them into decreasing order of frequency. The nonlinearity of the EEG signals is assessed by computing the centered correntropy (CCE) from the obtained subbands, which is further used as a feature for classifying the different categories of EEG signals. In this work, EEG signals are categorised in two different classification problems. First category is seizure free and seizure (NF-S) classes, and the other one is the normal, seizure free and seizure (ZO-NF-S) classes. Features obtained from the EEG signals of these classes are fed to the input of three different classifiers namely, random forest classifier (RF), multilayer perceptron (MLP) classifier, and logistic regression (LR) classifier. For NF-S classes, we achieved 98.3% classification accuracy with RF classifier for signal length of 1000 samples. The obtained accuracy of classification is 98.2% for ZO-NF-S classes using MLP classifier when features are extracted from signal length of 1000 samples.

Convolutional Neural Networks have demonstrated exceptional effectiveness in the field of medical image segmentation by effectively capturing intricate local details such as edges and textures. But still, their limited domain of view often impedes comprehensive representation of global information. Transformers, on the other hand, have shown promise in modeling long-range dependencies, yet, Convolutional Neural Networks occasionally face challenges in effectively capturing high-level spatial features. An ideal segmentation model ought to effectively harness both local and global features to achieve precision and semantic accuracy. This article introduces a novel Cross Shaped Window Transformer framework, employing U-shaped network architecture. This network combines a Convolutional Neural Network encoder with a Multi-Head Self-Attention based CNN decoder. Within the CNN encoder, a transformer path is integrated with a shifted window mechanism, enhancing the representation of both local and global information, thus ensuring robust medical image segmentation. The encoder's skip connections are reinstated using a Multi-Head Self-Attention decoder. To decode a wide range of features and manage distortions in local details, a dilated-Uper decoder is introduced. The Synapse dataset is utilized to assess the effectiveness of the proposed method, revealing that it surpasses existing approaches with an impressive accuracy of approximately 93%. [ABSTRACT FROM AUTHOR]

Worldwide, glaucoma is a type of eye disease, which causes loss of vision by damaging optic nerves within the eye. Available glaucoma detection techniques are less accurate. This paper proposed a computer-based glaucoma detection using second-stage quasi-bivariate variational mode decomposition (SS-QB-VMD)-based fine sub-band images (SBIs) from fundus images. The preprocessed images are decomposed into five SBIs at the first stage using QB-VMD. The high-frequency fifth SBI obtained using the first stage is further decomposed into five SBIs using QB-VMD at the second stage. At the second stage, the decomposed SBIs are more discriminating than obtained at the first stage. These decomposed SBIs at second stages are fine with no mode mixing problems. These are finer and help to mine more useful detailed information to detect glaucoma more accurately. Texture features are extracted from decomposed SBIs. Extracted texture features are normalized using the z-score method and classified using the support vector machine classifier. The obtained accuracy, sensitivity, specificity, precision, dice value, and Jaccard index value are 92.67%, 91.43%, 93.75%, 94.03%, 91.98%, and 86.1%, for tenfold cross-validation, respectively. The results demonstrated that the proposed method is better compared to the existing methods for same images. It can be used to detect glaucoma more accurately. [ABSTRACT FROM AUTHOR]

The present study is a retrospective study conducted in Raichur institute of Medical sciences, Raichur Karnataka. During the study period that is from January 2017 to March 2017, out of 547 medico-legal cases which came to casualty, 164 cases were poisoning. Among 164 poisoning cases, 13 cases died in hospital and postmortem examination is done in mortuary RIMS Raichur hospital. The most common type of poisoning case in and around the Raichur district was organo-phosphorus poisoning (58.53%) followed by kerosene poisoning (13.41%). Males (55.48%) were the more common victims of the poisoning. Age group between 11-20 years (32.92%) was most commonly affected. Majority of poisoning cases was suicidal (84.14%) in manner. In 26.56% the motive for suicidal poisoning was financial crisis leading to depression and consumption of poisonous substance. [ABSTRACT FROM AUTHOR]

A new time-varying autoregressive (TVAR) modeling approach is proposed for non-stationary signal processing and analysis. In the new parametric modeling frame work, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The realization of the time-varying AR(TVAR)model here is distinguished from existing time-varying parametric models where the relevant time-dependent coefficients are represented using basis function expansions. In most existing time-varying parametric models, the basis functions used for representing the time-dependent coefficients are global, while the basis functions involved in the new proposed modeling approach are locally defined. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. The associated timevarying coefficients are then estimated by using a Orthogonal least square (OLS) Algorithm. Simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel technique for Instantaneous Frequency (IF) estimation of multi component non stationary signals using Fourier Bessel Series and Time- Varying Auto Regressive (FB-TVAR) model. In the proposed technique, the Fourier-Bessel (FB) expansion decomposes the multi-component non stationary signal into a number of monocomponent signals and TVAR model is used to model each mono-component signal. In TVAR modeling approach the time varying parameters are expanded as a linear combination of basis functions. In this paper, the TVAR parameters are expanded by a discrete cosine basis functions. The maximum likelihood estimation algorithm for model order selection in TVAR models is also discussed. The Instantaneous Frequency (IF) is extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. The estimation of the TVAR parameters of a multicomponent signal requires the inversion of a large covariance matrix, while the projected technique (FB-TVAR) requires the inversion of a number of comparatively small covariance matrices with better numerical stability properties. Simulation results are presented for Multi component discrete Amplitude and Frequency modulated (AM-FM) signal. [ABSTRACT FROM AUTHOR]

BACKGROUND: Type-2 diabetes mellitus (DM) is the most challenging public health problem. Most of the people are unaware of the disease and its preventive measures and complications. It is important to acknowledge the gap in the knowledge and awareness of the disease to prevent the disease and effectively plan public health care. METHODOLOGY: This was a cross-sectional study with the objective to assess the level of knowledge and awareness regarding type-2 DM and its complications among urban and rural population and to compare them. The sample size was calculated as 404 among >30 years of age with Type-2 DM with >1 year of duration with or without complications. Data was collected on demographic details, knowledge, awareness, complications and lifestyle modifications by using the structured questionnaire. The collected data was compared among urban and rural patients and the association was seen between different factors by appropriate statistical test. RESULTS: The knowledge on normal blood sugar was (45.6% and 27.2%) in urban and rural, HbA1C (63.8% and 49.5%), family history of diabetes (59.4% and 43.1%), whether DM2 is a multi system disorder, monitoring of blood sugar levels and frequency of monitoring and checkups were found to be more in the urban compare to the rural with P < 0.05. The awareness on complications like the rate of complications with increase in time (66.3% and 57.0%), awareness of diabetic foot (52.9% and 37.1%), prone to infections (67.3% and 58.9%), prone to COVID 19 infection (74.7% and 64.9%), most common organ involved as the complication arise was kidney (39.6% and 33.7%) with P < 0.05. CONCLUSION: The study suggests that there is a need to improve knowledge about DM and its complications, and there is a need for diabetes-specific health awareness campaigns on behavior and lifestyle changes, adherence to treatment and self-care.

Background: Aortic root dimension varies on the basis of age, body surface area, ethnicity, and in pathological states such as bicuspid aortic valve and connective tissue disorders. Aim: This observational study has been done to determine aortic root dimension in normal Indian population. In this study, aortic root dimensions were measured by two-dimensional (2D) echocardiography in parasternal long axis (PLAX) view by inner edge to inner edge method. Materials and Methods: Clinically normal Indian population was prospectively recruited. All participants were asymptomatic. All echocardiographic measurements were done twice and image acquired by single observer on the same machine. Aortic root dimensions measurements were done with 2D echo in PLAX view at 4-level aortic annulus, sinus of Valsalva, sinotubular junction, and proximal ascending aorta. Results: Aortic annulus does not show any significant correlation with age. Age parameter shows a significant correlation with the ascending aorta and sinus, sino-tubular (ST) junction. Weight parameter shows a significant correlation with annulus, sinus, and ST junction. Height parameter shows a significant correlation with all aortic dimensions. Conclusion: Indian patient has different body surface area than the Western population; therefore, there is a need of standard normal aortogram dimension for normal Indian population.

Introduction: Atrial Septal Defect by virtue of its left to right shunt induces geometric and electrical remodeling of the right sided chambers of the heart which produces prototypical changes in the Electrocardiogram. Various patterns produced by ASD in the ECG can give a clue in diagnosing the defect. Reversal of remodeling after the shunt closure and its accompanying changes in ECG are also helpful in follow up of these patients Methodology: This is a retrospective cohort analysis, wherein, ECG of the patients who have undergone ASD closure by percutaneous device closure or surgical closure during the study period,were obtained from hospital records and they were followed up. After screening, 60 patients met the inclusion criteria and were included in the study. ECG of the patients before closure of ASD and on immediate post procedure day, 2 weeks, 1month, 3 months, 6 months and 1year were obtained and analyzed., Results: Mean age of the study population was 28.4 ± 09 years. In analysis of ECG after closure of ASD, the mean QRS axis reduced from 108 ± 10 to 70 ±12 degrees (P = 0.016), mean QRS duration reduced from 123 ± 30 ms to 80 ± 27 ms (P= 0.28). Mean PR interval reduced from 188 ± 30 ms to 123 ± 24 ms (P value= 0.09). The Crochetage Sign disappeared in the 2 patients who had it before closure of the defect. The amplitude of R' wave in rSR' in right precordial leads reduced over 2 years. rR' and qR pattern in right precordial leads reverted to rSr' pattern in 10 out of 12 patients. The other 2 patients who did not show reversal had residual shunt. No clinically significant complete heart block was observed in any patient., Conclusion: ECG forms as an easily available tool for diagnosis of ASD as well as in assessing the success of closure in the follow up after the closure of ASD by surgical or percutaneous device therapy. Failure of reversal of electrocardiographic signs of ASD after the closure should prompt search for residual shunt or pulmonary complications., (© Journal of the Association of Physicians of India 2011.)