Exploring the Impact of a Trauma-informed Yoga and Mindfulness Curriculum for Multiple Populations: A Pilot Study.

Individuals with trauma experience negative mental health impacts and are at risk of poor cardiovascular outcomes. Unmanaged, these conditions may worsen, compromising healing and wellbeing. Yoga, particularly trauma-informed, may improve outcomes. The current pilot study explores the impact of a novel trauma-informed yoga and mindfulness curriculum on wellbeing in two parts. The first examined mental health (stress, mood) outcomes in four trauma-impacted populations: adults who were incarcerated (INC), individuals in recovery from substance use disorders (SU), veterans (VA), and vulnerable youth (YTH) assessing both the impact of individual class participation and impact of attending at least four curriculum sessions. For the subgroup of incarcerated individuals, impact by theme was examined. After curriculum sessions, stress was reduced, and mood improved. Across multiple sessions both the largest decreases in stress and greatest increase in mood occurred after participant in the first session. Further, a specific exploration of curriculum class impact by theme for participants who were incarcerated indicated no difference in impact by theme. The second part of this study explored cardiovascular outcomes for the population of those in recovery from substance use. Reductions in systolic blood pressure occurred immediately after the first curriculum session, and diastolic blood pressure reduced over three consecutive sessions.

Studying semblances of a true killer: experimental model of human ventricular fibrillation.

It is unknown whether ventricular fibrillation (VF) studied in experimental models represents in vivo human VF. First, we examined closed chest in vivo VF induced at defibrillation threshold testing (DFT) in four patients with ischemic cardiomyopathy pretransplantation. We examined VF in these same four hearts in an ex vivo human Langendorff posttransplantation. VF from DFT was compared with VF from the electrodes from a similar region in the right ventricular endocardium in the Langendorff using two parameters: the scale distribution width (extracted from continuous wavelet transform) and VF mean cycle length (CL). In a second substudy group where multielectrode phase mapping could be performed, we examined early VF intraoperatively (in vivo open chest condition) in three patients with left ventricular cardiomyopathy. We investigated early VF in the hearts of three patients in an ex vivo Langendorff and compared findings with intraoperative VF using two metrics: dominant frequency (DF) assessed by the Welch periodogram and the number of phase singularities (lasting >480 ms). Wavelet analysis (P = 0.9) and VF CL were similar between the Langendorff and the DFT groups (225 ± 13, 218 ± 24 ms; P = 0.9), indicating that wave characteristics and activation rate of VF was comparable between the two models. Intraoperative DF was slower but comparable with the Langendorff DF over the endocardium (4.6 ± 0.1, 5.0 ± 0.4 Hz; P = 0.9) and the epicardium (4.5 ± 0.2, 5.2 ± 0.4 Hz; P = 0.9). Endocardial phase singularity number (9.6 ± 5, 12.1 ± 1; P = 0.6) was lesser in number but comparable between in vivo and ex vivo VF. VF dynamics in the limited experimental human studies approximates human in vivo VF.

Efficacy of the 6-month thrice-weekly regimen in the treatment of new sputum smear-positive pulmonary tuberculosis under clinical trial conditions.

BACKGROUND: Under the Revised National Tuberculosis Control Programme of India, patients with new smear-positive pulmonary tuberculosis are treated with a thrice-weekly regimen of antitubercular drugs (2H(3)R(3)Z(3)E(3)/4H(3)R(3) [H isoniazid, R rifampicin, Z pyrazinamide and E ethambutol]) for 6 months. We conducted a retrospective analysis of the efficacy andtolerability of this regimen under clinical trial conditions in HIV-negative patients with newly diagnosed smear-positive pulmonary tuberculosis. METHODS: We retrospectively analysed the data on patients assigned to the control regimen (2H (3)R(3)Z(3)E(3)/4H(3)R(3)) in two clinical trials during 2001-06 at the National Institute for Research in Tuberculosis, Chennai, India. RESULTS: Of the 268 patients treated with this regimen, data for efficacy analysis were available for 249. At the end of treatment, of 249 patients, 238 (96%) had a favourable status. Treatment failure occurred in the remaining 11: 7 in whom the organisms were initially drug-susceptible and 4 with initial drug resistance. Of the 238 patients who had a favourable status at the end of treatment, 14 (6%) had recurrence of tuberculosis during the following 24 months. In the intention-to-treat analysis, 245 (94%) of 262 patients had a favourable status at the end of treatment. Of the 28 patients with initial drug resistance, 24 (86%) had a favourable outcome. Only 4 of these 24 patients were found to have recurrence of tuberculosis in 2 years of follow-up. Among the 221 patients initially infected with drug-susceptible organisms, drug resistance did not develop in any of the 7 patients in whom the treatment failed or the 10 who had recurrence of tuberculosis. Further, 5 of the 7 patients in whom the treatment failed continued to excrete drug-susceptible bacilli at 6 months. Adverse drug reactions were observed in 38 (14%) of the 262 patients. Only 3 (1.1%) needed a modification in the treatment. CONCLUSION: This thrice-weekly 6-month regimen of antitubercular drugs, when administered under full supervision, is associated with a high rate of favourable treatment outcomes in HIV-negative patients with newly diagnosed sputum smearpositive pulmonary tuberculosis. There are few adverse drug reactions in these patients.

Empirical mode decomposition based ECG features in classifying and tracking ventricular arrhythmias.

Ventricular arrhythmias (VA) are life-threatening pathophysiological conditions that seriously impact the normal functioning of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two well known types of VA. VF is the lethal of the VAs and could be characterized by its organizational progression over time. The success of cardiac resuscitation strongly depends on the type of VA, its evolution over time and response to therapy. Due to the time critical nature of VF, computationally efficient quantification of VAs and swift feedback are essential. This work attempted to arrive at computationally efficient and data-driven techniques based on Empirical Mode Decomposition for classifying and tracking VAs over time. The approaches are divided into two aims: (1) 'in-hospital' scenarios for characterizing the dynamics of VA episodes to assist clinicians in planning long-term therapy options, and (2) 'out-of-hospital' scenarios for providing near real-time feedback to detect/track the progression of VAs over time to assist medical personnel select/modify therapy options. Using an ECG database of 61 60-s VA segments obtained for classifying VT vs. VF and sub-classifying VF into organized VF (OVF) and disorganized VF (DVF), maximum classification accuracies of 96.7% (AUC = 0.993) and 87.2% (AUC = 0.968) were obtained for classifying VT vs. VF and OVF vs. DVF during 'in-hospital' analysis. Additionally, two near real-time approaches were presented for 'out-of-hospital' analysis where average accuracies of 71% and 73% were achieved for VT/VF and OVF/DVF classification, as well as demonstrating strong potential for monitoring VA progressions over time.

Detection of myasthenia gravis using electrooculography signals.

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder resulting from skeletal muscle weakness and fatigue. An early common symptom is fatigable weakness of the extrinsic ocular muscles; if symptoms remain confined to the ocular muscles after a few years, this is classified as ocular myasthenia gravis (OMG). Diagnosis of MG when there are mild, isolated ocular symptoms can be difficult, and currently available diagnostic techniques are insensitive, non-specific or technically cumbersome. In addition, there are no accurate biomarkers to follow severity of ocular dysfunction in MG over time. Single-fiber electromyography (SFEMG) and repetitive nerve stimulation (RNS) offers a way of detecting and measuring ocular muscle dysfunction in MG, however, challenges of these methods include a poor signal to noise ratio in quantifying eye muscle weakness especially in mild cases. This paper presents one of the attempts to use the electric potentials from the eyes or electrooculography (EOG) signals but obtained from three different forms of sleep testing to differentiate MG patients from age- and gender-matched controls. We analyzed 8 MG patients and 8 control patients and demonstrated a difference in the average eye movements detected between the groups. A classification accuracy as high as 68.8% was achieved using a linear discriminant analysis based classifier.

Modeling Current Density Maps Using Aliev-Panfilov Electrophysiological Heart Model.

Most existing studies of cardiac arrhythmia rely on surface measurements through optical or electrical mapping techniques. Current density imaging (CDI) is a method which enables us to study current pathways inside the tissue. However, this method entails implementation complexities for beating ex vivo hearts. Hence, this work presents an approach to simulate and study the current distributions in different cardiac electrophysiological states. The results are corroborated by experimental data, and they indicate that different states were distinguishable. The CDI simulations can be used for studying cardiac arrhythmias under simulation conditions which are otherwise impossible or difficult to be implemented experimentally.

Feature analysis of pathological speech signals using local discriminant bases technique.

Speech is an integral part of the human communication system. Various pathological conditions affect the vocal functions, inducing speech disorders. Acoustic parameters of speech are commonly used for the assessment of speech disorders and for monitoring the progress of the patient over the course of therapy. In the last two decades, signal-processing techniques have been successfully applied in screening speech disorders. In the paper, a novel approach is proposed to classify pathological speech signals using a local discriminant bases (LDB) algorithm and wavelet packet decompositions. The focus of the paper was to demonstrate the significance of identifying the signal subspaces that contribute to the discriminatory characteristics of normal and pathological speech signals in a computationally efficient way. Features were extracted from target subspaces for classification, and time-frequency decomposition was used to eliminate the need for segmentation of the speech signals. The technique was tested with a database of 212 speech signals (51 normal and 161 pathological) using the Daubechies wavelet (db4). Classification accuracies up to 96% were achieved for a two-group classification as normal and pathological speech signals, and 74% was achieved for a four-group classification as male normal, female normal, male pathological and female pathological signals.

Discriminative sparse coding of ECG during ventricular arrhythmias using LC-K-SVD approach.

Ventricular tachycardia (VT) and ventricular fibrillation (VF) are two major types of ventricular arrhythmias that results due to abnormalities in the electrical activation in the ventricles of the heart. VF is the lethal of the two arrhythmias, which may lead to sudden cardiac death. The treatment options for the two arrhythmias are different. Therefore, detection and characterization of the two arrhythmias is critical to choose appropriate therapy options. Due to the time-varying nature of the signal content during cardiac arrhythmias, modeling and extracting information from them using time and frequency localized functions would be ideal. To this effect, in this work, we perform discriminative sparse coding of the ECG during ventricular arrhythmia with hybrid time-frequency dictionaries using the recently introduced Label consistent K-SVD (LC-K-SVD) approach. Using 944 segments of ventricular arrhythmias extracted from 23 patients in the Malignant Ventricular Ectopy and Creighton University Tachy-Arrhythmia databases, an overall classification accuracy of 71.55% was attained with a hybrid dictionary of Gabor and symlet4 atoms. In comparison, for the same database and non-trained dictionary (i.e the original dictionary) the classification accuracy was found to be 62.71%. In addition, the modeling error using the trained dictionary from LC-K-SVD approach was found to be significantly lower to the one using the non-trained dictionary.

Wavelet-based characterization of gait signal for neurological abnormalities.

Studies conducted by the World Health Organization (WHO) indicate that over one billion suffer from neurological disorders worldwide, and lack of efficient diagnosis procedures affects their therapeutic interventions. Characterizing certain pathologies of motor control for facilitating their diagnosis can be useful in quantitatively monitoring disease progression and efficient treatment planning. As a suitable directive, we introduce a wavelet-based scheme for effective characterization of gait associated with certain neurological disorders. In addition, since the data were recorded from a dynamic process, this work also investigates the need for gait signal re-sampling prior to identification of signal markers in the presence of pathologies. To benefit automated discrimination of gait data, certain characteristic features are extracted from the wavelet-transformed signals. The performance of the proposed approach was evaluated using a database consisting of 15 Parkinson's disease (PD), 20 Huntington's disease (HD), 13 Amyotrophic lateral sclerosis (ALS) and 16 healthy control subjects, and an average classification accuracy of 85% is achieved using an unbiased cross-validation strategy. The obtained results demonstrate the potential of the proposed methodology for computer-aided diagnosis and automatic characterization of certain neurological disorders.

Early bactericidal action of pulsed exposure to rifampicin, ethambutol, isoniazid & pyrazinamide in pulmonary tuberculosis patients.

The bactericidal action of two therapeutic regimens on Mycobacterium tuberculosis was assessed by viable counts in serial sputum samples in 49 pulmonary tuberculosis patients being treated with rifampicin (R), ethambutol (Emb), isoniazid (I) and pyrazinamide (Z) together in a single dose thrice weekly (REmbIZ3) or with REmb and IZ on alternate days (REmb3IZ3alt). In both groups of patients, there was a significant reduction (P < or = 0.02) in the colony forming units (cfu) of M. tuberculosis per ml of sputum during the first two days of treatment itself. This early bactericidal action (EBA) as well as the reduction in counts during the subsequent days of treatment were similar (P > 0.2) for both REmbIZ3 and REmb3IZ3alt regimens indicating that splitting up REmbIZ into REmb on one day and IZ on the next day in short course chemotherapy (SCC) regimens may not affect the bactericidal action of the regimens.

The effects of long chain polyunsaturated fatty acids on local activation properties in dogs vulnerable to atrial fibrillation.

Marine derived long chain polyunsaturated fatty acids (PUFAs) were found to have benefits in reducing inducibility and maintenance of atrial fibrillation (AF) in a dog model. This study was conducted to evaluate the effect of PUFAs on local atrial electrical conduction properties acquired via a multi-electrode plaque sutured to the posterior wall of the left atrium of the heart in these dogs. Eleven dogs underwent simultaneous atrioventricular pacing (SAVP) for 2 weeks, and were organized into 2 groups: 5 dogs received no PUFAs (SAVP-PLACEBO), 6 dogs received Eicosapentaenoic or Docosahexaenoic acid derived from fish oils (SAVP-PUFA), where PUFAs were given for 21 days, starting 1 week prior to pacing and during the 2 week pacing period. Three features were extracted, which were the average conduction velocity, average intra atrial conduction time, and total activation time. The PUFA group had a faster average conduction velocity (0.82±0.19 m/s) than the PLACEBO group (0.47±0.21 m/s, P=0.02). Using the average conduction velocity feature, classification was performed with a linear classifier and leave-one-out method. In the SAVP-PLACEBO group, 60% of the dogs were correctly classified, and 66% of the dogs were correctly classified in SAVP-PUFA group, leading to an overall classification accuracy of 63.5%.

A novel approach to quantification of real and artifactual components of current density imaging for phantom and live heart.

Spatial distribution of injected current in a subject could be calculated and visualized through current density imaging (CDI). Calculated CDI paths however have a limited degree of accuracy due to both avoidable methodological errors and inevitable limitations dictated by MR imaging constraints. The source and impact of these limitations are scrutinized in this paper. Quantification of such limitations is an essential step prior to passing any judgment about the results especially in biomedical applications. An innovative technique along with metrics for evaluation of range of errors using baseline and phase cycle MR images is proposed in this work. The presented approach is helpful in pinpointing the local artifacts (areas for which CDI results are suspect), evaluation of global noises and artifacts and assessment of the effect of approximation algorithms on real and artifactual components. We will demonstrate how this error/reliability evaluation is applicable to interpretation of CDI results and in this framework, report the CDI results for an artificial phantom and a live pig heart in Langendorff setup. It is contended here that using this method, the inevitable trade-off between details and approximations of CDI components could be monitored which provides a great opportunity for robust interpretation of results. The proposed approach could be extended, adapted and used for statistical analysis of similar methods which aim at mapping current and impedance based on magnetic flux images obtained through MRI.

Fusion of structural and functional cardiac magnetic resonance imaging data for studying ventricular fibrillation.

Magnetic Resonance Imaging (MRI) techniques such as Current Density Imaging (CDI) and Diffusion Tensor Imaging (DTI) provide a complementing set of imaging data that can describe both the functional and structural states of biological tissues. This paper presents a Joint Independent Component Analysis (jICA) based fusion approach which can be utilized to fuse CDI and DTI data to quantify the differences between two cardiac states: Ventricular Fibrillation (VF) and Asystolic/Normal (AS/NM). Such an approach could lead to a better insight on the mechanism of VF. Fusing CDI and DTI data from 8 data sets from 6 beating porcine hearts, in effect, detects the differences between two cardiac states, qualitatively and quantitatively. This initial study demonstrates the applicability of MRI-based imaging techniques and jICA-based fusion approach in studying cardiac arrhythmias.

A flexible approach for simulating physiological signals.

Generating synthetic physiological signals using information extracted from real world physiological signals plays an important role in the field of medical device development and education. Most of the existing approaches are limited in the sense that they either focus on a particular physiological signal or lack flexibility in generating signals that mimic real world scenarios. In this paper, we present a cubic B-Spline interpolator-based flexible signal generator intended for simulating a variety of physiological signals. A simulated artifact generator (SAG) is also included in the proposed scheme to add artifacts to the physiological signals mimicking signal deviations associated with real world scenarios. In addition, the proposed method offers the ability to easily present a parametric representation to model a case-specific physiological signal. To demonstrate the ability of the proposed method, case studies on electromyogram (EMG), electro-oculogram (EOG), and electrocardiogram (ECG) during ventricular fibrillation are presented. Using a database of 20 ECG signals, the proposed approach was compared with an existing-model-based method and the results confirm the flexibility of our proposed approach as well as higher signal reproduction accuracy (a mean root mean square error improvement of 47.9% for waveform-based modeling and 4.3% for parametric-based modeling).

Automated signal pattern detection in ECG during human ventricular arrhythmias.

Ventricular arrhythmias seriously affects cardiac function. Of these arrhythmias, Ventricular fibrillation is considered as a lethal cardiac condition. Recent studies have reported that ventricular arrhythmias are not completely random and may exhibit regional spatio-temporal organizations. These organizations could be indicative of reoccurring signal patterns and might be embedded within the surface electrocardiograms (ECGs) during ventricular arrhythmias. In this work, we aim to identify such reoccurring ECG signal patterns during ventricular arrhythmias. The detection of such signal patterns and their distribution could be of help in sub-classifying the affected population for better targeted diagnosis and treatment. Our analysis on 14 ECG segments (on average 3.24 minutes per segment) obtained from the MIT-BIH ventricular arrhythmia database identified three reoccurring signal patterns. A wavelet based technique was developed for automating the pattern identification process using ECGs. The proposed method achieved automated detection accuracies of 73.3%, 75.0% and 86.6% for the proposed signal patterns.

A classification scheme for ventricular arrhythmias using wavelets analysis.

Identification and classification of ventricular arrhythmias such as rhythmic ventricular tachycardia (VT) and disorganized ventricular fibrillation (VF) are vital tasks in guiding implantable devices to deliver appropriate therapy in preventing sudden cardiac deaths. Recent studies have shown VF can exhibit strong regional organizations, which makes the overlap zone between the fast paced rhythmic VT and VF even more ambiguous. Considering that implantable cardioverter-defibrillator (ICD) are primarily rate dependent detectors of arrhythmias and that there may be patients who suffer from arrhythmias that fall in the overlap zone, it is essential to identify the degree of affinity of the arrhythmia toward VT or organized/disorganized VF. The method proposed in this work better categorizes the overlap zone using Wavelet analysis of surface ECGs. Sixty-three surface ECG signal segments from the MIT-BIH database were used to classify between VT, organized VF (OVF), and disorganized VF (DVF). A two-level binary classifier was used to first extract VT with an overall accuracy of 93.7% and then the separation between OVF and DVF with an accuracy of 80.0%. The proposed approach could assist clinicians to provide optimal therapeutic solutions for patients in the overlap zone of VT and VF.

Blind source separation in characterizing ECG pre-shock waveforms during Ventricular Fibrillation.

Ventricular Fibrillation (VF) is a cardiac arrhythmia for which the only available treatment option is defibrillation by electrical shock. Existing literature indicates that VF could be the manifestation of different sources controlling the heart with different degrees of organization. In this work we test the hypothesis that the pre-shock waveforms of successful and unsuccessful shock outcomes could be related to the number of independent sources present in these waveforms. The proposed method uses Blind Source Separation (BSS) to extract independent components in frequency direction from a pig database consisting of 20 pre-shock waveforms. The slope of the energy capture curve was used as an indicator to demonstrate the number of independent sources required to model the pre-shock waveforms. The results were also quantified by performing a linear discriminant analysis based classification achieving an overall classification accuracy of 75%. The results indicate that successful cases can be modeled with less number of independent sources compared to unsuccessful cases.

Analysis of reliability metrics and quality enhancement measures in current density imaging.

Low frequency current density imaging (LFCDI) is a magnetic resonance imaging (MRI) technique which enables calculation of current pathways within the medium of study. The induced current produces a magnetic flux which presents itself in phase images obtained through MRI scanning. A class of LFCDI challenges arises from the subject rotation requirement, which calls for reliability analysis metrics and specific image registration techniques. In this study these challenges are formulated and in light of proposed discussions, the reliability analysis of calculation of current pathways in a designed phantom and a pig heart is presented. The current passed is measured with less than 5% error for phantom, using CDI method. It is shown that Gauss's law for magnetism can be treated as reliability metric in matching the images in two orientations. For the phantom and pig heart the usefulness of image registration for mitigation of rotation errors is demonstrated. The reliability metric provides a good representation of the degree of correspondence between images in two orientations for phantom and pig heart. In our CDI experiments this metric produced values of 95% and 26%, for phantom, and 88% and 75% for pig heart, for mismatch rotations of 0 and 20 degrees respectively.

Ambiguity domain-based identification of altered gait pattern in ALS disorder.

The onset of a neurological disorder, such as amyotrophic lateral sclerosis (ALS), is so subtle that the symptoms are often overlooked, thereby ruling out the option of early detection of the abnormality. In the case of ALS, over 75% of the affected individuals often experience awkwardness when using their limbs, which alters their gait, i.e. stride and swing intervals. The aim of this work is to suitably represent the non-stationary characteristics of gait (fluctuations in stride and swing intervals) in order to facilitate discrimination between normal and ALS subjects. We define a simple-yet-representative feature vector space by exploiting the ambiguity domain (AD) to achieve efficient classification between healthy and pathological gait stride interval. The stride-to-stride fluctuations and the swing intervals of 16 healthy control and 13 ALS-affected subjects were analyzed. Three features that are representative of the gait signal characteristics were extracted from the AD-space and are fed to linear discriminant analysis and neural network classifiers, respectively. Overall, maximum accuracies of 89.2% (LDA) and 100% (NN) were obtained in classifying the ALS gait.

Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options.

Ventricular arrhythmias arise from abnormal electrical activity of the lower chambers (ventricles) of the heart. Ventricular tachycardia (VT) and ventricular fibrillation (VF) are the two major subclasses of ventricular arrhythmias. While VT has treatment options that can be performed in catheterization labs, VF is a lethal cardiac arrhythmia, often when detected the patient receives an implantable defibrillator which restores the normal heart rhythm by the application of electric shocks whenever VF is detected. The classification of these two subclasses are important in making a decision on the therapy performed. As in the case of all real world process the boundary between VT and VF is ill defined which might lead to many of the patients experiencing arrhythmias in the overlap zone (that might be predominately VT) to receive shocks by the an implantable defibrillator. There may also be a small population of patients who could be treated with anti-arrhythmic drugs or catheterization procedure if they can be diagnosed to suffer from predominately VT after objectively analyzing their intracardiac electrogram data obtained from implantable defibrillator. The proposed work attempts to arrive at a quantifiable way to scale the ventricular arrhythmias into VT, VF, and the overlap zone arrhythmias as VT-VF candidates using features extracted from the wavelet analysis of surface electrograms. This might eventually lead to an objective way of analyzing arrhythmias in the overlap zone and computing their degree of affinity towards VT or VF. A database of 24 human ventricular arrhythmia tracings obtained from the MIT-BIH arrhythmia database was analyzed and wavelet-based features that demonstrated discrimination between the VT, VF, and VT-VF groups were extracted. An overall accuracy of 75% in classifying the ventricular arrhythmias into 3 groups was achieved.