A novel scheme for the selective stimulation of nerve fibers using nonlinear properties of action potentials

In this paper, we propose a novel technique for selective stimulation of nerve fibers. We show that a set of point electrodes arranged in the 3-D space around a nerve trunk can be used to systematically synthesize highly useful activation patterns within the nerve, by exploiting the spatial arrangement of the electrodes and the excitation currents. Using such activation patterns, we present a novel scheme to selectively stimulate nerve fibers, based on the nonlinear properties of action potential generation. We illustrate the developed techniques via computer simulations of a nerve trunk consisting of a large number of nerve fibers. The results indicate that the proposed technique has great potential to achieve position selective stimulation of nerve in FES.     

Automatic Identification of Wet and Dry Cough in Pediatric Patients with Respiratory Diseases

Cough is the most common symptom of several respiratory diseases. It is a defense mechanism of the body to clear the respiratory tract from foreign materials inhaled accidentally or produced internally by infections. The identification of wet and dry cough is an important clinical finding, aiding in the differential diagnosis especially in children. Wet coughs are more likely to be associated with lower respiratory track bacterial infections. At present during a typical consultation session, the wet/dry decision is based on the subjective judgment of a physician. It is not available for the non-trained person, long term monitoring or in the assessment of treatment efficacy. In this paper we address these issues and develop an automated technology to classify cough into ‘wet’ and ‘dry’ categories. We propose novel features and a Logistic regression model (LRM) for the classification of coughs into wet/dry classes. The performance of the method was evaluated on a clinical database of pediatric coughs (C = 536) recorded using a bed-side non-contact microphone from N = 78 patients. Results of the automatic classification were compared against two expert human scorers. The sensitivity and specificity of the LRM in picking wet coughs were between 87 and 88% with 95% confidence interval on training/validation dataset (310 cough events from 60 patients) and 84 and 76% respectively on prospective dataset (117 cough events from 18 patients). The kappa agreement with two expert human scorers on prospective dataset was 0.51. These results indicate the potential of the method as a useful clinical tool for cough monitoring, especially at home settings.     

Inter-hemispheric asynchrony of the brain during events of apnoea and EEG arousals

Sleep apnoea hypopnea syndrome (SAHS) is a serious sleep disorder affecting a large percentage of the population. Apnoea/hypopnea and electroencephalographic-arousal (EEGA) events occur frequently in SAHS patients. These events significantly disturb the sleep architecture, as revealed through nocturnal EEG signals. Even though EEG carries vital information on the state of the brain, its use in clinical SAHS diagnosis is limited mainly to routine sleep staging. In this paper, we address this issue. We propose a novel measure, called the inter-hemispheric asynchrony (Psi(a-->b)), to capture EEG-symmetry changes associated with a transition a --> b between the brain states 'a' and 'b'. Our work takes into account macro-states such as the traditional sleep stages, and micro-states such as EEGA and apnoea/hypopnea events. We measured EEG data using electrodes C4-A2 and C3-A1 of the International 10/20 System from 18 subjects undergoing polysomnography (PSG) testing. These electrode pairs are symmetrical about the brain mid-line and allow us to discern any hemispheric EEG asymmetry. EEG data were segmented and filtered into classical bands delta(0.5-4 Hz), theta(4.1-8 Hz), alpha(8.1-12 Hz) and beta(12.1-16 Hz). Then they were further categorized according to the particular sleep state of their origin. Spectral correlation coefficients were computed between the EEG data from the two hemispheres and averaged over the overnight EEG recording. This was done for each frequency band and state of interest, and then the measure Psi(a-->b) was computed. Results from the 18 subjects showed that Psi(a-->b) increased significantly (p < 0.05) when the sleep state changed from NREM to REM, in all the frequency bands considered. Similarly, within both NREM and REM macro-states, Psi(a-->b) changes significantly (p < 0.1) with micro-state changes from the background state towards apnoea/hypopnea and EEGA states. Extensive statistical analysis we conducted with the 18 subjects indicated that the measure Psi(a-->b) provides a novel insight into the functional asymmetry of the brain during SAHS events.     

Identification of mycobacteria and other acid fast organisms associated with pulmonary disease

ObjectiveTo identify Mycobacterium tuberculosis (M. tuberculosis) and other acid fast organisms isolated from sputum of HIV positive adult patients with pulmonary disease in Jos, Nigeria.MethodsAcid fast organisms isolated from 80 acid fast bacilli (AFB) positive sputa of HIV positive adult patients suspected for tuberculosis in Jos, Nigeria were identified for members of M. tuberculosis complex (M. tuberculosis, Mycobacterium bovis, Mycobacterium africanum, Mycobacterium canetti, Mycobacterium microti and Mycobacterium caprae) by use of spoligootyping, Multiplex Gen Probe, Hain genotype assay and gene sequencing for spoligotype negative isolates.ResultsSeven different spoligotypes of M. tuberculosis complex were identified from 70/80 (87.5%) total number of isolates. Mycobacterium kansasii (1), Mycobacterium dulvalii (1) Nocardia species (1) and Tsukamurella species (2) were detected from 5/10 spoligotype negative isolates.ConclusionsAlthough M. tuberculosis is the dominant AFB associated with chronic pulmonary disease in Jos, Nigeria, other clinically relevant mycobacteria were also observed in the study. This suggests that other AFB positive microorganisms associated with tuberculosis-like symptoms might be misdiagnosed and incorrectly treated as M. tuberculosis. It is therefore necessary for laboratories in tuberculosis high burden countries to step up diagnostic procedures beyond routine smear microscopy.     

Behavioural and emotional symptoms of preschool children with cerebral palsy: a population‐based study

Aim To describe behavioural and emotional symptoms among Icelandic preschool children with cerebral palsy (CP). Method Children with congenital CP, assessed with the Child Behavior Checklist/1½–5 (CBCL/1½–5) and Caregiver‐Teacher Report Form (C‐TRF), were enrolled in the study. A comparison group was recruited from the general population. Thirty‐six children (53% males) with CP were assessed at a mean age of 4 years 11 months (SD 5mo, range 4–6y); 26 (72%) had bilateral distribution of symptoms and 32 (89%) had spastic CP. Thirty (83%) were at Gross Motor Function Classification System levels I or II and six at levels III or IV. For comparison, 110 (43% males) and 120 (48% males) children were assessed with the CBCL/1½–5 and the C‐TRF respectively, at a mean age of 4 years 6 months (SD 6mo, range 4–6y). Results Sixteen children (48%) with CP had high scores on total problems scale of the CBCL/1½–5 and 20 (65%) on the C‐TRF compared with 18% of the comparison group, both on the CBCL/1½–5 and the C‐TRF (p<0.001). Children with CP had higher scores on all subscales of the CBCL/1½–5 and the C‐TRF, except somatic complaints. Attention difficulties, withdrawn, aggressive behaviour, and anxious/depressed symptoms were most pronounced among children with CP. Interpretation A large proportion of preschool children with CP have substantial behavioural and emotional difficulties, which need to be addressed in their treatment.     

Could formant frequencies of snore signals be an alternative means for the diagnosis of obstructive sleep apnea?

ObjectiveTo study the feasibility of using acoustic signatures in snore signals for the diagnosis of obstructive sleep apnea (OSA).MethodsSnoring sounds of 30 apneic snorers (24 males; 6 females; apnea–hypopnea index, AHI = 46.9 ± 25.7 events/h) and 10 benign snorers (6 males; 4 females; AHI = 4.6 ± 3.4 events/h) were captured in a sleep laboratory. The recorded snore signals were preprocessed to remove noise, and subsequently, modeled using a linear predictive coding (LPC) technique. Formant frequencies (F1, F2, and F3) were extracted from the LPC spectrum for analysis. The accuracy of this approach was assessed using receiver operating characteristic curves and notched box plots. The relationship between AHI and F1 was further explored via regression analysis.ResultsQuantitative differences in formant frequencies between apneic and benign snores are found in same- or both-gender snorers. Apneic snores exhibit higher formant frequencies than benign snores, especially F1, which can be related to the pathology of OSA. This study yields a sensitivity of 88%, a specificity of 82%, and a threshold value of F1 = 470 Hz that best differentiate apneic snorers from benign snorers (both gender combined).ConclusionAcoustic signatures in snore signals carry information for OSA diagnosis, and snore-based analysis might potentially be a non-invasive and inexpensive diagnostic approach for mass screening of OSA.