Extrathoracic airway hyperresponsiveness as a mechanism of post infectious cough: case report

Post-infectious cough is a common diagnosis in people with chronic cough. However, the specific infectious aetiology and cough mechanisms are seldom identified

Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device

There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient aid. This article presents an evaluation of compressive sensing techniques in an accelerometer-based intelligent fall detection system modelled on a wearable Shimmer biomedical embedded computing device with Matlab. The presented fall detection system utilises a database of fall and activities of daily living signals evaluated with discrete wavelet transforms and principal component analysis to obtain binary tree classifiers for fall evaluation. 14 test subjects undertook various fall and activities of daily living experiments with a Shimmer device to generate data for principal component analysis-based fall classifiers and evaluate the proposed fall analysis system. The presented system obtains highly accurate fall detection results, demonstrating significant advantages in comparison with the thresholding method presented. Additionally, the presented approach offers advantageous fall diagnostic information. Furthermore, transmitted data accounts for over 80% battery current usage of the Shimmer device, hence it is critical the acceleration data is reduced to increase transmission efficiency and in-turn improve battery usage performance. Various Matching pursuit-based compressive sensing techniques have been utilised to significantly reduce acceleration information required for transmission.Scopu

Chronic cough and laryngeal dysfunction improve with specific treatment of cough and paradoxical vocal fold movement

Rationale: Chronic persistent cough can be associated with laryngeal dysfunction that leads to symptoms such as dysphonia, sensory hyperresponsiveness to capsaicin, and motor dysfunction with paradoxical vocal fold movement and variable extrathoracic airflow obstruction (reduced inspiratory airflow). Successful therapy of chronic persistent cough improves symptoms and sensory hyperresponsiveness. The effects of treatment for chronic cough on laryngeal dysfunction are not known. Objective: The aim of this study was to investigate effects of therapy for chronic cough and paradoxical vocal fold movement. Methods: Adults with chronic cough (n = 24) were assessed before and after treatment for chronic persistent cough by measuring quality of life, extrathoracic airway hyperresponsiveness to hypertonic saline provocation, capsaicin cough reflex hypersensitivity and fibreoptic laryngoscopy to observe paradoxical vocal fold movement. Subjects with chronic cough were classified into those with (n = 14) or without (n = 10) paradoxical vocal fold movement based on direct observation at laryngoscopy. Results: Following treatment there was a significant improvement in cough related quality of life and cough reflex sensitivity in both groups. Subjects with chronic cough and paradoxical vocal fold movement also had additional improvements in extrathoracic airway hyperresponsiveness and paradoxical vocal fold movement. The degree of improvement in cough reflex sensitivity correlated with the improvement in extrathoracic airway hyperresponsiveness. Conclusion: Laryngeal dysfunction is common in chronic persistent cough, where it is manifest as paradoxical vocal fold movement and extrathoracic airway hyperresponsiveness. Successful treatment for chronic persistent cough leads to improvements in these features of laryngeal dysfunction

Hardware/software co-design of fractal features based fall detection system

Falls are a leading cause of death in older adults and result in high levels of mortality, morbidity and immobility. Fall Detection Systems (FDS) are imperative for timely medical aid and have been known to reduce death rate by 80%. We propose a novel wearable sensor FDS which exploits fractal dynamics of fall accelerometer signals. Fractal dynamics can be used as an irregularity measure of signals and our work shows that it is a key discriminant for classification of falls from other activities of life. We design, implement and evaluate a hardware feature accelerator for computation of fractal features through multi-level wavelet transform on a reconfigurable embedded System on Chip, Zynq device for evaluating wearable accelerometer sensors. The proposed FDS utilises a hardware/software co-design approach with hardware accelerator for fractal features and software implementation of Linear Discriminant Analysis on an embedded ARM core for high accuracy and energy efficiency. The proposed system achieves 99.38% fall detection accuracy, 7.3× speed-up and 6.53× improvements in power consumption, compared to the software only execution with an overall performance per Watt advantage of 47.6×, while consuming low reconfigurable resources at 28.67%

Random neural network based epileptic seizure episode detection exploiting electroencephalogram signals

Epileptic seizures are caused by abnormal electrical activity in the brain that manifests itself in a variety of ways, including confusion and loss of awareness. Correct identification of epileptic seizures is critical in the treatment and management of patients with epileptic disorders. One in four patients present resistance against seizures episodes and are in dire need of detecting these critical events through continuous treatment in order to manage the specific disease. Epileptic seizures can be identified by reliably and accurately monitoring the patients’ neuro and muscle activities, cardiac activity, and oxygen saturation level using state-of-the-art sensing techniques including electroencephalograms (EEGs), electromyography (EMG), electrocardiograms (ECGs), and motion or audio/video recording that focuses on the human head and body. EEG analysis provides a prominent solution to distinguish between the signals associated with epileptic episodes and normal signals; therefore, this work aims to leverage on the latest EEG dataset using cutting-edge deep learning algorithms such as random neural network (RNN), convolutional neural network (CNN), extremely random tree (ERT), and residual neural network (ResNet) to classify multiple variants of epileptic seizures from non-seizures. The results obtained highlighted that RNN outperformed all other algorithms used and provided an overall accuracy of 97%, which was slightly improved after cross validation

Cough reflex sensitivity improves with speech language pathology management of refractory chronic cough

Rationale: Speech language pathology is an effective management intervention for chronic cough that persists despite medical treatment. The mechanism behind the improvement has not been determined but may include active cough suppression, reduced cough sensitivity or increased cough threshold from reduced laryngeal irritation. Objective measures such as cough reflex sensitivity and cough frequency could be used to determine whether the treatment response was due to reduced underlying cough sensitivity or to more deliberate control exerted by individual patients. The number of treatments required to effect a response was also assessed. Objective: The aim of this study was to investigate subjective and objective measures of cough before, during and after speech language pathology treatment for refractory chronic cough and the mechanism underlying the improvement. Methods: Adults with chronic cough (n = 17) were assessed before, during and after speech language pathology intervention for refractory chronic cough. The primary outcome measures were capsaicin cough reflex sensitivity, automated cough frequency detection and cough-related quality of life. Results: Following treatment there was a significant improvement in cough related quality of life (Median (IQR) at baseline: 13.5 (6.3) vs. post treatment: 16.9 (4.9), p = 0.002), objective cough frequency (Mean ± SD at baseline: 72.5 ± 55.8 vs. post treatment: 25 ± 27.9 coughs/hr, p = 0.009), and cough reflex sensitivity (Mean ± SD log C5 at baseline: 0.88 ± 0.48 vs. post treatment: 1.65 ± 0.88, p < 0.0001). Conclusions: This is the first study to show that speech language pathology management is an effective intervention for refractory chronic cough and that the mechanism behind the improvement is due to reduced laryngeal irritation which results in decreased cough sensitivity, decreased urge to cough and an increased cough threshold. Speech language pathology may be a useful and sustained treatment for refractory chronic cough. Trial Registration: Australian New Zealand Clinical Trials Register, ACTRN12608000284369