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Introduction: WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries., Methods: This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children's hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis., Discussion: This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia.

In regions of the world where tuberculosis (TB) poses the greatest disease burden, the lack of access to skilled laboratories is a significant problem. A lab-free method for assessing patient recovery during treatment would be of great benefit, particularly for identifying patients who may have drug-resistant tuberculosis. We hypothesize that cough analysis may provide such a test. In this paper we describe algorithm development in support of a pilot study of TB patient coughing. We describe several approaches to event detection and classification, and show preliminary data which suggest that cough count decreases after the start of treatment in drug-responsive patients. Our eventual goal is development of a low-cost ambulatory cough analysis system that will help identify patients with drug-resistant tuberculosis.

Nerve localization using peripheral nerve stimulation (PNS) is affected by tissue properties, the anatomy surrounding the nerve, and characteristics of the stimulus waveform. A better understanding of the factors influencing PNS should lead to improved nerve localization techniques for use in regional anesthesia. A finite element approach is described here that includes capacitive effects and accounts for frequency-dependent tissue properties in a computationally efficient manner. The modeling approach can be applied to other bioelectric problems where capacitive effects may be important.

An algorithm for automated removal of stimulus artifact has been developed and tested on nerve conduction study data. The algorithm uses a hardware-based model of the stimulus artifact (SA). Model parameters are estimated from portions of the data that are judged to contain only the artifact. The model can be used to remove SA even when it is temporally overlapped with the nerve signal. Data are shown to demonstrate the algorithm's performance and to quantify the effect of SA removal on clinical parameters.