Respiratory Sound Analysis for the Evidence of Lung Health

Significant changes have been made on audio-based technologies over years in several different fields along with healthcare industry. Analysis of Lung sounds is a potential source of noninvasive, quantitative information along with additional objective on the status of the pulmonary system. To do that medical professionals listen to sounds heard over the chest wall at different positions with a stethoscope which is known as auscultation and is important in diagnosing respiratory diseases. At times, possibility of inaccurate interpretation of respiratory sounds happens because of clinician’s lack of considerable expertise or sometimes trainees such as interns and residents misidentify respiratory sounds. We have built a tool to distinguish healthy respiratory sound from non-healthy ones that come from respiratory infection carrying patients. The audio clips were characterized using Linear Predictive Cepstral Coefficient (LPCC)-based features and the highest possible accuracy of 99.22% was obtained with a Multi-Layer Perceptron (MLP)- based classifier on the publicly available ICBHI17 respiratory sounds dataset [1] of size 6800+ clips. The system also outperformed established works in literature and other machine learning techniques. In future we will try to use larger dataset with other acoustic techniques along with deep learning-based approaches and try to identify the nature and severity of infection using respiratory sounds

Automatic lung health screening using respiratory sounds

Background: As rapid growth of respiratory diseases is witnessed around the world; medical research field has gained interest in integrating audio signal analysis-based technique which aids timely diagnosis of respiratory ailments effortlessly in early stages of a respiratory dysfunction. Method: Our tool [1] distinguishes adventitious respiratory sounds using Linear Predictive Cepstral Coefficient (LPCC)-based feature [2] along with a Multi-Layer Perceptron (MLP)-based classifier. Results: When we applied our model on the largest publicly available ICBHI17 dataset [2] of size 6800+ clips we obtained the highest possible accuracy of 99.22% with an AUC value of 0.9993. Conclusion: Our results (accuracy 99.22%) outperformed established works in literature and other machine learning techniques and in future we will extend our research to identify nature and severity of infection just from the breath sounds

Effectiveness of daily directly observed treatment, short-course regimen among patients registered for treatment at an urban primary health center in Bengaluru

Background: Tuberculosis (TB) is a major public health problem in India with high morbidity and mortality. As per the World Health Organization guidelines, the Revised National Tuberculosis Control Program introduced daily directly observed treatment, short-course (DOTS) regimen with a fixed-dose combination with weight bands. This study was undertaken to compare the effectiveness of daily DOTS regimen with intermittent regimen and to assess the proportion of adverse drug reactions in both groups. Materials and Methods: A descriptive study was conducted at a peripheral health institute under one of the TB Units in South Bengaluru. Participants registered for treatment during the third and fourth quarter of 2017 were selected using continuous sampling. Data were collected by case record analysis, structured interviews, and telephonic follow-up. Results: The study included 81 participants, with the mean age of 40 ± 16.1 years. Majority of the study participants 55 (67.9%) were male, and majority (38 [46.9%]) belonged to the upper-lower class. Forty-two (51.8%) of the study participants were on intermittent regimen, and 39 (48.1%) were on daily DOTS regimen. There was 100% sputum conversion at the end of treatment under both treatment regimens. A total of 36 (85.7%) participants under intermittent regimen and nine (23%) under daily regimen developed one or the other adverse drug reactions. The treatment success for participants under intermittent regimen was 38 (90.47%) and that for daily regimen was 35 (89.74%). However, there was no statistically significant difference between the two groups. Conclusion: Both daily and intermittent DOTS regimens are equally effective in TB treatment, but adverse drug reactions were more common with the intermittent regimen