Detection of tuberculosis by automatic cough sound analysis.

OBJECTIVE: Globally, tuberculosis (TB) remains one of the most deadly diseases. Although several effective diagnosis methods exist, in lower income countries clinics may not be in a position to afford expensive equipment and employ the trained experts needed to interpret results. In these situations, symptoms including cough are commonly used to identify patients for testing. However, self-reported cough has suboptimal sensitivity and specificity, which may be improved by digital detection. APPROACH: This study investigates a simple and easily applied method for TB screening based on the automatic analysis of coughing sounds. A database of cough audio recordings was collected and used to develop statistical classifiers. MAIN RESULTS: These classifiers use short-term spectral information to automatically distinguish between the coughs of TB positive patients and healthy controls with an accuracy of 78% and an AUC of 0.95. When a set of five clinical measurements is available in addition to the audio, this accuracy improves to 82%. By choosing an appropriate decision threshold, the system can achieve a sensitivity of 95% at a specificity of approximately 72%. The experiments suggest that the classifiers are using some spectral information that is not perceivable by the human auditory system, and that certain frequencies are more useful for classification than others. SIGNIFICANCE: We conclude that automatic classification of coughing sounds may represent a viable low-cost and low-complexity screening method for TB.

Automatic detection, segmentation and assessment of snoring from ambient acoustic data.

Snoring is a prevalent condition with a variety of negative social effects and associated health problems. Treatments, both surgical and therapeutic, have been developed, but the objective non-invasive monitoring of their success remains problematic. We present a method which allows the automatic monitoring of snoring characteristics, such as intensity and frequency, from audio data captured via a freestanding microphone. This represents a simple and portable diagnostic alternative to polysomnography. Our system is based on methods that have proved effective in the field of speech recognition. Hidden Markov models (HMMs) were employed as basic elements with which to model different types of sound by means of spectrally based features. This allows periods of snoring to be identified, while rejecting silence, breathing and other sounds. Training and test data were gathered from six subjects, and annotated appropriately. The system was tested by requiring it to automatically classify snoring sounds in new audio recordings and then comparing the result with manually obtained annotations. We found that our system was able to correctly identify snores with 82-89% accuracy, despite the small size of the training set. We could further demonstrate how this segmentation can be used to measure the snoring intensity, snoring frequency and snoring index. We conclude that a system based on hidden Markov models and spectrally based features is effective in the automatic detection and monitoring of snoring from audio data.