Sound signature for identification of tracheal collapse and laryngeal paralysis in dogs

Neural network analysis of pharyngeal sounds can detect obstructive upper respiratory disease in brachycephalic dogs

Brachycephalic obstructive airway syndrome (BOAS) is a highly prevalent respiratory disease affecting popular short-faced dog breeds such as Pugs and French bulldogs. BOAS causes significant morbidity, leading to poor exercise tolerance, sleep disorders and a shortened lifespan. Despite its severity, the disease is commonly missed by owners or disregarded by veterinary practitioners. A key clinical sign of BOAS is stertor, a low-frequency snoring sound. In recent years, a functional grading scheme has been introduced to semi-objectively grade BOAS based on the presence of stertor and other abnormal signs. However, correctly grading stertor requires significant experience and adding an objective component would aid accuracy and repeatability. This study proposes a recurrent neural network model to automatically detect and grade stertor in laryngeal electronic stethoscope recordings. The model is developed using a novel dataset of 665 labelled recordings taken from 341 dogs with diverse BOAS clinical signs. Evaluated via nested cross validation, the neural network predicts the presence of clinically significant BOAS with an area under the receiving operating characteristic of 0.85, an operating sensitivity of 71% and a specificity of 86%. The algorithm could enable widespread screening for BOAS to be conducted by both owners and veterinarians, improving treatment and breeding decisions.

Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems

Automatic detection of pig wasting diseases is an important issue in the management of group-housed pigs. Further, respiratory diseases are one of the main causes of mortality among pigs and loss of productivity in intensive pig farming. In this study, we propose an efficient data mining solution for the detection and recognition of pig wasting diseases using sound data in audio surveillance systems. In this method, we extract the Mel Frequency Cepstrum Coefficients (MFCC) from sound data with an automatic pig sound acquisition process, and use a hierarchical two-level structure: the Support Vector Data Description (SVDD) and the Sparse Representation Classifier (SRC) as an early anomaly detector and a respiratory disease classifier, respectively. Our experimental results show that this new method can be used to detect pig wasting diseases both economically (even a cheap microphone can be used) and accurately (94% detection and 91% classification accuracy), either as a standalone solution or to complement known methods to obtain a more accurate solution.

Laryngeal paralysis in dogs: An update on recent knowledge

Laryngeal paralysis is the effect of an inability to abduct the arytenoid cartilages duringinspiration, resulting in respiratory signs consistent with partial airway obstruction. Theaetiology of the disease can be congenital (hereditary laryngeal paralysis or congenitalpolyneuropathy), or acquired (trauma, neoplasia, polyneuropathy, endocrinopathy). Themost common form of acquired laryngeal paralysis (LP) is typically seen in old, large breeddogs and is a clinical manifestation of a generalised peripheral polyneuropathy recentlyreferred to as geriatric onset laryngeal paralysis polyneuropathy. Diagnosing LP based onclinical signs, breed and history has a very high sensitivity (90%) and can be confirmed bylaryngeal inspection. Prognosis after surgical correction depends on the aetiology: traumaticcases have a good prognosis, whereas tumour-induced or polyneuropathy-induced LP has aguarded prognosis. Acquired idiopathic LP is a slow progressive disease, with dogs reachingmedian survival times of 3–5 years after surgical correction.

Approach to the respiratory patient

Several challenges arise when evaluating a dog or cat with respiratory disease. The history can span a long period, and some owners may have a difficult time in recognizing or describing respiratory abnormalities. A good history and thorough physical examination are essential when evaluating the respiratory patient. There are some noninvasive diagnostics that can aid in the diagnosis of respiratory disease; however, other more invasive tests often require anesthesia, which can be a potential hazard with a respiratory patient. This article focuses on reviewing the function of the respiratory system and how best to identify and diagnose cats and dogs with respiratory disease by implementing a thorough history and physical examination as well as appropriate diagnostic testing.