Battling with the low-resource condition for snore sound recognition: introducing a meta-learning strategy

Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA is also closely associated with various life-threatening diseases such as sudden cardiac arrest and is regarded as a grave medical ailment. Preliminary studies have shown that in the USA, OSA affects over 34 % of men and 14 % of women. In recent years, polysomnography has increasingly been used to diagnose OSA. However, due to its drawbacks such as being time-consuming and costly, intelligent audio analysis of snoring has emerged as an alternative method. Considering the higher demand for identifying the excitation location of snoring in clinical practice, we utilised the Munich-Passau Snore Sound Corpus (MPSSC) snoring database which classifies the snoring excitation location into four categories. Nonetheless, the problem of small samples remains in the MPSSC database due to factors such as privacy concerns and difficulties in accurate labelling. In fact, accurately labelled medical data that can be used for machine learning is often scarce, especially for rare diseases. In view of this, Model-Agnostic Meta-Learning (MAML), a small sample method based on meta-learning, is used to classify snore signals with less resources in this work. The experimental results indicate that even when using only the ESC-50 dataset (non-snoring sound signals) as the data for meta-training, we are able to achieve an unweighted average recall of 60.2 % on the test dataset after fine-tuning on just 36 instances of snoring from the development part of the MPSSC dataset. While our results only exceed the baseline by 4.4 %, they still demonstrate that even with fine-tuning on a few instances of snoring, our model can outperform the baseline. This implies that the MAML algorithm can effectively tackle the low-resource problem even with limited data resources

Signal processing and machine learning techniques for Doppler ultrasound haemodynamic measurements

Haemodynamic monitoring is an invaluable tool for evaluating, diagnosing and treating the cardiovascular system, and is an integral component of intensive care units, obstetrics wards and other medical units. Doppler ultrasound provides a non-invasive, cost-effective and fast means of haemodynamic monitoring, which traditionally necessitates highly invasive methods such as Pulmonary artery catheter or transoesophageal echocardiography. However, Doppler ultrasound scan acquisition requires a highly experienced operator and can be very challenging. Machine learning solutions that quantify and guide the scanning process in an automatic and intelligent manner could overcome these limitations and lead to routine monitoring. Development of such methods is the primary goal of the presented work. In response to this goal, this thesis proposes a suite of signal processing and machine learning techniques. Among these is a new and real-time method of maximum frequency envelope estimation. This method, which is based on image-processing techniques and is highly adaptive to varying signal quality, was developed to facilitate automatic and consistent extraction of features from Doppler ultrasound measurements. Through a thorough evaluation, this method was demonstrated to be accurate and more stable than alternative state-of-art methods. Two novel real-time methods of beat segmentation, which operate using the maximum frequency envelope, were developed to enable systematic feature extraction from individual cardiac cycles. These methods do not require any additional hardware, such as an electrocardiogram machine, and are fully automatic, real-time and highly resilient to noise. These qualities are not available in existing methods. Extensive evaluation demonstrated the methods to be highly successful. A host of machine learning solutions were analysed, designed and evaluated. This led to a set of novel features being proposed for Doppler ultrasound analysis. In addition, a state of- the-art image recognition classification method, hitherto undocumented for Doppler ultrasound analysis, was shown to be superior to more traditional modelling approaches. These contributions facilitated the design of two innovative types of feedback. To reflect beneficial probe movements, which are otherwise difficult to distinguish, a regression model to quantitatively score ultrasound measurements was proposed. This feedback was shown to be highly correlated with an ideal response. The second type of feedback explicitly predicted beneficial probe movements. This was achieved using classification models with up to five categories, giving a more challenging scenario than those addressed in prior disease classification work. Evaluation of these, for the first time, demonstrated that Doppler scan information can be used to automatically indicate probe position. Overall, the presented work includes significant contributions for Doppler ultrasound analysis, it proposes valuable new machine learning techniques, and with continued work, could lead to solutions that unlock the full potential of Doppler ultrasound haemodynamic monitoring