Heart Sound Localization in Respiratory Sound Based on a New Computationally Efficient Entropy Bound

Partial Discharge Pulse Segmentation Approach of Converter Transformers Based on Higher Order Cumulant

As one of the most effective methods to detect the partial discharge (PD) of transformers, high frequency PD detection has been widely used. However, this method also has a bottleneck problem; the biggest problem is the mixed pulse interference under the fixed length sampling. Therefore, this paper focuses on the study of a new pulse segmentation technology, which can separate the partial discharge pulse from the sampling signal containing impulse noise so as to suppress the interference of pulse noise. Based on the characteristics of the high-order-cumulant variation at the rising edge of the pulse signal, a method for judging the starting and ending time of the pulse based on the high-order-cumulant is designed, which can accurately extract the partial discharge pulse from the original data. Simulation results show that the location accuracy of the proposed method can reach 94.67% without stationary noise. The field test shows that the extraction rate of the PD analog signal can reach 79% after applying the segmentation method, which has a great improvement compared with a very low location accuracy rate of 1.65% before using the proposed method.

Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS - the Heart Sounds Shenzhen Corpus

Auscultation of the heart is a widely studied technique, which requires precise hearing from practitioners as a means of distinguishing subtle differences in heart-beat rhythm. This technique is popular due to its non-invasive nature, and can be an early diagnosis aid for a range of cardiac conditions. Machine listening approaches can support this process, monitoring continuously and allowing for a representation of both mild and chronic heart conditions. Despite this potential, relevant databases and benchmark studies are scarce. In this paper, we introduce our publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), which was first released during the recent INTERSPEECH 2018 ComParE Heart Sound sub-challenge. Additionally, we provide a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models. At best our support vector machine with Log Mel features achieves 49.7% unweighted average recall on a three category task (normal, mild, moderate/severe).

Blind Equalization of Lung Crackle Sounds to Compensate Chest Attenuation

Diseased lungs generate adventitious sounds that propagate through the thorax, reaching the surface where they may be heard or recorded. The attenuation imposed to the lung sounds by the thorax depends on the physical characteristics of each patient, hampering the analysis of quantitative indexes measured to assist the diagnosis of cardiorespiratory disorders. This work proposes the application of a blind equalizer (eigenvector algorithm - EVA) to reduce the effects of thorax attenuation on indexes measured from crackle sounds. Computer simulated crackles (acquired on the posterior chest wall after being applied to volunteer's mouth) and actual crackles belonging to a database were equalized. Quantitative indexes were measured from crackles before and after equalization. Comparison of indexes measured from simulated crackles reveals that the equalizer improves the results due to attenuation compensation and removal of Gaussian noise. Effects of equalization on indexes measured from actual crackles were qualitatively assessed. Results point out that blind equalization of crackles recorded on the thorax provides more consistent quantitative indexes to assist the diagnosis of different cardiorespiratory diseases.