Abnormality detection in noisy biosignals

Abstract

Although great strides have been achieved in computer-aided diagnosis (CAD) research, a major remaining problem is the ability to perform well under the presence of significant noise. In this work, we propose a mechanism to find instances of potential interest in time series for further analysis. Adaptive Kalman filters are employed in parallel among different feature axes. Lung sounds recorded in noisy conditions are used as an example application, with spectro-temporal feature extraction to capture the complex variabilities in sound. We demonstrate that both disease indicators and distortion events can be detected, reducing long time series signals into a sparse set of relevant events.

Modelling auditory attention

“…Auditory salience has been demonstrated to be an effective criterion for compression to reduce data size while retaining meaningful segments of large datasets of sound [85] and video [86]. Salience extraction has also been used as an abnormal sound detection mechanism for temporal signals, and generalized to lung sounds to use for finding medical abnormalities [87].…”

Spectral features and optimal Hierarchical attention networks for pulmonary abnormality detection from the respiratory sound signals

“…Avoidance, earlier analysis and proper treatment are the chief factors for restricting harmful impact of these toxic diseases [5]. Moreover, respiratory sounds produced from the airways and the lungs offer essential information regarding their pathologies and physiologies [4] [21]. The respiratory sounds are categorized in two classes, like normal and abnormal based on acoustic sound created from lungs.…”