Title :
Abnormality detection in noisy biosignals
Author :
Kaya, Emine Merve ; Elhilali, Mounya
Author_Institution :
Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA
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.
Keywords :
adaptive Kalman filters; bioacoustics; feature extraction; lung; medical diagnostic computing; medical signal detection; signal denoising; time series; CAD; abnormality detection; adaptive Kalman filters; computer-aided diagnosis research; disease indicators; distortion events; feature axes; long time series signals; lung sounds; noisy biosignals; spectrotemporal feature extraction; Diseases; Feature extraction; Kalman filters; Lungs; Noise; Noise measurement;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Conference_Location :
Osaka
DOI :
10.1109/EMBC.2013.6610409