Long-term signal detection, segmentation and summarization using wavelets and fractal dimension: A bioacoustics application in gastrointestinal-motility monitoring

Joint Wavelet Video Denoising and Motion Activity Detection in Multimodal Human Activity Analysis: Application to Video-Assisted Bioacoustic/Psychophysiological Monitoring

“…A counterpart AADSI methodology has been developed, taking advantage of the multiresolution scanning approach of the long-term wavelet-based detection, segmentation, and summarization (LT-WDSS) algorithm [1,12,38]. Besides the determination of the sound/bioacoustics events, energy-comparisons between the tracks of the multichannel recordings are performed for topographic analysis purposes, while spectrographic colormaps and power envelope curves are employed for summarization purposes [1,12,38]. Since the AADSI methodology is well presented in the related [1, 12, 38], we will focus our attention to the VDSS method, as well as the interaction between the three content types (video, sound, and bioacoustic events).…”

“…The Boolean expression suggesting the registration of a multimedia event m E is given in the formula below, while refinement of the event's timing is also necessary: In the case of multimedia events, further multimodal analysis is enabled to facilitate the long-term inspection process. As stated, bioacoustic events provide information about the human behavior (gastrointestinal motility in our case [12]), such as activity presence or absence, energy/frequency/duration characteristics, and so forth, that could be further utilized for diagnostic purposes [1,[12][13][14]. However, misclassified bioacoustic b E events might be registered due to the presence of human body movements and sliding noises, as well as due to intense dialogues between the subjects and the nursing/medical staff.…”

“…In this way, "automated audio-detection segmentation and indexing" (AADSI) is conducted in order to estimate the corresponding sound and bioacoustic events (s E and b E , resp.). A counterpart AADSI methodology has been developed, taking advantage of the multiresolution scanning approach of the long-term wavelet-based detection, segmentation, and summarization (LT-WDSS) algorithm [1,12,38]. Besides the determination of the sound/bioacoustics events, energy-comparisons between the tracks of the multichannel recordings are performed for topographic analysis purposes, while spectrographic colormaps and power envelope curves are employed for summarization purposes [1,12,38].…”

“…Among the wanted characteristics of those algorithms is the ability to accurately extract suitable motion parameters that could be consequently used for content management purposes [1,[25][26][27][28], especially for prolonged monitoring periods. Thus, motion-detection-based video indexing is quite useful in surveillance applications, while the interaction with audio content and other modalities can serve as a powerful tool towards multimodal event detection segmentation and summarization [1,12,13].…”

“…Video monitoring and analysis allows physicians to evaluate the exact experimental condition under which the biomedical data were acquired [1]. The method described in this paper was employed in long-term gastrointestinal motility monitoring by means of abdominal sounds [1,12], to offer an alternative approach in detecting and rejecting motion-produced sliding noises; it was also very helpful during evaluation of audio-based automated pattern recognition, which offered an alternative approach in artefacts detection and removal [1,13]. Besides these two technical aspects, the incorporation of video surveillance was decided in order to be able to correlate the phases of the gastrointestinal bio-acoustic activity with other physiological parameters previously mentioned, such as brain-activity, sleep cycles' alteration, respiratoryrelated parameters, or even abnormal behavior caused by psychological factors [1].…”