Sleep monitoring using body sounds and motion tracking.

This paper presents a system for sleep monitoring that can continuously analyze snoring, breathing, sleep phases and the activity of the patient during the night and the beginning of the day. Early results show that the system can be used to detect the occurrence of obstructive sleep apnea syndrome (OSAS). OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital, where the patient is attached to multiple electrodes and sensors. Our system detects heartbeats, breathing, snoring, sleeping positions and movements using a special electret microphone and an inertial measurement unit (IMU). The system first analyses the sleep using the acoustic information provided by the electret microphone. From the acoustic information breathing events and heartbeats are identified. The system also analyses the patient's activity and positions from data delivered by the IMU. The information from both sensors is fused to detect sleep events. First experiments show that the system is capable of detecting and interpreting relevant data to improve sleep monitoring.

Reconstruction of chemical molecules from images.

We have developed a system for the automatic reconstruction of chemical molecules from images. The system takes as input an electronically produced image of a chemical molecule and produces an SDF file containing the complete chemical description of the molecule. The SDF file can then be read and used by most chemical computer programs. Our system finds extensive application in information extraction problems where the molecule images contained in chemical documents need to be rendered computer readable. We have benchmarked our system against a commercially available product and have also tested it using chemical databases of several thousand images. The system can be parameterized to reconstruct images of different sources and different characteristics.

Classification of anatomical structures in MR brain images using fuzzy parameters.

We present an algorithm that automatically segments and classifies the brain structures in a set of magnetic resonance (MR) brain images using expert information contained in a small subset of the image set. The algorithm is intended to do the segmentation and classification tasks mimicking the way a human expert would reason. The algorithm uses a knowledge base taken from a small subset of semiautomatically classified images that is combined with a set of fuzzy indexes that capture the experience and expectation a human expert uses during recognition tasks. The fuzzy indexes are tissue specific and spatial specific, in order to consider the biological variations in the tissues and the acquisition inhomogeneities through the image set. The brain structures are segmented and classified one at a time. For each brain structure the algorithm needs one semiautomatically classified image and makes one pass through the image set. The algorithm uses low-level image processing techniques on a pixel basis for the segmentations, then validates or corrects the segmentations, and makes the final classification decision using higher level criteria measured by the set of fuzzy indexes. We use single-echo MR images because of their high volumetric resolution; but even though we are working with only one image per brain slice, we have multiple sources of information on each pixel: absolute and relative positions in the image, gray level value, statistics of the pixel and its three-dimensional neighborhood and relation to its counterpart pixels in adjacent images. We have validated our algorithm for ease of use and precision both with clinical experts and with measurable error indexes over a Brainweb simulated MR set.