Abstract: A method for diagnosis includes acquiring a physiological signal from a patient (22) during a period of sleep, and segmenting the signal to define a time sequence of quasi-stationary segments, each of the segments having a respective frequency spectrum. Respective levels of membership of the segments in a plurality of frequency states are computed responsively to the respective frequency spectrum. Based on the respective levels of membership, a sleep quality indicator is determined and displayed, responsively to a statistical characteristic of the segments.

Abstract: A method for diagnosis of a sleep-related condition of a patient having a thorax. The method includes receiving physiological signals from sensors coupled to the thorax of the patient, and analyzing the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

Abstract: Apparatus (10) is provided that includes at least one sensor (30), configured to sense a physiological parameter of a subject (12) and to sense large body movement of the subject (12), an output unit (24), and a control unit (14). The control unit (14) is configured to monitor a condition of the subject (12) by analyzing the physiological parameter and the sensed large body movement, and to drive the output unit (24) to generate an alert upon detecting a deterioration of the monitored condition. Other embodiments are also described.

Abstract: A method for diagnosis of a sleep-related condition of a patient having a thorax. The method includes receiving physiological signals from sensors coupled to the thorax of the patient, and analyzing the physiological signals, independently of any electroencephalogram (EEG) or electro-oculogram (EOG) signals, in order to identify sleep stages of the patient.

Abstract: Methods and systems that (a) adaptively segment a raw data stream(s) of different type biomedical signals; (b) assign attribute values to each segment; (c) determine an attribute domain based on a point corresponding to said segment attribute value; (d) generate a cluster set(s) in said attribute domain that includes a combination of points; (e) obtain a probability of order of appearance of each cluster point, according to its property value; (f) use said probability to update each point's property value; (g) repeat (d) through (f) while varying combinations of points in clusters according to their most updated property values and points derived from additional raw data stream adaptive segmentations; (h) associate at least one updated cluster with a normal/abnormal physiological state based on reference clusters in said domain; and (i) obtain the change probability between normal/abnormal physiological states using said probability of the order of appearance.