Self-adaptive system for the analysis of biomedical signals of a patient
First Claim
1. A method for sleep diagnosis, comprising:
- using one or more sensors, receiving biomedical signals from a sleeping patient over a given time period;
using a processor, extracting features from the biomedical signals and grouping the features into multiple clusters;
using the processor, assigning respective vectors of membership values to a plurality of intervals within the given time period, each membership value indicating a degree of association between the biomedical signals in a corresponding interval and a respective cluster among the multiple clusters; and
with the processor, determining a condition of the patient responsively to the vectors using a state machine model which comprises machine states corresponding to physiological states of the patient.
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Accused Products
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.
114 Citations
14 Claims
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1. A method for sleep diagnosis, comprising:
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using one or more sensors, receiving biomedical signals from a sleeping patient over a given time period; using a processor, extracting features from the biomedical signals and grouping the features into multiple clusters; using the processor, assigning respective vectors of membership values to a plurality of intervals within the given time period, each membership value indicating a degree of association between the biomedical signals in a corresponding interval and a respective cluster among the multiple clusters; and with the processor, determining a condition of the patient responsively to the vectors using a state machine model which comprises machine states corresponding to physiological states of the patient. - View Dependent Claims (2, 3, 4, 5)
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6. A method for sleep diagnosis, comprising:
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using a processor, defining a state machine model comprising machine states corresponding to physiological states of a patient, and comprising respective probabilities of transitions among the states; using one or more sensors, receiving biomedical signals from a sleeping patient over a given time period; using the processor, segmenting the biomedical signals into a sequence of segments, and determining one or more respective features of each of the segments; using the processor, assigning respective state classifications to the segments responsively to the respective features and to the probabilities of the transitions among the states of the state machine model; and using the processor, evaluating a condition of the patient during the given time period responsively to the state classifications. - View Dependent Claims (7, 8, 9)
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10. Apparatus for sleep diagnosis, comprising:
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one or more sensors, which are adapted to receive biomedical signals from a sleeping patient over a given time period; and a processor, which is adapted to extract features from the biomedical signals and to group the features into multiple clusters, to assign respective vectors of membership values to a plurality of intervals within the given time period, each membership value indicating a degree of association between the biomedical signals in a corresponding interval and a respective cluster among the multiple clusters, and to evaluate a condition of the patient responsively to the vectors, wherein the processor is adapted to receive a state machine model comprising machine states corresponding to physiological states of the patient, and to determine the condition using the state machine model.
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11. Apparatus for sleep diagnosis, comprising:
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one or more sensors, which are adapted to receive biomedical signals from a sleeping patient over a given time period; and a processor, which is adapted to receive a definition of a state machine model comprising machine states corresponding to physiological states of a patient, the model comprising respective probabilities of transitions among the states, wherein the processor is adapted to segment the biomedical signals into a sequence of segments and determine one or more respective features of each of the segments, to assign respective state classifications to the segments responsively to the respective features and to the probabilities of the transitions among the states of the state machine model, and evaluate a condition of the patient during the given time period responsively to the state classifications. - View Dependent Claims (12, 13, 14)
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Specification