Systems and methods for detecting and managing physiological patterns

Inventors

Levendowski, Daniel J. • Veljkovic, Bratislav • Matthews, Amy • Velimirovic, Vladislav • Angel, Elise Ramos • Berka, Christine • Davis, Gene • Zoranovic, Aleksandar • Cvetinovic, Milenko • Westbrook, Philip R.

Assignees

Advanced Brain Monitoring Inc

Abstract

Systems and methods for managing sleep quality of a patient, comprising: collecting physiological signal data of the patient using a data acquisition unit electrically coupled to at least one sensor affixed to the patient that generates the physiologic signal data; using one or more hardware processors executing instructions stored in a storage device: filtering the physiological signal data into a plurality of frequency bands corresponding to a plurality of power spectra waveforms; and characterizing an etiology of sleep quality of the patient based on a comparison of at least a first power spectra waveform of the plurality of power spectra waveforms against at least a second power spectra waveform of the plurality of power spectra waveforms, wherein the sleep quality of the patient is managed based on the characterized etiology of sleep.

Core Innovation

The disclosure describes systems and methods for detecting one or more abnormal conditions of a patient associated with adverse outcomes in encephalopathic patients by collecting physiological signal data using a data acquisition unit electrically coupled to at least one sensor affixed to the patient. The physiological signal data is processed to identify abnormal electroencephalographic (EEG) activity over one or more epochs, where each epoch corresponds to a first time scale. The abnormal EEG activity is derived from filtered physiological data expressed as frequency bands and a plurality of power spectra waveforms.

The core processing includes monitoring at least one power spectra waveform of the plurality of power spectra waveforms over a portion of a time period on a second time scale that is longer than the first time scale. The second time scale portion comprises at least one of a portion of the time period previous to at least one epoch and a portion of the time period subsequent to at least one epoch. Abnormal EEG activity identified on the first time scale is compared against the power spectra waveform on the second time scale to detect one or more abnormal conditions associated with adverse outcomes in encephalopathic patients.

The disclosure further encompasses monitoring and managing sleep quality using a wearable data acquisition unit with at least one sensor affixed to the patient, where physiological signals are filtered into EEG frequency bands and power spectra waveforms. The resulting characterization supports identifying sleep architecture and sleep continuity and differentiating an etiology of poor sleep. The disclosure also supports remote/cloud viewing and an integrated clinical workflow including alarm/notification and recommendation retrieval.

Claims Coverage

The partial content provides two independent claims, one method and one system. Across these claims, the inventive features center on acquiring physiological signal data from patient-affixed sensors, filtering the data into frequency bands and power spectra waveforms, extracting abnormal EEG activity for epochs on a first time scale, monitoring power spectra waveforms on a longer second time scale including time before and/or after epochs, and detecting abnormal conditions associated with adverse outcomes by comparing the time scales.

Both independent claims share the same core detection logic: abnormal EEG activity is extracted for epochs on a first time scale after filtering physiological data into frequency bands and power spectra waveforms, then the monitored power spectra waveforms are evaluated over a longer second time scale including time before and/or after the epochs, and abnormal conditions associated with adverse outcomes are detected via comparison across these time scales.

Stated Advantages

Documented Applications