Prediction and monitoring of clinical episodes

• US 9,131,902 B2
• Filed: 02/18/2015
• Issued: 09/15/2015
• Est. Priority Date: 02/05/2004
• Status: Active Grant

• Current Assignee: Hill-Rom Services Incorporated (Baxter International Inc.)
• Original Assignee: EarlySense Ltd.
• Inventors: Halperin, Avner, Pinhas, Itzhak, Gross, Yosef, Lange, Daniel H.
• Primary Examiner(s): Lavert, Nicole F
• Application Number:
  US14/624,904
• Publication Number:
  US 20150164433A1
• Time in Patent Office: 209 Days
• Field of Search: 600/16, 600/301
• US Class Current: 1/1
• CPC Class Codes: A61B 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• CPC Class Codes: A61B 5/08 Detecting, measuring or recording devices for evaluating the respiratory organs A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/0823 Detecting or evaluating cough events
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/4094 Diagnosing or monitoring seizure diseases, e.g. epilepsy
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A61B 5/6892 Mats
• CPC Class Codes: A61B 5/704 Tables
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/7405 using sound
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: A61N 1/3956 Implantable devices for applying electric shocks to the heart, e.g. for cardioversion
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Apparatus and methods are described, including apparatus for predicting an onset of a clinical episode. The apparatus includes a sensor, configured to sense at least one parameter of a subject substantially continuously during a period having a duration of at least one hour, and a control unit, configured to predict, at least one hour prior to the onset of the clinical episode, the onset at least in part responsively to the sensed parameter. Other applications are also described.

Claims

1. Apparatus for predicting an onset of a clinical episode, the apparatus comprising:

a sensor, the sensor is configured to sense at least one parameter of a subject substantially continuously during a period having a duration of at least one hour and without contacting or viewing the subject or clothes the subject is wearing; and

a control unit, configured to predict, at least one hour prior to the onset of the clinical episode, the onset at least in part responsively to the sensed parameter.

2. The apparatus according to claim 1, wherein the sensor is configured to sense at least two parameters of the subject substantially continuously during the period, the control unit being configured to predict the onset at least in part responsively to the sensed parameters.

3. The apparatus according to claim 1, wherein the clinical episode includes an asthma attack, the control unit being configured to predict the onset of the asthma attack.

4. The apparatus according to claim 1, wherein the control unit is configured to predict the onset at least four hours prior to the onset.

5. The apparatus according to claim 1, wherein the period has a duration of at least four hours, the sensor being configured to sense the at least one parameter substantially continuously during the period having the duration of at least four hours.