METHODS AND SYSTEM FOR MONITORING PATIENTS FOR CLINICAL EPISODES

• US 20130245502A1
• Filed: 04/15/2013
• Published: 09/19/2013
• Est. Priority Date: 11/01/2005
• Status: Abandoned Application

• Current Assignee: EarlySense Ltd.
• Original Assignee: EarlySense Ltd.
• Inventors: Lange, Daniel H., Averboukh, Arkadi, Halperin, Avner
• Application Number:
  US13/863,293
• Publication Number:
  US 20130245502A1
• Time in Patent Office: Days
• US Class Current: 600/595
• CPC Class Codes: A61B 2560/0247 for compensation or correction of the measured physiological value
• CPC Class Codes: A61B 5/0823 Detecting or evaluating cough events
• CPC Class Codes: A61B 5/1101 Detecting tremor
• CPC Class Codes: A61B 5/1102 Ballistocardiography
• CPC Class Codes: A61B 5/1104 induced by stimuli or drugs; A61B5/1102 takes precedence; investigating medicinal preparations G01N33/15
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/4809 Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4812 Detecting sleep stages or cycles
• CPC Class Codes: A61B 5/4815 Sleep quality
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/6891 Furniture
• CPC Class Codes: A61B 5/6896 Toys
• CPC Class Codes: A61B 5/7239 using differentiation including higher order derivatives
• 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 5/7282 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: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Apparatus and methods are described for monitoring a subject. A motion sensor senses motion of the subject and generates a sensor signal in response thereto. A control unit includes a filter configured to extract from the sensor signal at least one signal selected from the group consisting of: a breathing-related signal and a heartbeat-related signal. The control unit is configured to analyze the selected signal and to detect changes in body posture of the subject at least partially in response thereto. Other applications are also described.

Claims

1-116. -116. (canceled)

117. Apparatus for monitoring a subject, the apparatus comprising:

a motion sensor configured to sense motion of the subject and generate a sensor signal in response thereto; and

a control unit comprising a filter configured to extract from the sensor signal at least one signal selected from the group consisting of;

a breathing-related signal and a heartbeat-related signal,

the control unit being configured to analyze the selected signal and to detect changes in body posture of the subject at least partially in response thereto.

118. The apparatus according to claim 117, wherein the control unit is configured to analyze frequency and timings of the changes in body posture, to detect restlessness of the subject in response thereto, and to generate an output in response to detecting the restlessness.

119. The apparatus according to claim 117, wherein the control unit is configured to analyze frequency and timings of the detected changes in body posture, to detect restlessness of the subject in response thereto, and, in response to detecting the restlessness, to discard data sensed by the sensor during the restlessness.

120. The apparatus according to claim 117, wherein the control unit is configured to detect the changes in body posture by analyzing a relationship between the breathing-related signal and the heartbeat-related signal.

121. The apparatus according to claim 120, wherein the control unit is configured to detect the changes in body posture by calculating a ratio between a signal parameter of the heartbeat-related signal over a given time period, and a signal parameter of the breathing-related signal over the given time period, and detecting that the ratio corresponding to a first occurrence of the given time period, and the ratio corresponding to a subsequent occurrence of the given time period has changed by more than a threshold amount.

122. The apparatus according to claim 121, wherein the control unit is configured to calculate the ratio between the signal parameter of the heartbeat-related signal over a given time period, and the signal parameter of the breathing-related signal over the given time period by calculating the ratio between the signal parameter of the heartbeat-related signal and the signal parameter of the breathing-related signal over a time period of between 30 seconds and 300 seconds.

123. The apparatus according to claim 121, wherein the control unit is configured to detect that the ratio corresponding to the first occurrence of the given time period and the ratio corresponding to the subsequent occurrence of the given time period has changed by more than a threshold amount by detecting that the ratio corresponding to consecutive occurrences of the given time period has changed by more than a threshold amount.

124. The apparatus according to claim 121, wherein the control unit is configured to detect that the ratio corresponding to the first occurrence of the given time period and the ratio corresponding to the subsequent occurrence of the given time period has changed by more than a threshold amount by detecting that the ratio corresponding to the first occurrence of the given time period, and the ratio corresponding to the subsequent occurrence of the given time period has changed by more than between 10% and 50%.

125. The apparatus according to claim 121, wherein the control unit is configured to calculate the ratio between the signal parameter of the heartbeat-related signal and the signal parameter of the breathing-related signal by calculating a ratio between an amplitude of the heartbeat-related signal, and an amplitude of the breathing-related signal.

126. The apparatus according to claim 125,

wherein the filter is configured to:

extract the breathing-related signal from the sensor signal, by performing spectral filtering on the sensor signal at a first frequency range,

extract the heartbeat-related signal from the sensor signal, by performing spectral filtering on the sensor signal at a second frequency range, and

wherein the control unit is configured to calculate the ratio between the amplitude of the heartbeat-related signal, and the amplitude of the breathing-related signal by;

calculating a power spectrum for the breathing-related signal, and, in response thereto, identifying a largest peak in the breathing-related signal,

calculating a power spectrum for the heartbeat-related signal, and, in response thereto, identifying a largest peak in the heartbeat-related signal, and

calculating a ratio between the largest peak in the heartbeat-related signal and the largest peak in the breathing-related signal.

127. A method for monitoring a subject comprising:

sensing motion of the subject with a sensor, and generating a sensor signal in response thereto;

using a filter, extracting from the sensor signal at least one signal selected from the group consisting of;

a breathing-related signal and a heartbeat-related signal;

using a control unit, analyzing the selected signal; and

using the control unit, detecting changes in body posture of the subject at least partially in response to the analysis of the selected signal.

128. The method according to claim 127, further comprising analyzing frequency and timings of the detected changes in body posture, detecting restlessness of the subject in response thereto, and generating an output in response to detecting the restlessness.

129. The method according to claim 127, further comprising analyzing frequency and timings of the detected changes in body posture, detecting restlessness of the subject in response thereto, and, in response to detecting the restlessness, discarding data that was sensed by the sensor during the restlessness.

130. The method according to claim 127, wherein detecting the changes in body posture comprises analyzing a relationship between the breathing-related signal and the heartbeat-related signal.

131. The method according to claim 130, wherein detecting the changes in body posture comprises calculating a ratio between a signal parameter of the heartbeat-related signal over a given time period, and a signal parameter of the breathing-related signal over the given time period, and detecting that the ratio corresponding to a first occurrence of the given time period, and the ratio corresponding to a subsequent occurrence of the given time period has changed by more than a threshold amount.

132. The method according to claim 131, wherein calculating the ratio between the signal parameter of the heartbeat-related signal over a given time period, and the signal parameter of the breathing-related signal over the given time period comprises calculating the ratio between the signal parameter of the heartbeat-related signal and the signal parameter of the breathing-related signal over a time period of between 30 seconds and 300 seconds.

133. The method according to claim 131, wherein detecting that the ratio corresponding to the first occurrence of the given time period, and the ratio corresponding to the subsequent occurrence of the given time period has changed by more than a threshold amount comprises detecting that the ratio corresponding to consecutive occurrences of the given time period has changed by more than a threshold amount.

134. The method according to claim 131, wherein detecting that the ratio corresponding to the first occurrence of the given time period, and the ratio corresponding to the subsequent occurrence of the given time period has changed by more than a threshold amount comprises detecting that the ratio corresponding to the first occurrence of the given time period, and the ratio corresponding to the subsequent occurrence of the given time period has changed by more than between 10% and 50%.

135. The method according to claim 131, wherein calculating the ratio between the signal parameter of the heartbeat-related signal, and the signal parameter of the breathing-related signal comprises calculating a ratio between an amplitude of the heartbeat-related signal, and an amplitude of the breathing-related signal.

136. The method according to claim 135,

wherein extracting the at least one signal comprises:

extracting the breathing-related signal from the sensor signal, by performing spectral filtering on the sensor signal at a first frequency range,

extracting the heartbeat-related signal from the sensor signal, by performing spectral filtering on the sensor signal at a second frequency range, and

wherein calculating the ratio between the amplitude of the heartbeat-related signal, and the amplitude of the breathing-related signal comprises;

calculating a power spectrum for the breathing-related signal, and in response thereto, identifying a largest peak in the breathing-related signal,

calculating a power spectrum for the heartbeat-related signal, and in response thereto, identifying a largest peak in the heartbeat-related signal, and

calculating a ratio between the largest peak in the heartbeat-related signal and the largest peak in the breathing related signal.