Physio-control, Inc.

Microsoft Technology Licensing, LLC

Medtronic Physio-control Manufacturing Corporation

Physio-control Manufacturing Corporation

Tae Hong Joo

David R Hampton

Ronald Eugene Stickney

Cynthia P Jayne

James W Taylor

Paula Lank

Patricia O'Hearn

Daniel Yerkovich

William Edward Crone

Douglas Keith Medema

Robert A Niskanen

Tarek Z Elabbady

Mona Soliman Habib

Paul W Schmitt

Richard C Nova

William E Saltzstein

Patrick F Kelly

Methods, systems, apparatuses, and computer program products are provided for altering the behavior of an electronic personal assistant based on a situation associated with a mobile device. A situation is sensed with a plurality of sensors to generate sensor data. A situation score is calculated based on the sensor data. Behavior of an electronic personal assistant is altered based on the calculated situation score. In one aspect, the situation is a driving situation in which a driver drives a vehicle on a roadway. In such case, a driving situation score is calculated based on the sensor data, and behavior of the electronic personal assistant is altered based on the calculated driving situation score, such as suspending interactions by the electronic personal assistant with the driver to avoid the driver being distracted.

Situation aware personal assistant

Appropriate cardiac therapy is determined by data sensed and analyzed by the disclosed defibrillators and other medical devices that one or both of treat and monitor a patient. The disclosed devices sense various patient physiological parameters including patient pulse and breathing data to determine whether the patient has a pulse and to determine if the patient is breathing. Depending on the analysis of the generated patient physiological data, the disclosed devices determine the appropriate therapy for the patient, which can include providing breathing assistance to the patient and providing electrotherapy and other therapies to the patient. Some of the disclosed medical devices can be wearable by the patient. The disclosed devices can include therapy modules like electrotherapy for delivering therapies to the patient while other devices monitor but do not deliver the therapies to the patient.

Pulse detection apparatus, software, and methods using patient physiological signals

A cardiac pulse in a patient is determined by evaluating physiological signals in the patient. A medical device evaluates two or more different physiological signals, such as phonocardiogram (PCG) signals, electrocardiogram (ECG) signals, patient impedance signals, piezoelectric signals, and accelerometer signals for features indicative of the presence of a cardiac pulse. Using these features, the medical device determines whether a cardiac pulse is present in the patient. The medical device may also be configured to report whether the patient is in a VF, VT, asystole, or PEA condition, in addition to being in a pulseless condition, and prompt different therapies, such as chest compressions, rescue breathing, defibrillation, and PEA-specific electrotherapy, depending on the analysis of the physiological signals. Auto-capture of a cardiac pulse using pacing stimuli is further provided. The medical devices, or any portion thereof, can be worn by the patient or may be attached to the patient.

The presence of a cardiac pulse in a patient is determined by evaluating physiological signals in the patient. In one embodiment, a medical device evaluates two or more different physiological signals, such as phonocardiogram (PCG) signals, electrocardiogram (ECG) signals, patient impedance signals, piezoelectric signals, and accelerometer signals for features indicative of the presence of a cardiac pulse. Using these features, the medical device determines whether a cardiac pulse is present in the patient. The medical device may also be configured to report whether the patient is in a VF, VT, asystole, or PEA condition, in addition to being in a pulseless condition, and prompt different therapies, such as chest compressions, rescue breathing, defibrillation, and PEA-specific electrotherapy, depending on the analysis of the physiological signals. Auto-capture of a cardiac pulse using pacing stimuli is further provided.

Signal data obtained from a piezoelectric sensor placed on a patient's body is used to detect the presence of a cardiac pulse. The piezoelectric sensor has a transducing element adapted to sense movement due to a cardiac pulse and produce piezoelectric signal data in response thereto. Processing circuitry analyzes the piezoelectric signal data for a feature indicative of a cardiac pulse and determines whether a cardiac pulse is present in the patient based on the feature. In one aspect, the feature may be a temporal feature such as a relative change in energy. In another aspect, the feature may be a spectral feature such as the energy or frequency of a peak in the energy spectrum of the signal. In yet another aspect, the feature may be obtained by comparing the piezoelectric signal data with a previously-identified pattern known to predict the presence of a cardiac pulse. Multiple features may also be obtained from the piezoelectric signal data and classified to determine the presence of a cardiac pulse.

Apparatus, software, and methods for cardiac pulse detection using a piezoelectric sensor

A method and apparatus determines the presence of a cardiac pulse in a patient by evaluating a physiological signal in the patient, preferably for the presence of characteristic heart sounds. The presence of a heart sound in a patient is determined by analyzing phonocardiogram (PCG) data obtained from the patient. Analyzing the PCG data may include evaluating temporal energy in the PCG data or evaluating spectral energy in the PCG data. Evaluating temporal energy in the PCG data may include estimating a first and second energy in the PCG data and comparing the first and second energy to determine a relative change in energy between them. Evaluating spectral energy in the PCG data may include calculating an energy spectrum of the PCG data and evaluating either the energy value or the frequency of a peak energy in the energy spectrum. The presence of a heart sound may also be determined by combining an evaluation of temporal energy with an evaluation of spectral energy in the PCG data. Electrocardiogram data obtained from the patient may further be used in connection with the PCG data to determine the presence of a cardiac pulse in the patient. An automated external defibrillator processing the physiological signal sensed in the patient automatically reports to an operator of the defibrillator whether a cardiac pulse is determined to be present.

Method and apparatus for using heart sounds to determine the presence of a pulse

A reduced lead set device (,) that detects and reports the presence of an acute cardiac ischemic condition in a patient includes a reduced set of sensing electrodes (,and,) placed on a patient for acquiring ECG data from the patient. The reduced lead set device evaluates the ECG data on a reduced set of leads by analyzing local features and/or global features of the ECG data. Local features may include local morphological measures such as ST elevation and clinical information on the patient such as age and sex. Global features include projection coefficients calculated from projecting a concatenated vector of heartbeat data onto separate sets of basis vectors that define signal subspaces of ischemic and non-ischemic ECGs. One or more classifiers evaluate the local features and/or global features to determine whether an acute cardiac ischemic condition is detected. The operating point, i.e., sensitivity and specificity, of the reduced lead set device is adjustable. The result of the evaluation is reported to the user of the device.

Reduced lead set device and method for detecting acute cardiac ischemic conditions

A device (,) that detects and reports the presence of an acute cardiac ischemic condition in a patient includes sensing electrodes (,, and,) placed on a patient for acquiring multiple (e.g., twelve) lead ECG data from the patient. The device evaluates the acquired ECG data by analyzing global features derived from the ECG data. Global features include projection coefficients calculated from projecting a concatenated vector of representative heartbeat data onto sets of basis vectors that define an acute cardiac ischemic ECG subspace and a non-ischemic ECG subspace. A classifier evaluates the global features to determine whether an acute cardiac ischemic condition is detected. In a further embodiment of the invention, one or more classifiers evaluate local features, such as local morphological features and patient clinical information, in addition to the global features to determine whether an acute cardiac ischemic condition is detected. The device (,) may be configured with an adjustable sensitivity/specificity operating point. The result of the evaluation is reported to the user of the device.

Method and apparatus for detecting a condition associated with acute cardiac ischemia

A defibrillator (8) is disclosed that analyzes a 12-lead ECG signal of a patient (25) to detect internal pacing pulses generated by an internal pacemaker (26). At least one of the twelve leads, e.g., lead II, is passed through a low pass filter (28) followed by an analog-to-digital converter (16). The digitized lead II signal is then applied to a digital bandpass filter to remove low and high frequency noise. Using the digital, bandpass filtered, lead II signal, a short term energy (s) and a long term energy (r) are computed. The short term energy (s) consists mostly internal pacing pulse energy, while the long term energy (r) essentially consists of the energy associated with background noise. If the ratio of the short term energy (s) to the long term energy (r) is greater than a predetermined threshold, or if the pair of energy values falls within a range of values defined as a function of the predetermined threshold and absolute noise, a pacing pulse is detected.

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Redmond, WA, United States of America

Tae Hong Joo