Embodiments of a wearable monitoring device system can include one or more dry ECG electrodes and a processor that can be configured with one or more algorithms for detecting atrial fibrillation (AF) from sensed ECG signals sensed by the one or more dry ECG electrodes, and optionally other signals. In some embodiments the algorithms include one or more AF detection algorithms and optionally a noise detection algorithm. In some embodiments the wearable monitoring device or a remote system that receives data from the wearable medical device may calculate and/or characterize AF burden from ECG signals sensed by the one or more dry ECG electrodes.

The present invention relates to device, system and method for detecting a cardiac and/or respiratory disease of a subject. The proposed device comprises a sound input (20) for obtaining a sound signal representing sounds generated by the subject's body; a motion input (21) for obtaining a motion signal representing motions generated by the subject's body; and a processor (22) for processing the obtained sound signal and motion signal. This processing includes identifying inhalation and/or exhalation periods of the subject based on the motion signal, detecting abnormal lung sounds during inhalation and/or exhalation periods based on the sound signal, determining abnormal lung sound characteristics of the detected abnormal lung sounds, determining breathing characteristics of the subject's breathing based on the sound signal, determining the phase of the abnormal lung sounds in the inhalation-exhalation cycle, and detecting a cardiac and/or respiratory disease of the subject based on the determined abnormal lung sound characteristics, the determined breathing characteristics and the determined phase of the abnormal lung sounds in the inhalation-exhalation cycle.

A physiological signal monitoring system includes a single set of sensing electrodes to provide conditioned physiological signals to a primary monitoring device and a secondary monitoring device. The monitoring system includes pre-processing circuitry configured to receive a raw physiological signal. The pre-processing circuitry is configured to produce a primary physiological signal and a secondary physiological signal. Each of the primary and secondary physiological signals are conditioned. The primary conditioned physiological signal is directed to a primary monitoring device such as a hospital wearable defibrillator device. The secondary conditioned physiological signal is directed to telemetry modeling circuitry where it is further processed to output one or more telemetry signals. The one or more telemetry signals are output to a secondary monitoring device such as a three lead ECG monitoring device. Thus, a single set of sensing electrodes can provide physiological signals to multiple monitoring devices.

A system includes a collar that is worn around a neck of the user. A stimulator is coupled to the collar such that the stimulator is positioned adjacent to an airway of the user. The sensor is coupled to the collar and configured to generate data associated with the airway of the user. The memory is coupled to the collar and storing machine-readable instructions. The control system is coupled to the collar and includes one or more processors configured to execute the machine-readable instructions to determine, based at least on an analysis of the generated data, that the user is currently experiencing an apnea event. In response to the determination, the control system causes the stimulator to provide electrical stimulation, at a first intensity level, to one or more muscles of the user that are adjacent to the airway to aid in stopping the apnea event.

Embodiments of the disclosed subject matter generally relate to electrically grounding a user who may be working in an environment where static electricity might otherwise build up on the user and damage sensitive electronic devices being handled by the user. Embodiments include attaching an electrocardiogram (ECG) electrode to the user's skin and removably attaching the ECG electrode to an electrical conductor which may be detachably connected to a flexible base disposed on the outside of and under the user's shoe.

Technologies and implementations for a wearable healthcare system including one or more electrodes, which may detect and determine smart leads off conditions of the one or more electrodes. The wearable healthcare system may include a leads off monitor module, which may be configured to learn when and when not to cause a leads off alert.

A wearable device may include a sensor. The sensor may include a flexible fabric, a conjugated polymer coating deposited on the fabric via vapor-phase oxidative chemical vapor deposition (oCVD), and a plurality of electrodes in coupled to the conjugated polymer coating. The wearable device may further include a processor communicatively coupled to the electrodes. The processor may measure an electrical property across the electrodes, determine a physiological event based on the measured electrical property, and output measurement information corresponding the physiological event.

An ambulatory medical device configured to analyze heart rates in different operating modes includes a plurality of ECG sensing electrodes, a plurality of therapy electrodes and at least one processor configured to in a default operating mode, perform a default heart rate calculation for determining a heart rate of the patient for use in detecting a cardiac arrhythmia condition of the patient. The at least one processor is configured to change a device operating mode from a default mode based on detecting patient activity to an activity operating mode, and in the activity operating mode, perform a different heart rate calculation from the default heart rate calculation for determining the heart rate for use in detecting the cardiac arrhythmia condition of the patient during the activity operating mode. The at least one processor is configured to deliver the treatment in response to detecting the cardiac arrhythmia condition.

A method for detecting wheeze from an audio respiratory signal comprises capturing the audio respiratory signal from a subject using a microphone. Further, the method comprises recognizing a plurality of breath cycles and a plurality of breath phases from the audio respiratory signal and detecting wheezing from the plurality of breath cycles and the plurality of breath phases. The detecting comprises analyzing a block of interest in the audio respiratory signal, wherein the block of interest comprises a plurality of frames. The detecting further comprises calculating an auto-correlation function (ACF) for each frame in the block and determining a maximum value of the ACF calculated for each frame in the block. Finally, the detecting comprises analyzing the maximum value to detect if wheezing is present in the block.

Certain exemplary embodiments can provide a system, machine, device, manufacture, circuit, composition of matter, and/or user interface adapted for and/or resulting from, and/or a method and/or machine-readable medium comprising machine-implementable instructions for, activities that can comprise and/or relate to, facilitating determination of the oxygen consumption, carbon dioxide production, respiratory exchange ratio, and/or energy expenditure of a mammal.