An example of a system for providing patient care guidance to a caregiver based on ultrasound detection of blood flow includes a defibrillator including an electrode assembly and an output device, a portable computing device communicatively coupled to the defibrillator and including an output device, a Doppler shift waveform evaluation engine disposed at the defibrillator and/or the portable computing device, and a wearable ultrasound blood flow sensor configured to couple to a patient and the defibrillator and/or the portable computing device and to generate data signals representing a Doppler shift waveform. The engine is configured to receive the data signals representing the waveform, generate caregiver instructions according to a cardiac arrest protocol, analyze the waveform based on the received data signals, identify heart-induced blood flow based on the waveform analysis, and generate and provide caregiver instructions according to a non-cardiac arrest protocol based on the identified heart-induced blood flow.

Provided is a real-time multi-monitoring method using an electrocardiograph and a real-time multi-monitoring method using an electrocardiograph including connecting at least one of a plurality of electrocardiographs via a network; receiving identification information of a user which is linked to an electrocardiograph to identify the plurality of electrocardiographs; collecting biometric signal information from the plurality of electrocardiographs; and analyzing a health condition in consideration of predetermined reference information which is defined for each user and biometric signal information collected from each of the plurality of electrocardiographs.

Provided is a real-time multi-monitoring method using an electrocardiograph and a real-time multi-monitoring method using an electrocardiograph including connecting at least one of a plurality of electrocardiographs via a network; receiving identification information of a user which is linked to an electrocardiograph to identify the plurality of electrocardiographs; collecting biometric signal information from the plurality of electrocardiographs; and analyzing a health condition in consideration of predetermined reference information which is defined for each user and biometric signal information collected from each of the plurality of electrocardiographs.

The present disclosure generally relates to user interfaces for health applications. In some embodiments, exemplary user interfaces for managing health and safety features on an electronic device are described. In some embodiments, exemplary user interfaces for managing the setup of a health feature on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described. In some embodiments, exemplary user interfaces for managing a biometric measurement taken using an electronic device are described. In some embodiments, exemplary user interfaces for providing results for captured health information on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described.

The present disclosure generally relates to user interfaces for health applications. In some embodiments, exemplary user interfaces for managing health and safety features on an electronic device are described. In some embodiments, exemplary user interfaces for managing the setup of a health feature on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described. In some embodiments, exemplary user interfaces for managing a biometric measurement taken using an electronic device are described. In some embodiments, exemplary user interfaces for providing results for captured health information on an electronic device are described. In some embodiments, exemplary user interfaces for managing background health measurements on an electronic device are described.

A wearable device is provided. The wearable device includes a first sensor having a plurality of electrodes, and at least one processor electrically connected to the first sensor. The at least one processor may obtain a first electrocardiogram signal by using a first sensor in a state where the wearable device is worn on a user's body, obtain an electromyogram signal from the first electrocardiogram signal, obtain a second electrocardiogram signal by filtering the electromyogram signal from the first electrocardiogram signal, determine the wearing state of the wearable device on the basis of the intensity of the electromyogram signal and the quality of the second electrocardiogram signal, and output a guide on the wearing state on the basis of a determination result.

A wearable device is provided. The wearable device includes a first sensor having a plurality of electrodes, and at least one processor electrically connected to the first sensor. The at least one processor may obtain a first electrocardiogram signal by using a first sensor in a state where the wearable device is worn on a user's body, obtain an electromyogram signal from the first electrocardiogram signal, obtain a second electrocardiogram signal by filtering the electromyogram signal from the first electrocardiogram signal, determine the wearing state of the wearable device on the basis of the intensity of the electromyogram signal and the quality of the second electrocardiogram signal, and output a guide on the wearing state on the basis of a determination result.

Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

A portable electrocardiographic device includes an electrode unit configured to be brought into contact with a predetermined location of a subject's body and detect an electrocardiographic waveform, a setting unit configured to set lead system used in detection of the electrocardiographic waveform, among a plurality of types of lead systems, an analysis unit configured to analyze the electrocardiographic waveform detected by the electrode unit in accordance with the lead system set through the setting unit, and an storage unit configured to store the electrocardiographic waveform detected at the electrode unit, the lead system set through the setting unit, and an analysis result of the electrocardiographic waveform analyzed by the analysis unit are stored in association with one another.

A portable electrocardiographic device includes an electrode unit configured to be brought into contact with a predetermined location of a subject's body and detect an electrocardiographic waveform, a setting unit configured to set lead system used in detection of the electrocardiographic waveform, among a plurality of types of lead systems, an analysis unit configured to analyze the electrocardiographic waveform detected by the electrode unit in accordance with the lead system set through the setting unit, and an storage unit configured to store the electrocardiographic waveform detected at the electrode unit, the lead system set through the setting unit, and an analysis result of the electrocardiographic waveform analyzed by the analysis unit are stored in association with one another.