An artificial intelligence self-learning-based automatic electrocardiography analysis method and apparatus, the method comprising data preprocessing, heartbeat feature detection, interference signal detection and heartbeat classification based on deep learning, signal quality evaluation and lead combination, heartbeat verification, analysis and calculation of electrocardiography events and parameters, and finally automatic output of reporting data, realizing an automated analysis method having a complete and rapid flow. The automatic electrocardiography analysis method may also record modification information of an automatic analysis result, collect modified data, and feed same back to the depth learning model to continue training, thereby continuously making improvements and improving the accuracy of the automatic analysis method.

A bridge device includes a housing, a plurality of electrodes exposed outside of the housing such that at least two of the plurality of electrodes can be concurrently placed in contact with a patient's skin. A controller is disposed within the housing. A first communications module is operably coupled to the controller and to the at least two of the plurality of electrodes. The first communications module is configured to allow the controller to communicate with an implantable medical device via at least two of the plurality of electrodes using conducted communication. A second communications module is operably coupled to the controller and is configured to allow the controller to communicate with a remote device external to the patient.

A method for providing personalized health assessment of a subject includes: receiving a raw electrocardiography (ECG) signal of a subject from an ECG device and a raw blood pressure (BP) signal of the subject from a BP waveform detector; calculating a beat-to-beat ECG signal from the raw ECG signal; calculating a beat-to-beat BP signal from the raw BP signal; calculating a beat-to-beat pulse arrival time (PAT) signal that is measured as a time delay between the beat-to-beat ECG signal and the beat-to-beat BP signal; calculating an interpolated beat-to-beat PAT signal and an interpolated beat-to-beat BP signal by interpolating the beat-to-beat PAT signal and the beat-to-beat BP signal, respectively; assessing a subject-specific relationship between the interpolated beat-to-beat PAT signal and the interpolated beat-to-beat BP signal; and estimating a real-time blood pressure of the subject based on the subject-specific relationship.

A method for providing personalized health assessment of a subject includes: receiving a raw electrocardiography (ECG) signal of a subject from an ECG device and a raw blood pressure (BP) signal of the subject from a BP waveform detector; calculating a beat-to-beat ECG signal from the raw ECG signal; calculating a beat-to-beat BP signal from the raw BP signal; calculating a beat-to-beat pulse arrival time (PAT) signal that is measured as a time delay between the beat-to-beat ECG signal and the beat-to-beat BP signal; calculating an interpolated beat-to-beat PAT signal and an interpolated beat-to-beat BP signal by interpolating the beat-to-beat PAT signal and the beat-to-beat BP signal, respectively; assessing a subject-specific relationship between the interpolated beat-to-beat PAT signal and the interpolated beat-to-beat BP signal; and estimating a real-time blood pressure of the subject based on the subject-specific relationship.

The differential voltage measuring system has a number of signal measuring circuits, each having a capacitive sensor element for capturing a measurement signal relating to the patient. The differential voltage measuring system further has a signal processing apparatus for determining at least one bioelectrical signal from the measurement signals and a computer unit which is configured to ascertain, on the basis of the at least one bioelectrical signal, and to provide, an item of breathing information, said breathing information indicating a breathing activity of the patient.

An apparatus for estimating a core body temperature of an object is provided. According to one embodiment, the apparatus includes a plurality of sensors configured to obtain data from an object and a processor configured to obtain a surface temperature, a heat flux, a skin blood flow rate, and a blood flow velocity by using the data obtained from the plurality of sensors, obtain a skin thermal conductivity based on the skin blood flow rate, and estimate a core body temperature of the object based on the surface temperature, the heat flux, the skin thermal conductivity, and the blood flow velocity of the object.

A modular wristband and sensor system and method of using the same are disclosed. The system uses top and bottom sensor modules that contain conductive ports for connection to wristbands' conductive ports. The wristbands' conductive ports are electrically connected to wires embedded within each wristband segment. These allow for the transfer of data and power between the bands and the top and bottom sensor modules. Having power and data conducted through wristbands into sensors makes it possible for wristband sensors to have swappable bands while maintaining connectivity. Other embodiments include a wristband that includes swappable top and bottom sensor modules communicating wirelessly with each other.

A modular wristband and sensor system and method of using the same are disclosed. The system uses top and bottom sensor modules that contain conductive ports for connection to wristbands' conductive ports. The wristbands' conductive ports are electrically connected to wires embedded within each wristband segment. These allow for the transfer of data and power between the bands and the top and bottom sensor modules. Having power and data conducted through wristbands into sensors makes it possible for wristband sensors to have swappable bands while maintaining connectivity. Other embodiments include a wristband that includes swappable top and bottom sensor modules communicating wirelessly with each other.

Provided is a body-attachable biometric signal acquisition device. The body-attachable biometric signal acquisition device includes: a main body including a sensor acquiring a biometric signal in a state of closely adhering to a surface of a body; and a first fixing portion winding the body to fix the main body to the body, wherein the first fixing portion includes a first strap winding the body, the first strap includes a framework formed of an elastic material, and when the first strap winds the body to fasten the main body and the first fixing portion to the body, a pressure applied by the sensor to the surface of the body is generated by an elastic restoring moment generated by the framework, and a gap is present between a portion of an outer surface of the first strap, and the surface of the body, the portion being positioned between the framework and the surface of the body. An air cushion is provided to prevent wobbling of the main body caused by an excessive space formed due to body size variation.

An arousal state estimation apparatus includes: a feature value acquisition unit acquiring a plurality of types of feature values regarding an arousal state of a human body from physiological data obtained by measuring the human body; and an estimation unit estimating the arousal state of the human body by using a principal feature value that is some type among the plurality of types of feature values. In a case where the principal feature value is unacquirable due to a defect of the physiological data, the estimation unit estimates the arousal state of the human body by using a different type of feature value than the principal feature value among the plurality of types of feature values acquired by the feature value acquisition unit instead of the unacquirable principal feature value.