A wearable device and respiration sensing module are provided. The wearable device includes a first respiration sensing module and a second respiration sensing module. The first respiration sensing module is configured to sensing respiration of a user to obtain a first respiration information. The second respiration sensing module is configured to sensing respiration of the user to obtain a second respiration information. The second respiration sensing module includes a substrate, a first electrode, a second electrode and a stretchable conductive element. The first electrode and the second electrode are disposed on a first surface of the substrate. The stretchable conductive element is physically and electrically connected between the first electrode and the second electrode. The respiration of the user is judged according to the first respiration information and the second respiration information.

A wearable device and respiration sensing module are provided. The wearable device includes a first respiration sensing module and a second respiration sensing module. The first respiration sensing module is configured to sensing respiration of a user to obtain a first respiration information. The second respiration sensing module is configured to sensing respiration of the user to obtain a second respiration information. The second respiration sensing module includes a substrate, a first electrode, a second electrode and a stretchable conductive element. The first electrode and the second electrode are disposed on a first surface of the substrate. The stretchable conductive element is physically and electrically connected between the first electrode and the second electrode. The respiration of the user is judged according to the first respiration information and the second respiration information.

Techniques are disclosed for explaining and visualizing an output of a machine learning system that detects cardiac arrhythmia in a patient. In one example, a computing device receives cardiac electrogram data sensed by a medical device. The computing device applies a machine learning model, trained using cardiac electrogram data for a plurality of patients, to the received cardiac electrogram data to determine, based on the machine learning model, that an episode of arrhythmia has occurred in the patient and a level of confidence in the determination that the episode of arrhythmia has occurred in the patient. In response to determining that the level of confidence is greater than a predetermined threshold, the computing device displays, to a user, a portion of the cardiac electrogram data, an indication that the episode of arrhythmia has occurred, and an indication of the level of confidence that the episode of arrhythmia has occurred.

A myocardial excitation complementation/visualization apparatus includes an acquiring section that acquires intracardiac electrocardiograms of a subject, the intracardiac electrocardiograms being recorded by a recording unit having a plurality of electrodes, a processing section that performs a computation for completing and visualizing a state of excitation in a myocardium of the subject based on the intracardiac electrocardiograms, and a displaying section that displays the state of excitation in the myocardium of the subject based on an output of the processing section. The processing section includes a first generating section, a correcting section, a second generating section, and a third generating section. The displaying section displays a change of the state of excitation in the myocardium of the subject based on the visualized data.

The invention provides a body-worn monitor that measures a patient's vital signs (e.g. blood pressure, SpO2, heart rate, respiratory rate, and temperature) while simultaneously characterizing their activity state (e.g. resting, walking, convulsing, falling). The body-worn monitor processes this information to minimize corruption of the vital signs by motion-related artifacts. A software framework generates alarms/alerts based on threshold values that are either preset or determined in real time. The framework additionally includes a series of heuristic rules that take the patient's activity state and motion into account, and process the vital signs accordingly. These rules, for example, indicate that a walking patient is likely breathing and has a regular heart rate, even if their motion-corrupted vital signs suggest otherwise.

An ECG system measures and annotates the P-point of an ECG waveform from harmonic waveforms. Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients. A discontinuity point is identified as the P-point of the ECG waveform from the comparison. Similar measurements are made for the P-point, I-point, J-point, and T-point. Distances from these points to the equipotential line are calculated and used to detect blockages leading to myocardial infarction.

Certain exemplary aspects of the present disclosure are directed towards methods and apparatuses for conducting physical examinations of a human. Optionally, such embodiments permit for remote examination of a patient, for example, the patient's heart or lung region. In such embodiments, a user operates a remote physical examination sensor, while a remote examination computer and/or remote medical personnel reviews/analyzes medical data received from the remote physical examination sensor to diagnose the condition of the user. The remote physical examination instrument may be equipped with a plurality of skin-compatible electrodes on a remote examination sensor connected to the user's chest, as well as one or more electrodes on the top cover or sides of the remote examination sensor connecting to the user's hand and providing medical data to the remote examination computer.

A myocardial excitation complementation/visualization apparatus includes an acquiring section that acquires intracardiac electrocardiograms of a subject, the intracardiac electrocardiograms being recorded by a recording unit having a plurality of electrodes, a processing section that performs a computation for completing and visualizing a state of excitation in a myocardium of the subject based on the intracardiac electrocardiograms, and a displaying section that displays the state of excitation in the myocardium of the subject based on an output of the processing section. The processing section includes a first generating section, a correcting section, a second generating section, and a third generating section. The displaying section displays a change of the state of excitation in the myocardium of the subject based on the visualized data.

A method of detecting atrial fibrillation includes detecting a pulse signal to obtain a time pulse waveform and converting it to an energy spectrum waveform via Fast Fourier Transform. The energy spectrum waveform includes a first frequency region, a second frequency region, and a third frequency region. The number of spikes in each frequency region was calculated and the heart indexes of the first, second, and third frequency regions were obtained, which were the first heart index, the second heart index, and the third heart index. And by the sum of the three heart index values and the first heart index to determine the possibility of atrial fibrillation. An apparatus for detecting atrial fibrillation is also provided, whereby the user can determine the possibility and predicting atrial fibrillation by simple measurement of blood pressure at home.

The invention provides a body-worn monitor that measures a patient's vital signs (e.g. blood pressure, SpO2, heart rate, respiratory rate, and temperature) while simultaneously characterizing their activity state (e.g. resting, walking, convulsing, falling). The body-worn monitor processes this information to minimize corruption of the vital signs by motion-related artifacts. A software framework generates alarms/alerts based on threshold values that are either preset or determined in real time. The framework additionally includes a series of heuristic rules that take the patient's activity state and motion into account, and process the vital signs accordingly. These rules, for example, indicate that a walking patient is likely breathing and has a regular heart rate, even if their motion-corrupted vital signs suggest otherwise.