A method for converting physiological signals includes: obtaining a first signal as a function of a time parameter, wherein the first signal represents electrocardiogram data; obtaining a second signal as a function of the time parameter, wherein the second signal represents physiological data different from the electrocardiogram data; mixing the first signal and the second signal to obtain a mixed signal; and generating a frequency spectrum pertaining to the mixed signal.

A cardiovascular detection system and method, comprising an active compression cuff contracting at a frequency higher than the systolic frequency of the heart. Meanwhile, the detection device is used to capture the influence of the active compression cuff and cardiac systole on the blood of the part to be detected. In addition, it is supplemented by electrocardiography to monitor the reference value of cardiac systole to distinguish the difference between the pulse wave generated by the active compression cuff and the pulse wave generated by the heart. In this way, the state of the cardiovascular system can be quickly understood. Since the active compression cuff is contracted at a frequency higher than the systolic frequency of the heart, it can be more accurately determined whether the blood vessel is blocked or hardened.

A system and method for automatically assessing pilot readiness via a plurality of biometric sensors includes continuously receiving biometric data including vision-based data; the biometric vision-based data is compared to a task specific set of movements and facial expressions as defined by known anchor points. A deviation is calculated based on the vision-based data and task specific set of movements and expressions, and the deviation is compared to an acceptable threshold for pilot readiness. Other biometric data may be included to refine the readiness assessment.

An electrocardiogram signal processing apparatus includes a controller and a communication unit transmitting an electrocardiogram signal. The controller is configured to (i) extract points satisfying a determined standard from the electrocardiogram signal and convert the extracted points into a two-dimensional graph expressed as frequencies for a plurality of class sections, (ii) generate a cumulative graph in which an order of the plurality of class sections is rearranged in an order of magnitude of the frequencies, and (iii) calculate a Gini index based on the cumulative graph and determine whether the electrocardiogram signal is an abnormal signal by using the Gini index.

An electrocardiogram signal processing apparatus includes a controller and a communication unit transmitting an electrocardiogram signal. The controller is configured to (i) extract points satisfying a determined standard from the electrocardiogram signal and convert the extracted points into a two-dimensional graph expressed as frequencies for a plurality of class sections, (ii) generate a cumulative graph in which an order of the plurality of class sections is rearranged in an order of magnitude of the frequencies, and (iii) calculate a Gini index based on the cumulative graph and determine whether the electrocardiogram signal is an abnormal signal by using the Gini index.

Disclosed includes a body surface device for diagnosing locations associated with electrical rhythm disorders to guide therapy. The device can sense electrical signals and determine multiple sites that may be operative in that patient. The patch may encompass the heart regions from where the heart rhythm disorder originates. The patch comprises an array of electrodes configured to detect electrical signals generated by a heart. A controller may determine the locations of interest based on detected electrical signals. The controller is configured to locate these regions relative to the surface patch. The system may be coupled to a sensor or therapy device inside the heart, to guide this device to a region of interest. The controller is further configured to instruct the operator to use the trigger or source information to treat the heart rhythm disorder in an individual using additional clinical data and methods for personalization such as machine learning.

Disclosed are various embodiments of methods, components and systems configured to determine a location of a source of cardiac arrhythmia in a patient's heart. In some embodiments, to determine a source location, electrogram signals are acquired from a region of the patients' heart using a first set of electrodes; and then a pre-trained artificial intelligence (AI) model is applied to predict the location of the cardiac arrhythmia source by using the signals. Importantly, pre-training of the AI model comprises acquiring electrogram signals from explanted human hearts, the signals are generated by a second set of electrodes assembled into an electrode array that covers at least a part of the explanted human heart, and acquiring co-registered functional and/or structural imaging data in the part of the explanted human heart covered with the electrode array, wherein the functional and/or structural imaging data provide location of at least one source of cardiac arrhythmia.

A method includes receiving intracardiac electrogram (IEGM) signals measured at a plurality of locations in a region of a heart that contains a His bundle of the heart. The IEGM signals are processed to find respective local activation time (LAT) values. A cluster of the locations is identified at which peaks associated with the LAT values occur later than a defined time. Respective time differences are calculated between times of occurrence of the associated peaks and a reference time. The time differences are compared to a threshold value to retain locations for which the time differences are below the threshold value. The respective IEGM signals are filtered to identify respective high-frequency peaks in the IEGM signals. The high-frequency peaks are cross-corelated to identify a subset of the locations whose high-frequency peaks meet a predefined cross-correlation level. The high-frequency peaks are tagged as His peaks and indicated on a cardiac map.

A method includes receiving intracardiac electrogram (IEGM) signals measured at a plurality of locations in a region of a heart that contains a His bundle of the heart. The IEGM signals are processed to find respective local activation time (LAT) values. A cluster of the locations is identified at which peaks associated with the LAT values occur later than a defined time. Respective time differences are calculated between times of occurrence of the associated peaks and a reference time. The time differences are compared to a threshold value to retain locations for which the time differences are below the threshold value. The respective IEGM signals are filtered to identify respective high-frequency peaks in the IEGM signals. The high-frequency peaks are cross-corelated to identify a subset of the locations whose high-frequency peaks meet a predefined cross-correlation level. The high-frequency peaks are tagged as His peaks and indicated on a cardiac map.

Disclosed includes a body surface device for diagnosing locations associated with electrical rhythm disorders to guide therapy. The device can sense electrical signals and determine multiple sites that may be operative in that patient. The patch may encompass the heart regions from where the heart rhythm disorder originates. The patch comprises an array of electrodes configured to detect electrical signals generated by a heart. A controller may determine the locations of interest based on detected electrical signals. The controller is configured to locate these regions relative to the surface patch. The system may be coupled to a sensor or therapy device inside the heart, to guide this device to a region of interest. The controller is further configured to instruct the operator to use the trigger or source information to treat the heart rhythm disorder in an individual using additional clinical data and methods for personalization such as machine learning.