A system including a sensor interface coupled to a processor. The sensor interface is configured to receive and process an analog electrocardiogram signal of a subject and provide a digitized electrocardiogram signal sampled over a first time period and a second time period that is subsequent to the first time period. The processor is configured to receive the digitized electrocardiogram signal, to analyze a frequency domain transform of the digitized electrocardiogram signal sampled over the first and second time periods and determine first and second metrics indicative of metabolic state of a myocardium of the subject during the first and second time periods, respectively, to compare the first and second metrics to determine whether the metabolic state of the myocardium of the subject is improving, and to indicate administration of an intervention to the subject in response to a determination that the metabolic state is not improving.

A Wearable Cardiac Defibrillator (WCD) system is configured to be worn by a patient who carries a mobile communication device. The mobile communication device has a user interface that is configured to enable the patient to enter wireless inputs. The WCD system includes a communication module that is configured to establish a local comlink with the mobile communication device. The WCD system also includes a tethered action unit that has a user interface configured to enable the patient to enter action inputs. The WCD system can perform some of its functions in response to the action inputs or to the wireless inputs. Since the wireless inputs can be provided from the mobile communication device instead of the action unit, the patient is less likely to attract attention when entering them, and thus exhibit better compliance.

The invention relates to the system and method of remote ECG monitoring, remote disease screening, and early-warning system based on wavelet analysis. The system includes a wireless ECG signal acquisition device, a mobile terminal, and a cloud storage platform. The wireless ECG signal acquisition device worn on the user's chest is used to collect ECG signals anywhere and anytime. The method includes transmitting the ECG signals to the mobile terminal using the wavelet analysis algorithm, analyzing and processing the received ECG signal, and uploading the processed ECG signals to the cloud storage platform. The cloud storage platform stores users' personal information and ECG signals. According to the ECG features detection with support vector machine learning algorithm for heart diseases diagnosis and features classification, the system gives feedback report and proposal, and transmits them to the mobile terminal.

A method for processing electrocardiograph (ECG) data using a garment includes determining, by a processor, a current working lead from ECG leads formed in advance using flexible electrodes in the garment based on a current ECG monitor type, and receiving, by the processor through lead wires corresponding to the current working lead, ECG data collected by flexible electrodes corresponding to the current working lead. A wearable apparatus for processing ECG data includes at least two flexible electrodes, in which the at least two flexible electrodes are capable of forming different leads based on predetermined configurations, at least two lead wires, and an ECG data collector configured to receive ECG data collected by the at least two flexible electrodes, in which each of the at least two flexible electrodes connects to the ECG data collector via at least one of the at least two lead wires.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and/or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

A system and method for display of subcutaneous cardiac monitoring data are provided. Cutaneous action potentials of a patient and other sensed data associated with the patient are recorded as electrocardiogram (EGC) data over a set time period using a subcutaneous insertable cardiac monitor. A set of R-wave peaks is identified within the ECG data and an R-R interval plot is constructed. A difference between recording times of successive pairs of the R-wave peaks in the set is determined. A heart rate associated with each difference is also determined. The pairs of the R-wave peaks and associated heart rate are plotted as the R-R interval plot. A diagnosis of cardiac disorder is facilitated based on patterns of the plotted pairs of the R-wave peaks, the associated heart rates in the R-R interval plot, and background data based on the other sensed data.

A medical device is configured to sense an electrical signal and determine that signal to noise criteria are met based on electrical signal segments stored in response to sensed electrophysiological events. The medical device is configured to determine an increased gain signal segment from one of the stored electrical signal segments in response to determining that the signal to noise criteria are met. The medical device determines a noise metric from the increased gain signal segment. The stored electrical signal segment associated with the increased gain signal segment may be classified as a noise segment in response to the noise metric meeting noise detection criteria.

A method of determining a fused sensor measurement is disclosed including: obtaining sensor measurements from sensors detecting a same type of physiological measurement; determining a signal quality index (SQI) of each sensor including determining an extent to which a sensor measurement differs from others among the sensor measurements obtained from each sensor; determining a weightage of each sensor based on the SQI of each sensor; and determining a fused sensor measurement from the plurality of sensors based on the weightage of each sensor and filtered sensor measurements of each sensor obtained from a Kalman filter operation. A vehicle safety system includes: a vehicle electronic control unit configured to: determine the sensor measurement extent, to determine the SQI of each sensor, determine the weightage of each sensor, determine the fused sensor measurement, determine the occupant's physiological condition, and if the physiological condition is abnormal, perform at least one vehicle operation.

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.