A dashboard centered around arrhythmia or atrial fibrillation tracking is provided. The dashboard includes a heart or cardiac health score that can be calculated in response to data from the user such as their ECG and other personal information and cardiac health influencing factors. The dashboard also provides to the user recommendations or goals, such as daily goals, for the user to meet and thereby improve their heart or cardiac health score. These goals and recommendations may be set by the user or a medical professional and routinely updated as his or her heart or cardiac health score improves or otherwise changes. The dashboard is generally displayed from an application provided on a smartphone or tablet computer of the user.

Techniques are provided for generating and processing compressed sensor data. Sensor signals can be collected using one or more sensors. The sensor signals can be compressed using a compression data structure. In some instances, the compressed signal corresponds to a sampling rate at or below the Nyquist sampling rate. The compressed signal can be compared to one or more templates. Events within the compressed signal can be detected and characterized based on the comparison.

A variety of convolutional neural network based shockable heart rhythm classifiers are described. The neural network is configured to receive an electrocardiogram segment as an input and to generate an output indicative of whether the received electrocardiogram segment represents a heart rhythm that is suitable for treatment by a defibrillation shock. Preferably, the received electrocardiogram segment is not transformed or processed prior to its reception by the convolutional neural network and no features of the electrocardiogram are identified to the convolutional neural network. In some embodiments, the received electrocardiogram segment is the sole input to the convolutional neural network. The described classifier is well suited for use in defibrillators.

A system can include a near-field instrument to be placed inside a chamber of a heart, a far-field instrument to be placed in a stable position in relation to the heart, and a control unit. The control unit is configured to identify a unique pattern in electrogram information received from the far-field instrument when the near-field instrument is in one or more positions within the heart. When the unique pattern is detected, the control unit is configured to receive electrogram information from the near-field instrument. While recording electrogram information from the near-field instrument, the control unit is also configured to record voltage and complex fractionated atrial electrogram (CFAE) characteristics of the tissue inside a heart chamber. This information combined with rotor information can be used to identify substrate versus non-substrate rotor characteristics.

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.

Methods and systems are provided for detecting arrhythmias in cardiac activity is provided. The method and systems are under control of one or more processors configured with specific executable instructions. The method and systems obtain a far field cardiac activity (CA) signal that includes a series of beats, the CA signal including paced events. The method and systems identify the paced events in the CA signals. The method and systems determine a score based on an amount of paced events and adjust at least one parameter of an atrial fibrillation (AF) detection process based on the score.

A method for analysis of cardiac rhythms, based on calculations of entropy and moments of interbeat intervals. An optimal determination of segments of data is provided that demonstrate statistical homogeneity, specifically with regard to moments and entropy. The invention also involves calculating moments and entropy on each segment with the goal of diagnosis of cardiac rhythm. More specifically, an absolute entropy measurement is calculated and provided as a continuous variable, providing dynamical information of fundamental importance in diagnosis and analysis. Through the present invention, standard histograms, thresholds, and categories can be avoided.

According to at least one example, an ambulatory medical device is provided. The device includes a plurality of electrodes disposed at spaced apart positions about a patient's body and a control unit. The control unit includes a sensor interface, a memory and a processor. The sensor interface is coupled to the plurality of electrodes and configured to receive a first ECG signal from a first pairing of the plurality of electrodes and to receive a second ECG signal from a second pairing of the plurality of electrodes. The memory stores information indicating a preferred pairing, the preferred pairing being either the first pairing or the second pairing. The processor is coupled to the sensor interface and the memory and is configured to resolve conflicts between interpretations of first ECG signal and the second ECG signal in favor of the preferred pairing.

A medical device is configured to detect an atrial tachyarrhythmia episode. The device senses a cardiac signal, identifies R-waves in the cardiac signal attendant ventricular depolarizations and determines classification factors from the R-waves identified over a predetermined time period. The device classifies the predetermined time period as one of unclassified, atrial tachyarrhythmia and non-atrial tachyarrhythmia by comparing the determined classification factors to classification criteria. A classification criterion is adjusted from a first classification criterion to a second classification criterion after at least one time period being classified as atrial tachyarrhythmia. An atrial tachyarrhythmia episode is detected by the device in response to at least one subsequent time period being classified as atrial tachyarrhythmia based on the adjusted classification criterion.

A method for detecting heart rhythm irregularities, including the steps of: measuring pulse peaks continuously over a period of time to obtain pulse peak sequence and pulse peak time interval sequence corresponding to the pulse peak sequence; and calculating the absolute value of a difference between a single pulse peak time interval in the pulse peak time interval sequence and an average value of the pulse peak time interval sequence, and determining that an irregular pulse peak (IPP) has occurred if the absolute value is larger than or equal to 15% to 25% of the average value of the pulse peak time interval sequence. A detection device using the method is also provided. This uses a non-invasive approach to detect heart rhythm irregularities such as sick sinus syndrome, irregular pulse peaks, irregular heartbeat, and atrial fibrillation, and can be applied to a sphygmomanometer or a wearable device.