Described herein are user-wearable devices, and methods for use therewith, for monitoring for one or more types of arrhythmias based on a photoplethysmography (PPG) signal obtained using an optical sensor of a user-wearable device. A PPG based statistical and/or machine learning model is used to analyze a PPG signal, obtained using the optical sensor, to monitor for one or more types of arrhythmias including atrial fibrillation (AF). In response to detecting an arrhythmia based on the PPG signal, an electrocardiogram (ECG) signal is obtained using an ECG sensor of the user-wearable device. An ECG based statistical and/or machine learning model is used to analyze the ECG signal obtained using the ECG sensor of the user-wearable device to confirm or reject the arrhythmia detected based on the PPG signal and/or to perform arrhythmia discrimination. Obtained PPG and/or ECG signal segments can be provided to the model(s) to update the model(s).

A monitor recorder optimized for electrocardiography and respiratory data acquisition and processing is provided. The recorder includes a sealed housing and an electronic circuitry comprised within the sealed housing, which includes an electrocardiographic front end circuit electrically interfaced to an externally-powered micro-controller and operable to sense electrocardiographic signals through electrodes provided on the patch; the micro-controller interfaced to one or more respiratory sensors, the micro-controller operable to sample the electrocardiographic signals, to sample respiratory events detected by the one or more respiratory sensors upon receiving one or more signals from the one or more respiratory sensors, to buffer each of the respiratory event samples, to compress each of the buffered respiratory event samples, to buffer each of the compressed respiratory event samples, and to write the buffered compressed respiratory event samples and the samples of the electrocardiography signals into an externally-powered flash memory; and the memory interfaced with the micro-controller.

An extended wear electrocardiography and physiological sensor monitor recorder is provided. A set of electrical contacts extend from a bottom surface of a proximal end of a sealed housing. The sealed housing includes electronic circuitry, including an electrographic front end circuit to sense electrocardiographic signals and a micro-controller interfaced to the electrocardiographic front end circuit to sample the electrocardiographic signals. A patient feedback button located is on a top surface of the proximal end of the sealed housing and positioned above the feedback bottom on the distal end.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for visualizing, scoring, recording, and analyzing sleep data and hypnograms. In some implementations, a method includes generating and providing a representation of sleep stages that includes a sequence of elements indicating a progression of the sleep stages over time during a sleep session. In some implementations, a method includes generating and providing one or more scores based on analysis of the sleep session. In some implementations, a wearable body data recorder includes a plurality of sensors and is configured to measure and process sensor data obtained during a sleep session of a subject.

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.

An example method includes analyzing morphology and/or amplitude of each of a plurality of electrophysiological signals across a surface of a patient's body to identify candidate segments of each signal satisfying predetermined conduction pattern criteria. The method also includes determining a conduction timing parameter for each candidate segment in each of the electrophysiological signals.

Methods and systems are described for sensing and recovery of a biological signal using generative-model-based compressive sensing. A transformation is applied to sparsify the quasi-periodic signal removing morphology parameters and leaving temporal parameters. The sparsified signal is sampled and the sampled signal data is transmitted to a base station. A homotopy recovery algorithm is applied to the received sampled signal data by the base station to recover the temporal parameters of the biological signal. Generative modelling is applied using previously captured morphology parameters to generate a reconstructed signal. Finally, the reconstructed signal is adjusted and scaled based on the recovered temporal parameters to provide a reconstructed signal that is diagnostically equivalent to the original biological signal.

A system and method for classifier-based atrial fibrillation detection with the aid of a digital computer are provided. Electrocardiography (ECG) features and annotated patterns of the features are maintained in a database, at least some of the patterns associated with atrial fibrillation. A classifier is trained based on the annotated patterns. A representation of an ECG signal recorded by one or more ambulatory monitors is received. ECG features in the representation falling within each of the temporal windows are detected. The trained classifier is used to identify patterns of the ECG features. At least one matrix with weights for the patterns are generated. A value indicative of whether portions of the representation are associated the patient experiencing atrial fibrillation is calculated. That one or more of the portions are associated with the patient experiencing atrial fibrillation is determined. An action is taken based on one or more of the determinations.

The disclosure relates to a method for determining a plurality of action potentials in the heart having the following steps:

a) Recording a surface ECG signal synchronously with at least 64 channels,

b) Recording at least one IEGM signal,

c) Processing the surface ECG signal by means of ICA analysis and determining the sum and position of a plurality of action potentials in the heart based on the ICA analysis, and,

d) Comparing the at least one IEGM signal to the plurality of action potentials and correcting the sum and/or position of at least one of the plurality of action potentials in the heart based on this comparison.

The disclosure further relates to a corresponding device, a corresponding computer program product, and a corresponding system.

Physiological monitoring can be provided through a lightweight wearable monitor that includes two components, a flexible extended wear electrode patch and a reusable monitor recorder that removably snaps into a receptacle on the electrode patch. The wearable monitor sits centrally on the patient's chest along the sternum oriented top-to-bottom. The placement of the wearable monitor in a location at the sternal midline, with its unique narrow hourglass-like shape, significantly improves the ability of the wearable monitor to cutaneously sense cardiac electrical potential signals, particularly the P-wave and the QRS interval signals indicating ventricular activity in the ECG waveforms. In particular, the ECG electrodes on the electrode patch are tailored to be positioned axially along the midline of the sternum for capturing action potential propagation in an orientation that corresponds to the aVF lead used in a conventional 12-lead ECG that is used to sense positive or upright P-waves.