A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine an R-wave of the cardiac signal and determine whether the R-wave is noisy. Based on the R-wave being noisy, the processing circuitry is configured to determine whether the cardiac signal around a determined T-wave is noisy. Based on the cardiac signal around the determined T-wave not being noisy, the processing circuitry is configured to determine a QT interval or a corrected QT interval based on the determined T-wave and the determined R-wave.

In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

In an aspect, the present disclosure provides a method comprising: (a) identifying a first negative and positive electromagnetic dipoles in a first electromagnetic field map associated with a heart of the individual at a first time; (b) identifying a second negative and positive electromagnetic dipoles in a second electromagnetic field map associated with the heart of the individual at a second time; (c) determining a first angle based on the first negative and positive electromagnetic dipoles; (d) determining a second angle based on the second negative and positive electromagnetic dipoles; and (e) determining a presence, an absence, or a likelihood of coronary artery disease in the individual, based at least in part on (i) whether the first angle differs from the second angle by at least 100 degrees, or (ii) whether there is a presence of a third electromagnetic dipole in the first or the second electromagnetic field map.

Systems and methods are provided for analyzing electrocardiogram (ECG) data of a patient using a substantial amount of ECG data. The systems receive ECG data from a sensing device positioned on a patient such as one or more ECG leads. The system may include an application that communicates with an ECG platform running on a server(s) that processes and analyzes the ECG data, e.g., using neural networks for delineation of the cardiac signal and classification of various abnormalities, conditions and/or descriptors. The processed ECG data is communicated from the server(s) for display in a user-friendly and interactive manner with enhanced accuracy.

The present teachings relate to monitoring the condition of a subject with a contactless system for sensing biopotential signals comprising: a support surface; one or more inner layers; a plurality of contactless electrode units within the one or more inner layers; one or more outer layers; and wherein the plurality of contactless electrode units are arranged in an inner shape within an outer shape such that the contactless electrode units form the vertices of the inner shape and the outer shape. The method includes the steps of: providing a support surface having one or more sensing devices embedded therein; positioning the subject at least partially on the support surface; acquiring data from an electrocardiograph reading on the subject for a predetermined amount of time; outputting the data of the step (c); and analyzing the data of the step (c), by identifying one or more biomarkers consistent with a disease condition.

Methods, systems, and devices for signal analysis in an implanted cardiac monitoring and treatment device such as an implantable cardioverter defibrillator. In some illustrative examples, detected events are analyzed to identify changes in detected event amplitudes. When detected event amplitudes are dissimilar from one another, a first set of detection parameters may be invoked, and, when detected event amplitudes are similar to one another, a second set of detection parameters may be invoked. Additional methods determine whether the calculated heart rate is “high” or “low,” and then may select a third set of detection parameters for use when the calculated heart rate is high.

A method for determining the location of a medical device within a body is provided. The method includes transmitting from the medical device an acoustic signal; receiving with the medical device a reflected acoustic signal; advancing the medical device based on a first algorithm, the first algorithm including a first weighting factor and a first feature extracted from the reflected acoustic signal; determining a first location of the medical device based on the first algorithm; and moving the medical device to a second location based on a second algorithm, the second algorithm based on the determined first location and including at least one of a second weighting factor and a second feature extracted from the reflected acoustic signal. Also disclosed are systems and devices for performing the methods described herein.

The present invention discloses an atrial fibrillation detection device, method, system and storage medium. The device includes an ECG signal processing module, configured to identify positions of Q, R, S and T points of all heartbeats in an ECG signal acquired in a preset time, and determine a RR interval, a P point amplitude, an R point amplitude and a TQ segment waveform of each heartbeat according to the positions of the P, Q, R, S and T positions; and a detection module, configured to acquire an integrated score and perform conditional judgment on the integrated score, wherein the fifth score is a quotient of a total number of f waves in all the TQ segment waveforms in the ECG signal to a total number of the TQ segment waveforms involved in the ECG signal.