An improved wearable device for detecting progression of Cardiac Amyloidosis based on changes in relative values of characteristics of P-wave and R-wave is disclosed. In an embodiment of the invention, two electrodes the device are connected to user's skin surface to obtain traces of ECG signals. Thereafter, correction factors are determined for the obtained traces of ECG signals. A microprocessor included in the device applies correction factors on the traces of ECG signals to obtain characteristics of P-wave and R-wave. Finally, the microprocessor determines the ratio of the characteristics (such as amplitude) of the P-wave to the characteristics (such as amplitude) of the R-wave and records said ratio. Still further, the microprocessor compares all such recorded ratios or features, to determine and display if there is disease progression.

An improved wearable device for detecting progression of Cardiac Amyloidosis based on changes in relative values of characteristics of P-wave and R-wave is disclosed. In an embodiment of the invention, two electrodes the device are connected to user's skin surface to obtain traces of ECG signals. Thereafter, correction factors are determined for the obtained traces of ECG signals. A microprocessor included in the device applies correction factors on the traces of ECG signals to obtain characteristics of P-wave and R-wave. Finally, the microprocessor determines the ratio of the characteristics (such as amplitude) of the P-wave to the characteristics (such as amplitude) of the R-wave and records said ratio. Still further, the microprocessor compares all such recorded ratios or features, to determine and display if there is disease progression.

A system for accounting for adverse pacing artifacts may include an electrode apparatus and a computing apparatus. The electrode apparatus may include one or more external electrodes to monitor electrical activity from tissue of a patient. The computing apparatus may include one or more processors and may be operatively coupled to the electrode apparatus. The computing apparatus may be configured to monitor electrical activity from the tissue of the patient using the one or more external electrodes. Such electrical activity may be used to generate one or more cardiac signals of the patient over time. The computing apparatus may detect a pacing artifact in the one or more cardiac signals and determine whether to account for, and account for, the pacing artifact based on a pacing artifact characteristic of the pacing artifact when producing electrical heterogeneity information.

A system for accounting for adverse pacing artifacts may include an electrode apparatus and a computing apparatus. The electrode apparatus may include one or more external electrodes to monitor electrical activity from tissue of a patient. The computing apparatus may include one or more processors and may be operatively coupled to the electrode apparatus. The computing apparatus may be configured to monitor electrical activity from the tissue of the patient using the one or more external electrodes. Such electrical activity may be used to generate one or more cardiac signals of the patient over time. The computing apparatus may detect a pacing artifact in the one or more cardiac signals and determine whether to account for, and account for, the pacing artifact based on a pacing artifact characteristic of the pacing artifact when producing electrical heterogeneity information.

Systems and methods for electrocardiographic waveform analysis, data presentation and actionable alert generation are described. Electrocardiographic waveform data can be received from a wearable device associated with a patient. A mathematical analysis of at least a portion of the electrocardiographic waveform data can be performed to provide cardiac analytics. In instances where (1) a pathologically prolonged QT interval and (2) an R on T premature ventricular contraction and/or a ventricular tachycardia are detected from the cardiac analytics of the at least a portion of the electrocardiographic waveform data, an actionable alert can be generated and displayed with a visualization of the cardiac analytics.

Systems and methods for electrocardiographic waveform analysis, data presentation and actionable alert generation are described. Electrocardiographic waveform data can be received from a wearable device associated with a patient. A mathematical analysis of at least a portion of the electrocardiographic waveform data can be performed to provide cardiac analytics. In instances where (1) a pathologically prolonged QT interval and (2) an R on T premature ventricular contraction and/or a ventricular tachycardia are detected from the cardiac analytics of the at least a portion of the electrocardiographic waveform data, an actionable alert can be generated and displayed with a visualization of the cardiac analytics.

Embodiments of the present disclosure relate to providing GUIs for visualizing ECG data and integrated QT analysis results. A system may comprise an electrocardiogram (ECG) monitoring device to measure an ECG of a user to generate ECG data and transmit the ECG data. The system may further comprise a cloud analysis platform to receive the ECG data, perform a QT analysis based on the ECG data to generate QT analysis results, and transmit the QT analysis results. The system may further comprise a computing device to receive the QT analysis results and provide a graphical user interface (GUI) to visualize the ECG data with the QT analysis results integrated therein.

Embodiments of the present disclosure relate to providing GUIs for visualizing ECG data and integrated QT analysis results. A system may comprise an electrocardiogram (ECG) monitoring device to measure an ECG of a user to generate ECG data and transmit the ECG data. The system may further comprise a cloud analysis platform to receive the ECG data, perform a QT analysis based on the ECG data to generate QT analysis results, and transmit the QT analysis results. The system may further comprise a computing device to receive the QT analysis results and provide a graphical user interface (GUI) to visualize the ECG data with the QT analysis results integrated therein.

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.

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.