A system (101) for monitoring a physiological condition of a user (104) is disclosed herein. The system (101) includes a receiving module (110) configured to receive a plurality of short-term segments of Heart Rate Variability (HMI) (302) or short-term electrocardiogram (ECG) segments (402) or short voice recordings (602) from the user (104) recorded at different time points. The system includes a stitching module (114) for stitching the plurality of short-term segments and creating a stitched segment. The system further includes an extracting module (116) extracting feature from the stitched segment and a predicting module (118) for predict the physiological condition, based on the feature.

A physiological signal monitoring system includes a single set of sensing electrodes to provide conditioned physiological signals to a primary monitoring device and a secondary monitoring device. The monitoring system includes pre-processing circuitry configured to receive a raw physiological signal. The pre-processing circuitry is configured to produce a primary physiological signal and a secondary physiological signal. Each of the primary and secondary physiological signals are conditioned. The primary conditioned physiological signal is directed to a primary monitoring device such as a hospital wearable defibrillator device. The secondary conditioned physiological signal is directed to telemetry modeling circuitry where it is further processed to output one or more telemetry signals. The one or more telemetry signals are output to a secondary monitoring device such as a three lead ECG monitoring device. Thus, a single set of sensing electrodes can provide physiological signals to multiple monitoring devices.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to estimate the action potential wave propagation in a patient's heart, and subsequently to detect at least one location or type of at least one source of, or rotational phenomenon associated with, at least one cardiac rhythm disorder using intracardiac electrodes and a modified multi-frame Horn-Schunck algorithm to generate a map corresponding to a spatial map, the map being configured to reveal on a monitor or display to a user the at least one location of the at least one source of the at least one cardiac rhythm disorder.

Disclosed are various examples and embodiments of systems, devices, components and methods configured to estimate the action potential wave propagation in a patient's heart, and subsequently to detect at least one location or type of at least one source of, or rotational phenomenon associated with, at least one cardiac rhythm disorder using intracardiac electrodes and a modified multi-frame Horn-Schunck algorithm to generate a map corresponding to a spatial map, the map being configured to reveal on a monitor or display to a user the at least one location of the at least one source of the at least one cardiac rhythm disorder.

Computer-aided diagnosis algorithms require a large volume of training data. The existing methods for simulating artificial biomedical signals are mostly based on physics driven mathematical models that require too many assumptions, making them challenging to simulate on a large scale. Alternatively, conventional deep learning-based approaches are pure data driven and hence, do not have physiological interpretation. The present disclosure provides a method that effectively combines both physiological domain knowledge and deep learning to enable simulation of realistic cardiovascular disease specific biomedical signals. An ensemble Generative Adversarial Network (GAN) including a Long Short-Term Memory GAN (LSTM-GAN) configured to generate a Heart Rate Variability (HRV) pattern associated with the cardiovascular disease condition and a Deep Convolutional GAN (DCGAN) configured to create a morphology of a representative cardiac cycle is provided. A complete waveform is simulated by combining an output from each GAN.

A data generation method by a computer is disclosed. First waveform data including marking information at a first position on a waveform, and acquiring second waveform data are acquired. A transformation function is specified that transforms the first waveform data to reduce the difference between a first value of a time axis for a first characteristic point in the first waveform data and a second value of the time axis for a second characteristic point, in the second waveform data, corresponding to the first characteristic point. Third waveform data are generated, in which the marking information is applied at a second position corresponding to the first position in the first waveform data, the second position being determined by using the transformation function.

A method is provided that includes determining a quality of a data portion of an input sensor data stream based, at least in part, on data of a first data type and determining between, at least, generation of two or more streams of a second, different data type including at least one synthesised data stream of the second data type. Determining between generation of two or more streams of a second, different data type is based, at least in part, on the determined quality. The synthesis is based, at least in part, on the data of the first data type. The method further includes causing generation of at least one stream of the second, different data type based, at least in part, on the determination between generation of two or more streams of the second, different data type.

Methods and systems are provided for automatically diagnosing an electrocardiogram (ECG) using a hybrid system comprising a rule-based system and one or more deep neural networks. In one embodiment, by mapping ECG data to a plurality of features using a convolutional neural network, mapping the plurality of features to a preliminary diagnosis using a decision network, and determining a diagnosis based on the ECG data and the preliminary diagnosis using the rule-based system, a more accurate diagnosis may be determined. In another example, by incorporating both a rule-based system and one or more deep neural networks into the hybrid system, the hybrid system may be more easily adapted for use in various contexts/communities, as the one or more deep learning networks may be trained using context/community specific ECG data.

Embodiments of the present disclosure provide systems and methods for providing an audio user interface by which a user may be provided audio instructions for measuring physiological parameters using a monitoring device as well as an audio explanation of the results of analysis of the physiological parameters. A cloud service may receive via a smart device a voice command associated with measuring physiological parameters and provide to the smart device, instructions for measuring the physiological parameters. The monitoring device is configured to communicate with the cloud service via the smart device, which may provide the instructions for taking the ECG using the ECG monitoring device to a user as an audio output. The cloud service may receive physiological parameter data measured by the monitoring device and process the data to provide a set of interpretations of the data as well as audio data corresponding to a simplified explanation thereof.

Embodiments of the present disclosure provide systems and methods for providing an audio user interface by which a user may be provided audio instructions for measuring physiological parameters using a monitoring device as well as an audio explanation of the results of analysis of the physiological parameters. A cloud service may receive via a smart device a voice command associated with measuring physiological parameters and provide to the smart device, instructions for measuring the physiological parameters. The monitoring device is configured to communicate with the cloud service via the smart device, which may provide the instructions for taking the ECG using the ECG monitoring device to a user as an audio output. The cloud service may receive physiological parameter data measured by the monitoring device and process the data to provide a set of interpretations of the data as well as audio data corresponding to a simplified explanation thereof.