A prediabetes detection system and method based on combination of electrocardiogram and electroencephalogram information are provided. The system includes: a signal obtaining module, configured to obtain an electrocardiogram signal and an electroencephalogram signal of a user in a noninvasive manner; a feature extraction module, configured to: perform dimension reduction processing on a combined feature set composed of an electrocardiogram feature and an electroencephalogram feature to obtain a plurality of dimension-reduced combined feature sets, and select an electrocardiogram feature and an electroencephalogram feature meeting a preset criteria of correlation by analyzing a correlation between the plurality of dimension-reduced combined feature sets and a blood glucose concentration value to constitute an optimized combined feature set; and a multimodal fusion module, configured to input the optimized combined feature set into a plurality of trained neural network models, to obtain a detection result by fusing results of the plurality of neural networks.

A prediabetes detection system and method based on combination of electrocardiogram and electroencephalogram information are provided. The system includes: a signal obtaining module, configured to obtain an electrocardiogram signal and an electroencephalogram signal of a user in a noninvasive manner; a feature extraction module, configured to: perform dimension reduction processing on a combined feature set composed of an electrocardiogram feature and an electroencephalogram feature to obtain a plurality of dimension-reduced combined feature sets, and select an electrocardiogram feature and an electroencephalogram feature meeting a preset criteria of correlation by analyzing a correlation between the plurality of dimension-reduced combined feature sets and a blood glucose concentration value to constitute an optimized combined feature set; and a multimodal fusion module, configured to input the optimized combined feature set into a plurality of trained neural network models, to obtain a detection result by fusing results of the plurality of neural networks.

A system includes an interface and a processor. The interface is configured to receive an electrogram acquired in a heart of a patient. The processor is configured to (i) estimate a level of injury current present in the electrogram, and (ii) based on the estimated level of injury current, decide whether to use the electrogram in a subsequent analysis.

A system includes an interface and a processor. The interface is configured to receive an electrogram acquired in a heart of a patient. The processor is configured to (i) estimate a level of injury current present in the electrogram, and (ii) based on the estimated level of injury current, decide whether to use the electrogram in a subsequent analysis.

A portable electrocardiogram device comprises a sensor array and a user associated control device. The sensor array comprises a first processor, a memory storage and a first set of communication means. The user associated control device comprises indicator means, a second processor and a second set of communication means. The sensor array is configured to be carried by a wearing user, comprises a first set of sensors configured to face the skin of the wearing user, and the first set of sensors is attached to at least one undergarment. The first set of communication means comprises at least one wireless communication device. The first processor is arranged to repeatedly, with a predetermined measurement frequency, control at least one sensor of the sensor array to record an ECG when carried by the wearing user, store the ECG recording in the memory storage, and to control the at least one wireless communication device to transmit at least one ECG recording to the user associated control device. The user associated control device is configured to detect abnormal ECG in the at least one ECG recording. The user associated control device is configured to present an alarm by said indicator means in response to detecting at least one abnormal ECG. The measurement frequency of the sensor array is set based on any detected abnormal ECG.

A portable electrocardiogram device comprises a sensor array and a user associated control device. The sensor array comprises a first processor, a memory storage and a first set of communication means. The user associated control device comprises indicator means, a second processor and a second set of communication means. The sensor array is configured to be carried by a wearing user, comprises a first set of sensors configured to face the skin of the wearing user, and the first set of sensors is attached to at least one undergarment. The first set of communication means comprises at least one wireless communication device. The first processor is arranged to repeatedly, with a predetermined measurement frequency, control at least one sensor of the sensor array to record an ECG when carried by the wearing user, store the ECG recording in the memory storage, and to control the at least one wireless communication device to transmit at least one ECG recording to the user associated control device. The user associated control device is configured to detect abnormal ECG in the at least one ECG recording. The user associated control device is configured to present an alarm by said indicator means in response to detecting at least one abnormal ECG. The measurement frequency of the sensor array is set based on any detected abnormal ECG.

Broadly speaking, embodiments of the present techniques provide a skin-conformable and compact wearable electronic apparatus for monitoring physiological and/or brain signals of the wearer.

The present disclosure provides a wearable device. The wearable device includes a first element and a second element. The first element is configured to sense a bio-signal from a user. The second element is configured to transmit the bio-signal to a processor. The second element has a first surface and a second surface non-coplanar with the first surface. The first element is in contact with the first surface and the second surface of the second element.

In a method of neurological assessment, multichannel electromyography (EMG) data are acquired for an anatomical region. A pairwise EMG channel-EMG channel similarity matrix is generated from the acquired multichannel EMG data. Network analysis is performed on the similarity matrix to generate a network representing the similarity matrix. One or more metrics of the network are computed. One or more biomarkers are determined for the anatomical region based on the one or more metrics. In another method, EMG data are acquired using an electrode array contacting skin of a target anatomy, the EMG data are processed to produce reduced-dimensionality data; and time-invariant muscle synergies and corresponding time-varying activation functions are determined in the reduced-dimensionality data.

In a method of neurological assessment, multichannel electromyography (EMG) data are acquired for an anatomical region. A pairwise EMG channel-EMG channel similarity matrix is generated from the acquired multichannel EMG data. Network analysis is performed on the similarity matrix to generate a network representing the similarity matrix. One or more metrics of the network are computed. One or more biomarkers are determined for the anatomical region based on the one or more metrics. In another method, EMG data are acquired using an electrode array contacting skin of a target anatomy, the EMG data are processed to produce reduced-dimensionality data; and time-invariant muscle synergies and corresponding time-varying activation functions are determined in the reduced-dimensionality data.