Cambridge Heartwear Limited

Cambridge Heatwear Limited

Rameen Shakur

Roberto Cipolla

Andrew McCulloch

James Charles

Levin Chun Kiat Tan

Wearable ECG monitor

We disclose a portable wearable device for measuring electrocardiographic signals, the device comprising a first portion comprising at least two electrodes; a second portion comprising at least three electrodes; the first portion and the second portion being vertically spaced from one another; a controller configured to take: a first potential difference measurement using one of the electrodes of the first portion and one of the electrodes of the second portion; a second potential difference measurement using one other of the electrodes of the first portion and one other of the electrodes of the second portion; and a third potential difference measurement using one other of the electrodes of the first portion and one other of the electrodes of the second portion. The controller is configured to simultaneously take the first, second and third potential difference measurements at three independent positions of a user's body.

ECG device

A method of detecting abnormalities in ECG signals by providing an ECG signal to a neural network, performing a first series of convolution operations to a first subset of layers and in a final layer, and determining a plurality of preliminary classification estimates, each preliminary classification estimate corresponding with a time segment of the ECG signal. Furthermore, determining input data for a second subset of layers of the neural network by concatenating the preliminary classification with the output of a layer of the first subset of layers that precedes the final layer of the first subset of layers. Within the second subset of layers of the neural network, performing a second series of convolution operations. In a final layer of the second subset, determining plurality of final classification estimates, each final classification estimate corresponding with a time segment of the ECG signal.

Detecting abnormalities in ECG signals

A method of detecting abnormalities in an ECG signals, comprising receiving an ECG signal, extracting at least one ECG beat the ECG signal; providing the at least one ECG beat to a neural network. Within the neural network, performing at least two layers of convolution operations, and for a beat of the ECG signal, determining a confidence map for the location of each of the P, Q, R, S and T points, determining, from the respective confidence maps the position or absence of points, the points comprising each of the P, Q, R, S and T points; and reporting the positions and/or absence of the points.

We disclose herein a method of detecting abnormalities in electrocardiogram (ECG) signals, the method comprising receiving a set of ECG signals from an ECG device; amplifying only the peaks of at least some of the set of ECG signals to produce ECG beat markings from which a heart rate is derivable to detect an irregular rhythm between at least two ECG beats; extracting a single ECG beat from the set of ECG signals from the ECG device by using said ECG beat markings; feeding the extracted single ECG beat into a first neural network; producing, at the first neural network, a compact representation of the extracted single ECG signal so as to generate a feature extraction output; and using, at a second neural network, the feature extraction output from the first neural network to generate a score associated with the abnormalities in the ECG signals.

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Cambridge, United Kingdom

Rameen Shakur