An improved system for assessing cognitive function is described that uses tracked electrical activity of the brain of the individuals in response to a specific sequence of stimuli in generating data sets, which, for example, can be encapsulated as a data structure. The data sets can include tracked specific response types, at different times and amplitudes, including, but not limited to, event related potential signal components. Brainwave features including, event related potentials, are tracked in relation to both pre-attentive brain responses and consciously controlled attention responses.

An improved system for assessing cognitive function is described that uses tracked electrical activity of the brain of the individuals in response to a specific sequence of stimuli in generating data sets, which, for example, can be encapsulated as a data structure. The data sets can include tracked specific response types, at different times and amplitudes, including, but not limited to, event related potential signal components. Brainwave features including, event related potentials, are tracked in relation to both pre-attentive brain responses and consciously controlled attention responses.

A medical device is configured to detect an atrial tachyarrhythmia episode. The device senses a cardiac signal, identifies R-waves in the cardiac signal attendant ventricular depolarizations and determines classification factors from the R-waves identified over a predetermined time period. The device classifies the predetermined time period as one of unclassified, atrial tachyarrhythmia and non-atrial tachyarrhythmia by comparing the determined classification factors to classification criteria. A classification criterion is adjusted from a first classification criterion to a second classification criterion after at least one time period being classified as atrial tachyarrhythmia. An atrial tachyarrhythmia episode is detected by the device in response to at least one subsequent time period being classified as atrial tachyarrhythmia based on the adjusted classification criterion.

A medical device is configured to detect an atrial tachyarrhythmia episode. The device senses a cardiac signal, identifies R-waves in the cardiac signal attendant ventricular depolarizations and determines classification factors from the R-waves identified over a predetermined time period. The device classifies the predetermined time period as one of unclassified, atrial tachyarrhythmia and non-atrial tachyarrhythmia by comparing the determined classification factors to classification criteria. A classification criterion is adjusted from a first classification criterion to a second classification criterion after at least one time period being classified as atrial tachyarrhythmia. An atrial tachyarrhythmia episode is detected by the device in response to at least one subsequent time period being classified as atrial tachyarrhythmia based on the adjusted classification criterion.

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user; comparing one or more portions of the single-lead heartbeat waveform to one or more ML-generated waveform features to associate a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to a recipient.

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user; comparing one or more portions of the single-lead heartbeat waveform to one or more ML-generated waveform features to associate a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to a recipient.

An electrocardiogram analysis apparatus includes: an electrocardiogram signal inputting section to which electrocardiogram signals of measurement electrodes attached to a subject are input; a mistaken attachment determining section which, by using the input electrocardiogram signals, determines whether the measurement electrodes are mistakenly attached or not; an outputting section which, if it is determined that the measurement electrodes are mistakenly attached, notifies of mistaken attachment of the measurement electrodes; and an electrocardiogram data storing section which, in a case where there is an input indicative of confirmation of the notification, stores information indicating that the measurement electrodes have been checked, together with the input electrocardiogram signals, and, in a case where there is not an input indicative of confirmation of the notification, stores information indicating that the measurement electrodes have not been checked, together with the input electrocardiogram signals.

An electrocardiogram analysis apparatus includes: an electrocardiogram signal inputting section to which electrocardiogram signals of measurement electrodes attached to a subject are input; a mistaken attachment determining section which, by using the input electrocardiogram signals, determines whether the measurement electrodes are mistakenly attached or not; an outputting section which, if it is determined that the measurement electrodes are mistakenly attached, notifies of mistaken attachment of the measurement electrodes; and an electrocardiogram data storing section which, in a case where there is an input indicative of confirmation of the notification, stores information indicating that the measurement electrodes have been checked, together with the input electrocardiogram signals, and, in a case where there is not an input indicative of confirmation of the notification, stores information indicating that the measurement electrodes have not been checked, together with the input electrocardiogram signals.

A method for machine learning based artifact rejection is provided. The method may include applying a machine learning model to identify artefactual independent components in transcranial magnetic stimulation electroencephalogram data collected during a transcranial magnetic stimulation procedure. Clean transcranial magnetic stimulation electroencephalogram data is generated by removing, from the transcranial magnetic stimulation electroencephalogram data, the artefactual independent components. Real-time adjustments to parameters of the transcranial magnetic stimulation procedure may be performed based on the clean transcranial magnetic stimulation electroencephalogram data. Related systems and articles of manufacture, including computer program products, are also provided.

A method for machine learning based artifact rejection is provided. The method may include applying a machine learning model to identify artefactual independent components in transcranial magnetic stimulation electroencephalogram data collected during a transcranial magnetic stimulation procedure. Clean transcranial magnetic stimulation electroencephalogram data is generated by removing, from the transcranial magnetic stimulation electroencephalogram data, the artefactual independent components. Real-time adjustments to parameters of the transcranial magnetic stimulation procedure may be performed based on the clean transcranial magnetic stimulation electroencephalogram data. Related systems and articles of manufacture, including computer program products, are also provided.