The invention relates to an active implantable medical device comprising a processing unit able to be alternately operated during a predetermined period of activity and on standby during a standby period in a cyclical manner, and means for acquiring data relating to physiological and/or physical activity. The device also comprises means for calculating a frequency analysis of the data acquired, said calculating means being capable of successively perform part of the frequency analysis during periods of activity of the processing unit.

The invention relates to an active implantable medical device comprising a processing unit able to be alternately operated during a predetermined period of activity and on standby during a standby period in a cyclical manner, and means for acquiring data relating to physiological and/or physical activity. The device also comprises means for calculating a frequency analysis of the data acquired, said calculating means being capable of successively perform part of the frequency analysis during periods of activity of the processing unit.

Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

Typically, high NREM stage N3 sleep detection accuracy is achieved using a frontal electrode referenced to an electrode at a distant location on the head (e.g., the mastoid, or the earlobe). For comfort and design considerations it is more convenient to have active and reference electrodes closely positioned on the frontal region of the head. This configuration, however, significantly attenuates the signal, which degrades sleep stage detection (e.g., N3) performance. The present disclosure describes a deep neural network (DNN) based solution developed to detect sleep using frontal electrodes only. N3 detection is enhanced through post-processing of the soft DNN outputs. Detection of slow-waves and sleep micro-arousals is accomplished using frequency domain thresholds. Volume modulation uses a high-frequency/low-frequency spectral ratio extracted from the frontal signal.

A rehabilitation evaluation apparatus includes a sensor signal acquisition unit configured to acquire a sensor signal output from a detection sensor, a selection unit configured to select at least one myoelectric signal having a correlation with the sensor signal acquired by the sensor signal acquisition unit from among the plurality of second myoelectric signals on the second-side part acquired by the myoelectric-signal acquisition unit, and a similarity output unit configured to select a first myoelectric signal that has been output from a myoelectric sensor attached in a place that is left-right symmetric to a place of the myoelectric sensor that has output the second correlated myoelectric signal selected by the selection unit from among a plurality of first myoelectric signals on the first-side part acquired by the myoelectric-signal acquisition unit, calculate a similarity between these correlated myoelectric signals, and outputs the calculated similarity.

A rehabilitation evaluation apparatus includes a sensor signal acquisition unit configured to acquire a sensor signal output from a detection sensor, a selection unit configured to select at least one myoelectric signal having a correlation with the sensor signal acquired by the sensor signal acquisition unit from among the plurality of second myoelectric signals on the second-side part acquired by the myoelectric-signal acquisition unit, and a similarity output unit configured to select a first myoelectric signal that has been output from a myoelectric sensor attached in a place that is left-right symmetric to a place of the myoelectric sensor that has output the second correlated myoelectric signal selected by the selection unit from among a plurality of first myoelectric signals on the first-side part acquired by the myoelectric-signal acquisition unit, calculate a similarity between these correlated myoelectric signals, and outputs the calculated similarity.

The invention provides systems and methods for monitoring the wellbeing of a fetus by the non-invasive detection and analysis of fetal cardiac electrical activity data.

The invention provides systems and methods for monitoring the wellbeing of a fetus by the non-invasive detection and analysis of fetal cardiac electrical activity data.

A method of obtaining and analyzing ECG data from a patient or group of patients is disclosed. The ECG data is obtained from the patient at an acquisition device. Once the ECG data is obtained, the ECG data is transmitted to an analysis server that is operated by an analysis provider and is located remote from the location of the acquisition device. Along with the ECG data, acquisition parameters are transmitted to the analysis server. At the analysis server, one of a plurality of algorithms is selected to analyze the ECG data. If an abnormality is detected, the patient information is directed to a healthcare provider who can then contact the patient to schedule an appointment. Based upon the referral, a referral fee can be transferred from the healthcare provider to the analysis provider. The patient can be prompted to provide additional information and selections that dictate the level of analysis generated.

A computer system for use in monitoring blood glucose level monitoring, the computer system configured, in response to receiving electrocardiogram data measured over a given period of time for a given subject, to classify the electrocardiogram data using at least one neural network and a personalised model which is specific to the given subject so as to identify whether a low blood glucose level condition is present wherein blood glucose level falls below a predefined level and, upon identifying the presence of the low blood glucose level condition, to flag an alarm condition.