Novel tools and techniques are provided for implementing intelligent assistance (“IA”) or extended intelligence (“EI”) ecosystem to placement procedures for cardiac implantable electronic device (“CIED”). In various embodiments, a computing system might analyze received one or more first layer input data (i.e., room content-based data) and received one or more second layer input data (i.e., patient and/or tool-based data), and might generate one or more recommendations for guiding a medical professional in performing a CIED placement procedure in a heart of the patient, based at least in part on the analysis, the generated one or more recommendations comprising 3D or 4D mapped guides toward, in, and around the heart of the patient. The computing system might then generate one or more XR images, based at least in part on the generated one or more recommendations, and might present the generated one or more XR images using a UX device.

Apparatus for ophthalmic electrophysiological testing, the apparatus comprising: an EER stimulator for providing an electrical stimulus to an eye so as to evoke an electrophysiological response, wherein the EER stimulator comprises a power source; an amplifier for receiving the electrophysiological response and measuring that response, wherein the amplifier is integrated with the EER stimulator; and at least one switch disposed between the power source and the amplifier for isolating the power source from the amplifier when the electrical stimulus is delivered to the eye.

Apparatus for ophthalmic electrophysiological testing, the apparatus comprising: an EER stimulator for providing an electrical stimulus to an eye so as to evoke an electrophysiological response, wherein the EER stimulator comprises a power source; an amplifier for receiving the electrophysiological response and measuring that response, wherein the amplifier is integrated with the EER stimulator; and at least one switch disposed between the power source and the amplifier for isolating the power source from the amplifier when the electrical stimulus is delivered to the eye.

The present invention discloses a method for determining a degree of response to a physical activity. Acquire a physical activity signal measured by a sensing unit in the physical activity. Determine first data of a first physical activity feature set based on the physical activity signal. Determine a recognition of the degree of response to the physical activity based on the first data of the first physical activity feature set by a mathematical model describing a relationship between the first physical activity feature set and the degree of response to a physical activity. A portion of a first mechanism of the mathematical model adopts at least one portion of a second mechanism of a first neural network model associated with the second physical activity feature set.

Electrode array for monitoring of physiological parameters and devices including or utilizing same, the electrode array including an active electrode configured to provide an electrical signal and at least two inactive electrodes configured to collect the electrical signal transferred from the active electrode, wherein each of the at least two inactive electrodes are positioned at a different predetermined distance from the active electrode.

Electrode array for monitoring of physiological parameters and devices including or utilizing same, the electrode array including an active electrode configured to provide an electrical signal and at least two inactive electrodes configured to collect the electrical signal transferred from the active electrode, wherein each of the at least two inactive electrodes are positioned at a different predetermined distance from the active electrode.

An example system disclosed herein includes an analyzer to analyze first neuro-response data and second neuro-response data and a selector to identify a candidate location in source material for introduction of an advertisement or entertainment based on first neuro-response data and second neuro-response data. The analyzer is to detect a first pattern of oscillation in a first frequency band of third neuro-response data; detect a second pattern of oscillation in a second frequency band of the third neuro-response data; determine a degree of phase synchrony or amplitude synchrony based on the first pattern of oscillation and the second pattern of oscillation; and determine an effectiveness of the advertisement or entertainment based on the degree of phase synchrony or amplitude synchrony.

An example system disclosed herein includes an analyzer to analyze first neuro-response data and second neuro-response data and a selector to identify a candidate location in source material for introduction of an advertisement or entertainment based on first neuro-response data and second neuro-response data. The analyzer is to detect a first pattern of oscillation in a first frequency band of third neuro-response data; detect a second pattern of oscillation in a second frequency band of the third neuro-response data; determine a degree of phase synchrony or amplitude synchrony based on the first pattern of oscillation and the second pattern of oscillation; and determine an effectiveness of the advertisement or entertainment based on the degree of phase synchrony or amplitude synchrony.

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.