Koninklijke Philips Corporation N.V.

Jesse Salazar

Gary Nelson Garcia Molina

Anandi Mahadevan

Surya Subrahmanya Sreeram Vissapragada Venkata Satya

Antonio Aquino

Birpal Singh Sachdev

William Anthony Truschel

Adrienne Heinrich

Sander Theodoor Pastoor

Tsvetomira Kirova Tsoneva

Michele Bellesi

David White

Ulf Grossekathöfer

Stojan Trajanovski

John Gerthoffer

Bhavdeep Singh Biring

Noah Papas

A system for delivering sensory stimulation comprises a sensor configured to measure brain activity information of a patient during a sleep session; a sensory stimulator configured to deliver sensory simulation to the patient during the sleep session; and a computer system. One or more physical processors are programmed with computer program instructions which, when executed cause the computer system to: determine a first stimulation profile, a second stimulation profile, or a combination stimulation profile thereof based on obtained sleep cycle information and/or obtained cognitive domain information; and provide input to the sensory stimulator based on the determined stimulation profile, the provided input causing the sensory stimulator to deliver the sensory simulations to the patient based on the determined stimulation profile during the detected slow wave sleep in the patient.

Method and system to deliver timed and grouped sensory stimulation

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.

Enhancing deep sleep based on information from frontal brain activity monitoring sensors

The present disclosure pertains to a system and method for automatically detecting rapid eye movement (REM) sleep and delivering sensory stimulation to prolong REM duration, without disturbing sleep. The sensory stimulation may be auditory or other stimulation. The system and method ensure timely delivery of the stimulation and automatically adjust the amount, intensity, and/or timing of stimulation as necessary. REM sleep is detected based on brain activity, cardiac activity and/or other information. REM sleep may be detected and/or predicted by a trained neural network. The amount, timing, and/or intensity of the sensory stimulation may be determined and/or modulated to enhance REM sleep in a subject based on one or more values of one or more intermediate layers of the neural network and one or more brain activity and/or cardiac activity parameters.

System and method for enhancing REM sleep with sensory stimulation

The present disclosure pertains to a system configured to detect slow waves in a subject during a sleep session. The system generates output signals conveying information related to brain activity of the subject. The system is configured to detect individual sleep stages of the subject, the individual sleep stages including a deep sleep stage; and, responsive to detecting the deep sleep stage, generate a harmonic representation of the output signals for a period of time during the sleep session that includes the deep sleep stage; identify two or more points of significance on the harmonic representation of the output signals; and analyze a shape of the harmonic representation of the output signals around the two or more points of significance to determine whether the shape of the harmonic representation of the output signals around the two or more points of significance corresponds to a shape of a slow wave.

System and method for detecting slow waves

The present disclosure pertains to a system configured to a system configured to detect slow waves based on adjusted slow wave detection criteria and time delivery of the sensory stimulation to correspond to slow waves detected based on the adjusted criteria. The system is configured to adjust slow wave detection criteria to enhance detection of slow waves in a subject. Slow wave detection using adjustable slow wave detection criteria produces more stimulation relative to prior art systems because more individual stimuli are provided if more slow waves are detected. In some embodiments, the system includes one or more of a sensory stimulator, a sensor, a processor, electronic storage, a user interface, and/or other components.

System and method for adjusting slow wave detection criteria

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Pittsburgh, PA, United States of America

Jesse Salazar