A pelvic floor contraction detection system and method to evaluate pelvic floor muscle (PFM) exercise performed by a user are disclosed. The contraction detection system comprises a probe adapted to position within a pelvic cavity of the user, a data processing module, and a user interface. The probe comprises two or more sections. Each section is filled with a group of electronic sensors. The first and second sections are seated in contact with tissues of the pelvic cavity. The first and second groups of sensors are configured to detect pressure applied by the internal vaginal surface on the surface of the first and second sections of the probe respectively. The data processing module is in communication with the first and second groups of sensors and configured to calculate a number that is interpreted as the “quality of the contraction”. The data processing module determines the incorrect pelvic floor muscle contraction and notifies the user via a user interface.

An electronic system for non-invasive monitoring of estrogen of a female human comprises a wearable device (1) and a processor (13, 30, 40). The wearable device (1) includes a first sensor system (101) configured to be worn in contact with the skin of the female human and to determine a level of perfusion of the female human. The processor (13, 30, 40) is configured to receive and store the level of perfusion of the female human from the first sensor system (101) for a respective point in time. The processor (13, 30, 40) is further configured to determine a change in the level of perfusion of the female human, using the levels of perfusion of the female human stored for a plurality of respective points in time. Furthermore, the processor (13, 30, 40) is configured to detect a change in estrogen level of the female human based on the change in the level of perfusion of the female human.

A device for liveness detection is disclosed. The liveness detecting device has a simplest structure that principally comprises a light sensing unit and a signal processing module. Particularly, the signal processing module is configured for having a physiological feature extracting unit and a liveness detecting unit therein. The physiological feature extracting unit is adopted for extracting a first physiological feature from a PPG signal, or extracting a second physiological feature from the PPG signal that has been applied with a signal process. As such, through the first and second physiological features, the liveness detecting unit is able to determine whether a subject is a living body or not. The liveness detecting device does not use any camera unit and iPPG technology, such that the liveness detecting device has advantages of simple structure, low cost and immediately completing liveness detection.

A method of deriving depth EEG data from non-invasive 2D EEG data is described. The method receives several EEG scalp signals, each of which is produced by a contact of an EEG recording device. The method converts each EEG scalp signal into multiple frequency band signals. The method identifies a set of contacts that have similar signal fragments in frequency band signals for a particular frequency band. The method determines relative time delay in frequency band signal arrival at the set of contacts. The method determines relative radius of sphere for the set of contacts based on the relative time delay in frequency band signal arrival at the set of contacts. The method then determines a signal source location by performing trilateration on the set of contacts using locations of the set of contacts and the relative radius of sphere for the set of contacts.

An apparatus for estimating bio-information is provided. According to an embodiment of the present disclosure, the apparatus for estimating bio-information includes: a pulse wave sensor having a plurality of channels to measure a plurality of pulse wave signals from an object; a force sensor configured to obtain a force signal by measuring an external force exerted onto the pulse wave sensor; and a processor configured to: obtain a first feature for each channel by inputting the plurality of pulse wave signals for each channel and the force signal, into a first neural network model; obtain a weight for each channel by inputting the first feature to a second neural network model; obtain a second feature by applying the weight to the first feature for each channel by using the second neural network model; and obtain bio-information by inputting the second feature to a third neural network model.

A system (1) for estimating the brain blood volume and/or brain blood flow and/or depth of anesthesia of a patient, comprises at least one excitation electrode (110E) to be placed on the head (20) of a patient (2) for applying an excitation signal, at least one sensing electrode (110S) to be placed on the head (20) of the patient (2) for sensing a measurement signal caused by the excitation signal, and a processor device (12) for processing said measurement signal (VC) sensed by the at least one sensing electrode (110S) for determining an output indicative of the brain blood volume and/or the brain blood flow. Herein, the processor device (12) is constituted to reduce noise in the measurement signal (VC) by applying a non-linear noise-reduction algorithm. In this way a system for estimating the brain blood volume and/or the brain blood flow of a patient is provided which may lead to an increased accuracy and hence more exact estimates.

A sleep-wakefulness determination device is provided which can determine sleep and wakefulness of a user. The sleep-wakefulness determination device is provided with a scalar calculation unit, a feature amount calculation unit, and a sleep-wakefulness determination unit. The scalar calculation unit is configured to calculate a scalar value on the basis of each component of an acceleration vector in a part of the body of the user. The feature amount calculation unit is configured to calculate, on the basis of the scalar value, a feature amount for each epoch defined as a prescribed time. The sleep-wakefulness determination unit is configured to determine sleep or wakefulness of the user on the basis of the feature amount of a desired epoch among the epochs and the feature amounts of surrounding epochs included in preceding and subsequent epochs of the desired epoch in a time series.

The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more PPG waveform-based features or parameters determined from biophysical signals such as photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. PPG waveform-based features or parameters may include PPG waveform features or parameters, VPG waveform features or parameters, and/or APG waveform features or parameters. The PPG waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.

Some embodiments of a system or method for treating heart tissue can include a control system and catheter device operated in a manner to intermittently occlude a heart vessel for controlled periods of time that provide redistribution of blood flow. In particular embodiments, the system and methods may be configured to monitor at least one input signal detected at a coronary sinus and thereby execute a process for determining a satisfactory time period for the occlusion of the coronary sinus. In further embodiments, after the occlusion of the coronary sinus is released, the control system can be configured to select the duration of the release phase before the starting the next occlusion cycle.

An apparatus for estimating bio-information is disclosed. The bio-information estimating apparatus includes: a sensor configured to measure a bio-signal; and a processor configured to obtain one or more characteristic points, related to one or more pulse waveform components constituting the bio-signal, based on a differential signal of the bio-signal, and to estimate bio-information based on the obtained one or more characteristic points.