The present invention belongs to the technical field of processing and analysis of biomedical signals, and provides a calibration method for the critical point of mental fatigue based on self-organized criticality. It constructs a self-organized criticality model by using the dynamic characteristics of a brain network, and deduces the avalanche dynamics of mental fatigue, which is consistent with the internal mechanism of evolution of fatigue complexity. The critical state of calibration is dynamically stable and robust. Through the verification of the behavior data, the reliability of the critical state of mental fatigue determined from physiological and behavioral dimensions is high, providing support for the setting of the fatigue category labels to complete more accurate classification and recognition.

A machine learning system for aggregating electroencephalographic (EEG) data in preparation for downstream analysis via further machine learning models. Machine learning models can be used to assist in diagnosis of various mental health conditions, brain-computer interface, mood detection systems, or other biometric functions. Implementations of the present disclosure, employ a portion of the transformer network (the attention encoder stack) to aggregate EEG trials or EEG data segments, in a data-driven way, by ensuring the important content of each trial is not lost. Each EEG trial to be aggregated is converted into an input embedding, or a vector which numerically represents the data in the trial.

Foley type catheter embodiments for sensing physiologic data from a urinary tract of a patient are disclosed. The system includes the catheter and a data processing apparatus and methods for sensing physiologic data from the urinary tract. Embodiments may also include a pressure sensor having a pressure interface at a distal end of the catheter, a pressure transducer at a proximal end, and a fluid column disposed between the pressure interface and transducer. When the distal end is residing in the bladder, the pressure transducer can transduce pressure impinging on it into a chronological pressure profile, which can be processed by the data processing apparatus into one or more distinct physiologic pressure profiles, for example, peritoneal pressure, respiratory rate, and cardiac rate. At a sufficiently high data-sampling rate, these physiologic data may further include relative pulmonary tidal volume, cardiac output, relative cardiac output, and absolute cardiac stroke volume.

An initial set of parameters for operating one or more detection tools is automatically derived and subsequently adjusted so that each detection tool is more or less sensitive to signal characteristics in a region of interest. Detection tool(s) may be applied to physiological signals sensed from a patient (such as EEG signals) and may be configured to run in an implanted medical device that is programmable with the parameters to look for rhythmic activity, spiking, and power changes in the sensed signals, etc. A detection tool may be selected and parameter values derived in a logical sequence and/or in pairs based on a graphical representation of an activity type which may be selected by a user, for example, by clicking and dragging on the graphic via a GUI. Displayed simulations allow a user to assess what will be detected with a derived parameter set and then to adjust the sensitivity of the set or start over as desired.

An electronic device and a smart method for controlling an alarm are provided. The smart method includes, controlling a collection device to collect physiological parameters of a user when an alarm device of the electronic device sounds an alarm at a schedules time, obtaining the physiological parameters collected by the collection device, and determining whether the user is asleep or awake according to the obtained physiological parameters. The alarm device is disabled if the user is awake.

The invention provides a monitoring device (1) for attachment to a stance of a subject. The device comprises a data collector (2) and a processor (3) as two separate parts which can be detachably joined such that physiological signals which are detected by the data collector can be transferred to the processor for signal processing and provision of monitoring data. At least one of the data collector and the processor comprises a transducer which can convert the physiological signal to a data signal which can be processed electronically. The data collector is adapted for adhesive contact with a skin surface, and may comprise an adapter (6) for the detachable attachment of the processor.

The invention concerns a method of analysing the brain activity of a patient performing a given task or in response to an external stimulus, by comparison of standardized data with data in a database, by means of fuzzy logic algorithms.

A transmission device can be carried by the subject. A biological signal recording device can perform wireless communication with the transmission device. A transmitter transmits biological signal data corresponding to a biological signal of a subject. A storage stores the biological signal data. A receiver receives the biological signal data. A recorder records the biological signal data received by the receiver. A detector detects a missing portion in the biological signal data recorded by the recorder or a receipt of the biological signal data by the receiver. A notifier notifies the missing portion or transmits an acknowledgment of the receipt to the transmission device. A complementary transmitter retrieves biological signal data corresponding to the notified missing portion from the storage or identifies unreceived biological signal data and retrieves the identified biological signal data, and transmits the retrieved biological signal data. A complementary recorder records the biological signal data transmitted by the complementary transmitter.

The present invention discloses a depression assessment system based on physiological information, comprising an information acquisition module, a signal processing module, a parameters calculation module, a feature selection module, a machine learning module and an output result module. The present invention further discloses a depression assessment method based on various physiological information, comprising the following steps: 1, processing electrocardiogram (ECG) signal and one or more of photoplethysmography (PPG) signal, electroencephalogram (EEG) signal, galvanic skin response (GSR)signal, electrogastrography (EGG) signal, electromyogram (EMG) signal, electrooculogram (EOG) signal, polysomnogram (PSG) signal and temperature signal, and calculating signal parameters; 2, normalizing the obtained signal parameters, and performing the feature selection on parameters set formed by the normalized signal parameters to obtain feature parameters set; and 3, performing machine learning by utilizing the obtained feature parameters set, and establishing a depression assessment mathematic model to assess the depression level by utilizing a relationship between the feature parameters set and the depression level. The present invention has the advantage that the subjectivity of the assessment by utilizing the depression rating scale can be avoided.

The present disclosure discloses an apparatus for a brain computer interface (BCI) including a feature extraction filter trainer for training a feature extraction filter which minimizes an influence of a background brain wave while maximizing a difference between intended brain waves; and a classifier trainer for training a classifier for classifying the intended brain waves by using a feature vector obtained by filtering the intended brain wave at the feature extraction filter. With the apparatus, only the background brain wave is additionally measured, such that previous intended brain wave data can be reused and the brain wave can be classified more quickly and accurately.