A combined physiological sensor and methods for detecting one or more physiological characteristics of a subject are provided. The combined sensor (e.g., a forehead sensor) may be used to detect and/or calculate at least one of a pulse blood oxygen saturation level, a regional blood oxygen saturation level, a respiration rate, blood pressure, an electrical physiological signal (EPS), a pulse transit time (PTT), body temperature associated with the subject, a depth of consciousness (DOC) measurement, any other suitable physiological parameter, and any suitable combination thereof. The combined sensor may include a variety of individual sensors, such as electrodes, optical detectors, optical emitters, temperature sensors, and/or other suitable sensors. The sensors may be advantageously positioned in accordance with a number of different geometries. The combined sensor may also be coupled to a monitoring device, which may receive and/or process one or more output signals from the individual sensors to display information about the medical condition of the subject. In addition, several techniques may be employed to prevent or limit interference between the individual sensors and their associated input and/or output signals.

The present invention is for a method and system for pain classification and monitoring optionally in a subject that is an awake, semi-awake or sedated.

A system and method is operable to automatically determine the amplitude and latency of one or more evoked potential (EP) or event-related potential (ERP) from electroencephalography (EEG) data. The EEG data from an EEG scan is separated into one or more epochs containing the desired EP or ERP waveforms. Epochs corresponding to the same type of EP or ERP such as N100, P300, and N400 are averaged automatically. The averaged epochs are automatically filtered in the time-frequency domain using an automatically selected filtering mask associated with the type of EP or ERP. A corresponding peak is automatically identified from the filtered epoch, in which the amplitude and latency is automatically measured.

A method of determining a cosmetic skin attribute of a person is provided. The method includes obtaining a color channel image of a person's skin, analyzing the color channel image with a computer using entropy statistics to obtain an entropy value, and then determining a cosmetic skin attribute for the person based on the entropy value.

Described is a system for decoding and validating memory consolidation. During operation, the system receives electroencephalographic (EEG) data while a subject is performing a specific task. Nuisance signals are then removed from the EEG data, resulting in a nuisance free signal. Skill feature vectors are generated from the nuisance free signal using time-invariant feature extraction. A skill classifier can then be trained for the specific task based on the skill feature vectors to generate a subject specific model regarding a memory replay for the specific task. Finally, electrodes in a neural cap are activated based on the memory replay.

A method, comprising receiving a time series of patient body signal, determining first and second sliding time windows for the time series; applying an autoregression algorithm, comprising: applying an autoregression analysis to each of the first and second windows, yielding autoregression coefficients and a residual variance for each window; estimating a parameter vector for each window based on the autoregression coefficients and residual variances; and determining a difference between the parameter vectors; and determining seizure onset and seizure termination based on the difference between the parameter vectors. A non-transitory computer readable program storage unit encoded with instructions that, when executed by a computer, perform the method.

The present invention relates to a system and a method of determining disease based on a heat map image explainable from an electrocardiogram signal, which determine an electrocardiogram signal as a normal signal and a disease signal by transfer-learning a transfer-learning model through a deep learning network, calculate and visualize a part with a high relevance score to the determination to enable a user to objectively and finally determine the disease and the normal state. The system for determining disease based on a heat map image explainable from an electrocardiogram signal includes: an electrocardiogram measuring unit configured to acquire an electrocardiogram signal; a scalogram transform unit configured to transform the electrocardiogram signal acquired from the electrocardiogram measuring unit into a time-frequency region and store the transformed electrocardiogram signal as a two-dimensional image; a disease determining unit configured to determine the electrocardiogram signal as normal/disease through the two-dimensional image stored in the scalogram transform unit; a relevance score calculating unit configured to calculate a part contributed to determination of the electrocardiogram signal as normal/disease by the disease determining unit; and a heat map display unit configured to display the part contributed to the determination of the electrocardiogram signal as normal/disease calculated by the relevance score calculating unit as a heat map.

A method for automatically and independently associating a cardiovascular disease with an electrocardiogram, ECG, signal includes receiving the ECG signal; denoising the ECG signal using a wavelet transform; determining peaks of the denoised ECG signal by applying a combination of (1) two event-related moving averages and (2) fractional-Fourier-transform (FrFT) to the denoised ECG signal; and passing the peaks through a classifier for identifying the cardiovascular disease that corresponds to the ECG signal.

A method for generating stimulation parameters, an electrical stimulation control apparatus and an electrical stimulation system are provided. After receiving a brainwave signal, the brainwave signal is decomposed to obtain a first sub-signal and a second sub-signal. Then, the first sub-signal is analyzed to obtain an intrinsic frequency series, and the second sub-signal is converted to a Boolean signal. Subsequently, the intrinsic frequency series and the Boolean signal, which serve as a set of stimulation parameters, are outputted to the stimulator, enabling the stimulator to generate a stimulus signal.

A medical data providing device includes a memory, a communication unit, and a processor. The processor is configured to read a biosignal stored in the memory, divide the biosignal at certain time intervals, convert a signal segment into the frequency, and determines whether the signal segment is an analysis necessary section or an analysis unnecessary section based on a frequency component of the signal segment. The processor is further configured to analyze the signal segment of the analysis necessary section to generate data including an analysis result, and transmits data including the analysis result to a certain device.