The systems and methods can accurately and efficiently determine abnormal cardiac activity from motion data and/or cardiac data using techniques that can be used for long-term monitoring of a patient. In some embodiments, the method for using machine learning to determine abnormal cardiac activity may include receiving one or more periods of time of cardiac data and motion data for a subject. The method may include applying a trained deep learning architecture to each tensor of the one or more periods of time to classify each window and/or each period into one or more classes using at least the one or more signal quality indices for the cardiac data and the motion data and cardiovascular features. The deep learning architecture may include a convolutional neural network, a bidirectional recurrent neural network, and an attention network. The one or more classes may include abnormal cardiac activity and normal cardiac activity.

A method of facilitating a skill learning process or improving performance of a task, comprising: determining a brainwave pattern reflecting neuronal activity of a skilled subject while engaged in a respective skill or task; processing the determined brainwave pattern with at least one automated processor; and subjecting a subject training in the respective skill or task to brain entrainment by a stimulus selected from the group consisting of one or more of a sensory excitation, a peripheral excitation, a transcranial excitation, and a deep brain stimulation, dependent on the processed temporal pattern extracted from brainwaves reflecting neuronal activity of the skilled subject.

A medical image analyzing system and a medical image analyzing method are provided and include inputting at least one patient image into a first model of a first neural network module to obtain a result having determined positions and ranges of an organ and a tumor of the patient image; inputting the result into a plurality of second models of a second neural network module, respectively, to obtain a plurality of prediction values corresponding to each of the plurality of second models and a model number predicting having cancer in the plurality of prediction values; and outputting a determined result based on the model number predicting having cancer and a number threshold value. Further, processes between the first model and the second models can be automated, thereby improving identification rate of pancreatic cancer.

Described here are systems and methods for generating cardiac vibrational energy spectral (“VIBES”) heat maps from physical vibration data and cardiac cycle timing data measured from a subject. Quick screening for heart valve, cardiovascular, and/or cardiothoracic abnormalities can be provided based on an analysis and/or classification of the generated VIBES heat maps.

The present disclosure relates to devices, systems, methods and computer program products for continuously monitoring, diagnosing and providing treatment assistance to patients using sensor devices, location-sensitive and power-sensitive communication systems, analytical engines, and remote systems. The method of non-invasively measuring multiple physiological parameters in a patient includes collecting photoplethysmograph (PPG) signal data from a wearable sensor device, applying one or more filters to correct the signal data and extracting a plurality of features from the corrected data to determine values for blood glucose, blood pressure, SpO2, respiration rate, and pulse rate of the patient. An alert may be automatically sent to one or more computing devices when the value falls outside a custom computed threshold range for the patient. The method offers ease of usage, allows continuous real-time monitoring of the patient in any setting for timely intervention, and results in improved accuracy of the signal data.

An Electrocardiography (ECG) noise-filtering device is provided in the invention. The ECG device includes a filter and a calculation circuit. The filter receives a first ECG signal and performs a Savitzky-Golay algorithm to generate a second ECG signal. The calculation circuit is coupled to the filter to receive the second ECG signal and processes the second ECG signal according to a Stationary Wavelet Transform (SWT) algorithm to generate a noise signal, and subtracts the noise signal from the second ECG signal to filter the noise signal in the first ECG signal.

A tremor suppression device includes a garment wearable on an anatomical region and including electrodes contacting the anatomical region when the garment is worn on the anatomical region, and an electronic controller configured to: detect electromyography (EMG) signals as a function of anatomical location and time using the electrodes; identify tremors as a function of anatomical location and time based on the EMG signals; and apply neuromuscular electrical stimulation (NMES) at one or more anatomical locations as a function of time using the electrodes to suppress the identified tremors.

A computer-implemented method for detecting onset of a spontaneous breath by a patient coupled to a ventilation system includes receiving, at a processor, an electromyography (EMG) signal from an EMG sensor disposed on the patient. The method also includes pre-conditioning, via the processor, the EMG signal to separate the EMG signal into a plurality of components having EMG information utilizing a set of bandpass filters. The method further includes individually analyzing, via the processor, each component of the plurality of components to detect an onset of the spontaneous breath by the patient. The method still further includes determining, via the processor, the onset of the spontaneous breath by the patient is occurring when at least two components of the plurality of components indicate the onset of the spontaneous breath by the patient.

A noninvasive glucometer and a blood glucose detection method are provided. The noninvasive glucometer includes a light source, a spectrometer and detecting space into which an object to be detected intervenes; the detecting space is connected with the light source and the spectrometer respectively, so that a spectrum emitted by the light source can generate incident light entering the spectrometer after passing through the object to be detected. The spectrometer includes: an optical modulation layer configured to perform light modulation on the incident light to obtain a modulated spectrum; a photoelectric detection layer located below the optical modulation layer, and configured to receive the modulated spectrum and provide differential responses with respect to the modulated spectrum; and a signal processing circuit layer located below the photoelectric detection layer and configured to reconstruct the differential responses to obtain an original spectrum.

In some examples, a system includes processing circuitry configured to generate a laser speckle contrast signal based on a received signal indicative of detected light, wherein the detected light is scatted by tissue from a coherent light source. The processing circuitry may also determine, from the laser speckle contrast signal, a flow value and determine, from the laser speckle contrast signal, a waveform metric. Based on the flow value and the waveform metric, the processing circuitry may determine a blood flow metric for the tissue and output a representation of the blood flow metric.