A physiological acoustic monitoring system receives physiological data from an acoustic sensor, down-samples the data to generate raw audio of breathing sounds and compresses the raw audio. The acoustic monitoring system has an acoustic sensor signal responsive to tracheal sounds in a person. An A/D converter is responsive to the sensor signal so as to generate breathing sound data. A decimation filter and mixer down-samples the breathing sound data to raw audio data. A coder/compressor generates compressed audio data from the raw audio data. A decoder/decompressor decodes and decompresses the compressed audio data into decompressed audio data. The decompressed audio data is utilized to generate respiration-related parameters in real-time. The compressed audio data is stored and retrieved so as to generate respiration-related parameters in non-real-time. The real-time and non-real-time parameters are compared to verify matching results across multiple monitors.

A system and method for facilitating data processing of physiological information is disclosed. The system for facilitating data processing of physiological information includes: a device operable to collect a dataset of the physiological information from a person; and a server operable to receive the dataset from the device, and classify the dataset into at least one preliminary group of medical condition. The server is arranged in data communication to send the preliminary group to a third party device for verification. The server is operable to receive a verified group from the third party device, and modify at least a part of algorithm associated with the classification based on the verified group.

Provided are an electronic device, a signal processing method thereof, a biological signal measurement system, and a non-transitory computer readable recording medium. The electronic device, according to one embodiment of the present disclosure, comprises: a sensor for measuring a biological signal of a user; and a processor for determining the periodicity of the measured biological signal, and selectively compressing the measured biological signal according to the determined periodicity.

A system including a medical device is provided. The medical device includes at least one sensor configured to acquire first data descriptive of a patient, first memory storing a plurality of templates, and at least one processor coupled to the at least one sensor and the first memory. The at least one processor is configured to identify a first template of the plurality of templates that is similar to the first data, to determine first difference data based on the first template and the first data, and to store the first difference data in association with the first template. The system may further include the programmable device.

An encoder for encoding images. The encoder includes processor. The processor is configured to: receive, from display apparatus, information indicative of at least one of: head pose of user, gaze direction of user; identify gaze location in input image, based on the at least one of: head pose, gaze direction; divide input image into first input portion and second input portion, wherein first input portion includes and surrounds gaze location; and encode first input portion and second input portion at first compression ratio and at least one second compression ratio to generate first encoded portion and second encoded portion, respectively, wherein at least one second compression ratio is larger than first compression ratio.

The present disclosure pertains to systems and methods for encoding and/or decoding brain activity signals for data reduction. In a non-limiting embodiment, first user data associated with a first sleep session of a user is received. The first user data is determined to include at least a first instance of a first sleep feature being of a first data size. A first value representing the first instance during a first temporal interval is determined. First encoding data representing the first value is determine, the first encoding data being of a second data size that is less than the first data size. Second user data is generated by encoding the first user data using the first encoding data to represent the first instance in the second user data, and the second user data is stored.

There is disclosed herein examples of systems and methods for compressing a signal. Samples of the signal can be segmented and the samples within each of the segments can be averaged to produce a value that can represent the samples within the segment. The number of samples to average in each segment may be determined based on an error threshold, such that the number of samples being averaged can be maximized to produce less data to be transmitted while maintaining the representation of the samples within the error threshold. In some embodiments, a signal can be separated into a timing reference, a representative periodic function, and a highly compressible error signal. The error signal can be utilized for reproducing a representation of the signal.

Disclosed herein are methods and systems for receiving an encoded data packet, one or more activation commands, and a communication identifier, decoding the received data packet, validating the decoded received data packet, and executing one or more routines associated with the respective one or more activation commands.

Neurological Disease Mechanism Analysis for Diagnosis, Drug Screening, (Deep) Brain Stimulation Therapy design and monitoring, Stem Cell Transplantation therapy design and monitoring, Brain Machine Interface design, control, and monitoring.

A computerized method of analyzing a waveform using a machine learning transformer model includes obtaining labeled waveform training data and unlabeled waveform training data, supplying the unlabeled waveform training data to the transformer model to pre-train the transformer model by masking a portion of an input to the transformer model, and supplying the labeled waveform training data to the transformer model without masking a portion of the input to the transformer model to fine-tune the transformer model. Each waveform in the labeled waveform training data includes at least one label identifying a feature of the waveform. The method also includes supplying a target waveform to the transformer model to classify at least one feature of the target waveform. The at least one classified feature corresponds to the least one label of the labeled waveform training data.