The method and system for monitoring cough comprises receiving audio signals or audio recordings, where said signals or audio recordings comprises one or more of silent segments, cough sound segments, speech segments and extraneous noise. The processing of said received sound signals or sound recordings comprise one or more of removing one or more speech components from speech segments to render the speech unintelligible and clipping said silent segments, wherein one or more speech components include vowel sounds. Further processing of said received audio signals or audio recordings further comprises compressing said audio signals or audio recordings. In the alternative, processing of audio signals or audio recordings comprises compressing a resultant signal after said removal of one or more speech components and/or clipping of silent segments from said audio signals.

The invention relates to the system and method of remote ECG monitoring, remote disease screening, and early-warning system based on wavelet analysis. The system includes a wireless ECG signal acquisition device, a mobile terminal, and a cloud storage platform. The wireless ECG signal acquisition device worn on the user's chest is used to collect ECG signals anywhere and anytime. The method includes transmitting the ECG signals to the mobile terminal using the wavelet analysis algorithm, analyzing and processing the received ECG signal, and uploading the processed ECG signals to the cloud storage platform. The cloud storage platform stores users' personal information and ECG signals. According to the ECG features detection with support vector machine learning algorithm for heart diseases diagnosis and features classification, the system gives feedback report and proposal, and transmits them to the mobile terminal.

Disclosed is an image compression method of a digital pathology system. The image compression method is a method of compressing digital slide images having first to nth original plane images (n is a natural number greater than or equal to 2). The image compression method includes selecting a block having an optimal focal point as an optimal block from each set of blocks positioned at identical positions of the first to nth original plane images; forming one plane image as a virtual optimal plane image by combining only the optimal blocks; generating block descriptors for forming the first to nth original plane images based on the virtual optimal plane image; generating first to nth predictive plane images from the virtual optimal plane image such that the first to nth predictive plane images are the least out of focus by using location information for the blocks and the block descriptors; generating first to nth differential plane images, the first differential plane image corresponding to a difference between the first original plane image and the first predictive plane image and the nth differential plane image corresponding to a difference between the nth original plane image and the nth predictive plane image; and compressing the first to nth differential plane images.

One variation of a method for hosting mobile access to dense electroencephalography data includes: receiving a set of signals, in a raw resolution, recorded by a set of channels in an electroencephalography headset during an electroencephalography test; receiving, from a client computing device, a view parameters for viewing the set of signals on a display; calculating a quantity of raw signal points per pixel column of the display based on the view parameters and a length of a segment of the electroencephalography test; for each signal in the set of signals, for each discrete contiguous sequence of the quantity of raw signal points within the segment of the signal, calculating a value set characterizing the discrete contiguous sequence of the quantity of raw signal points in the signal; and generating a static image representing value sets for each channel, in the set of channels, across the segment of the electroencephalography test.

A method for acquiring image data obtains, for an intraoral feature, optical coherence tomography (OCT) data in three dimensions wherein at least one dimension is pseudo-randomly or randomly sampled and reconstructs an image volume of the intraoral feature using compressive sensing, wherein data density of the reconstructed image volume is larger than that of the obtained OCT data in the at least one dimension or according to a corresponding transform. The method renders the reconstructed image volume for display.

A medical data processing apparatus according to one embodiment includes processing circuitry. The processing circuitry obtains a compressed channel of data generated by compressing a plurality of first medical channels of data defined by first domain representation and respectively corresponding to a plurality of components, via an intermediate channel of data defined by second domain representation. The processing circuitry decodes the compressed channel of data to a second medical channel of data defined by the first domain representation based on a conversion process from the plurality of first medical channels of data to the compressed dataset.

A detection appliance and a method detect and evaluate a measuring signal that is indicative of breathing of a sleeping person, in connection with the observation of sleep-related breathing disorders. Instruments also detect signals that are indicative of breathing of a patient. The aim provides solutions that enable a reliable examination in terms of occurrence of sleep-related sleeping disorders, in the usual surroundings of the person concerned. In a first form, a mobile detection appliance is provided with a sensor device for detecting a nasal flow signal indicative of a nasal respiratory gas flow, and/or a respiratory flow signal indicative of an oral respiratory gas flow, in addition to an electronic data processing unit comprising a memory device and processing the signals indicative of temporal course of the nasal and oral respiration. The data processing device is configured to store data indicative of temporal course of the respiratory flow signals.

Provided is a method for long-distance transmission of physiological signals in a closed loop system, including generating a signal at a user terminal of the closed loop system, compressing the signal to generate a compressed signal, transmitting the compressed signal from the user terminal to a computing terminal of the closed loop system, receiving and comparing the compressed signal with a database at the computing terminal to generate a comparison result and a feedback signal, and transmitting the feedback signal from the computing terminal to the user terminal. A time interval between generating the signal and receiving the feedback signal at the user terminal is less than a threshold.

Physiological monitoring can be provided through a lightweight wearable monitor that includes two components, a flexible extended wear electrode patch and a reusable monitor recorder that removably snaps into a receptacle on the electrode patch. The wearable monitor sits centrally (in the midline) on the patient's chest along the sternum oriented top-to-bottom. The placement of the wearable monitor in a location at the sternal midline, with its unique narrow “hourglass”-like shape, significantly improves the ability of the wearable monitor to cutaneously sense cardiac electrical potential signals, particularly the P-wave and, to a lesser extent, the QRS interval signals indicating ventricular activity in the ECG waveforms. Additionally, the monitor recorder includes an ECG sensing circuit that measures raw cutaneous electrical signals and performs signal processing prior to outputting the processed signals for sampling and storage.

A method for transmitting compressed brainwave physiological signals is provided and including detecting a plurality of brainwave physiological signals of a subject, and generating an electroencephalography based on a time sequence of the plurality of brainwave physiological signals; splitting the electroencephalography into a plurality of sub-images based on the time sequence; using a plurality of static feature tags and a plurality of dynamic displacement tags stored in a brainwave database to identify at least one static feature tag and a plurality of associated dynamic displacement tags based on the time sequence according to the plurality of sub-images; generating at least one superimposed group tag, the superposed group tag is used to integrate the identified static feature tag and the associated dynamic displacement tag according to the time sequence; and transmitting the identified static feature tag, the associated dynamic displacement tag, and the superimposed group tag to a remote cloud system according to the time sequence.