A bio-signal measuring apparatus includes a sensing apparatus configured to sense electrocardiogram data representing an electrical change according to a pulse of an object and sense heart sound data according to the pulse and a processing apparatus configured to store the electrocardiogram data in a memory. The processing apparatus is further configured to analyze the electrocardiogram data to determine whether or not an abnormal signal is generated in the electrocardiogram data, when the abnormal signal is detected to be generated in the electrocardiogram data, generate a storage control signal for heart sound data associated with the abnormal signal in an abnormal signal section including the abnormal signal, and store the associated heart sound data in the abnormal signal section of the memory in response to the storage control signal.

A method for counting coughs is provided. The method includes acquiring a plurality of onset signals from the sound signal, wherein the onset signal has a predetermined time length; acquiring a plurality of spectrograms corresponding to each of the plurality of onset signals; determining whether each of the acquired plurality of spectrograms represents a cough using a cough determination model; and calculating a number of coughs included in the sound signal based on a time point of a cough signal. The cough signal is an onset signal corresponding to one spectrogram determined to represent the cough. When a time interval between a first time point of a first cough signal and a second time point of a second cough is within a reference time interval, the first cough signal and the second cough signal are regarded as one cough signal at the first time point.

Provided is a biological examination apparatus and a biological information analysis method capable of quickly grasping a two-dimensional motions of the up-down and front-back directions of the thyroid cartilage and the lingual bone accompanied by a swallowing sound as swallowing dynamics by a non-invasive examination. In the biological examination apparatus of the present invention, an up-down motion component associated with an up-down motion of the thyroid cartilage and a front-back motion component associated with a front-back motion of the thyroid cartilage are extracted from a fitting result obtained by fitting a model function modeling a swallowing motion to distance information based on detection data detected by a larynx portion displacement detector, and a two-dimensional trajectory data indicating behavior trajectories of an up-down direction and a front-back direction of the thyroid cartilage is generated based on the extracted up-down motion component and the extracted front-back motion component.

An automatic diagnostic apparatus and corresponding method is disclosed for recognizing heart sounds of interest, i.e., murmurs, detected in streaming audio data picked up by a stethoscope. Sensors included in the device capture audio data in real time during an auscultation exam performed by a physician. A feature vector that models the stream of audio data is created and supplied to a deep neural network stored on the diagnostic device. The deep neural network generates a probability for each of the heart sounds of interest. When the probability of detection exceeds a pre-established threshold value the device alerts the physician through visual and/or audio cues, enhancing the physician's diagnostic capability during routine examination.

A wearable device is provided. The wearable device includes a memory configured to store instructions, at least one display having a display area, a frame configured to support the at least one display, the frame including a nose pad in contact with a part of a user's body wearing the wearable device, a photoplethysmography (PPG) sensor exposed through at least a portion of the frame in contact with other part of the user's body, at least one microphone disposed in the nose pad, and a processor. The processor, when executing the instructions, is configured to identify a breathing state of the user, based at least in part on first data acquired through the PPG sensor and second data acquired through the at least one microphone.

A method of distinguishing sleep period states that a person experiences during a sleep period, the method comprising: using a non-contact microphone to acquire a sleep sound signal representing sounds made by a person during sleep; segmenting the sleep sound signals into epochs; generating a sleep sound feature vector for each epoch; providing a first model that gives a probability that a given sleep period state experienced by the person in a given epoch exhibits a given sleep sound feature vector; providing a second model that gives a probability that a first sleep period state associated with a first epoch transitions to a second sleep period state associated with a subsequent second epoch; and processing the feature vectors using the first and second models to determine a sleep period state of the person from a plurality of possible sleep period states for each of the epochs.

A system comprising a plurality of neuromuscular sensors, each of which is configured to record a time-series of neuromuscular signals from a surface of a user's body; and at least one computer hardware processor programmed to perform: applying a source separation technique to the time series of neuromuscular signals recorded by the plurality of neuromuscular sensors to obtain a plurality of neuromuscular source signals and corresponding mixing information; providing features, obtained from the plurality of neuromuscular source signals and/or the corresponding mixing information, as input to a trained statistical classifier and obtaining corresponding output; and identifying, based on the output of the trained statistical classifier, and for each of one or more of the plurality of neuromuscular source signals, an associated set of one or more biological structures.

Coaptation assist device for repairing cardiac valves and associated systems and methods are disclosed herein. A coaptation assist device configured in accordance with embodiments of the present technology can include, for example, a fixation member configured to press against cardiac tissue proximate to a native valve annulus, and a stationary coaptation structure extending away from the fixation member. The coaptation structure can include an anterior surface configured to coapt with a first native leaflet during systole and a posterior surface configured to displace at least a portion of a second native leaflet. The device also includes at least one sensor configured to detect parameters associated with at least one of cardiac function and device functionality. The sensors can be pressure sensors configured to detect left atrial pressure and/or left ventricular pressure.

A filtering method includes the following steps: receiving an sound signal; decomposing the sound signal into a primary lung sound signal and a reference heart sound signal; adjusting the reference heart sound signal according to a weighted value to generate an adjusted heart sound signal; and subtracting the adjusted heart sound signal from the primary lung sound signal to generate a filtered lung sound signal.

An infant sleep device may include a platform for supporting an infant, a base upon which the platform is supported, and one or more weight sensors positioned to measure weight of an infant positioned on the platform.