A data processing method for detecting and classifying a segment of a signal that is obtained from a single-channel EEG-recording as a target signal segment or as a non-target signal segment. The method includes a voting process to determine whether classification of a first detected segment of the signal as a target signal segment or classification of a second detected segment of the signal as a non-target signal segment is correct. A device and a system that are configured and arranged to perform the data processing method.

A noninvasive method and system for sleep apnea detection is disclosed. The method includes the following steps: acquiring vital sign signals of a sleeping user; performing structured processing on the vital sign signals of the user to remove invalid signals to obtain a set of valid vital sign signals; extracting multi-dimensional morphological features from a sleep respiratory signal and performing feature training on an initial model of a classifier by means of the multi-dimensional morphological features so as to obtain a sleep breathing detection model; and inputting the set of valid vital sign signals into the sleep breathing detection model and performing signal processing to obtain predicted probability of the user suffering from sleep apnea. As a result, data relating to the probability of a user suffering from sleep apnea can be more accurately obtained, thereby facilitating the determination of whether a sleep apnea event occurs during sleep.

A method includes receiving first physiological data associated with a user during a first sleep session. The method also includes receiving second physiological data associated with the user subsequent to the first sleep session and prior to a second sleep session. The method also includes determining a recommended bedtime for the user for the second sleep session based at least in part on the first physiological data, the second physiological data, or both. The method also includes causing an indication of the recommended bedtime for the second sleep session to be communicated to the user via a user device before the recommended bedtime.

Devices, systems, and methods herein relate to non-invasive patient monitoring for infection detection and infection resolution. These systems and methods may receive and measure patient biosignals to estimate an infection level of a patient. In some embodiments, a method may include the steps of receiving physiological data of a patient. An infection measure may be estimated based on the physiological data. An infection state of the patient may be detected based at least in part on the estimated infection measure.

A method for identifying cough sounds in an audio recording of a subject including: operating at least one electronic processor to identify potential cough sounds in the audio recording; operating the at least one electronic processor to transform one or more of the potential cough sounds into corresponding one or more image representations; operating the at least one electronic processor to apply the one or more image representations to a representation pattern classifier trained to confirm that a potential cough sound is a cough sound or is not a cough sound; and operating the at least one electronic processor to flag one or more of the potential cough sounds as confirmed cough sounds based on an output of the representation pattern classifier.

There is provided a learning device, including a learning unit that learns output related to a target feature point to be observed in a repetition section observed periodically, with the use of the first sensor data being acquired by the first system and having a time length corresponding to the repetition section, as learning data, and of teacher data based on the second sensor data acquired by the second system at a time point when a specific period of time has elapsed since a start time point of the time length related to the first sensor data, the second system being less affected by noises than the first system, in which the specific period of time is set on the basis of a time length from a start time point of the repetition section to a time point at which the target feature point is expected to appear.

To enable accurately determining, based on a sound emitted by an inspection target, a classification of the sound. A control apparatus (1) according to an embodiment includes a classification information acquiring unit (13) that acquires classification information of a sound, a sound acquiring unit (11) that acquires a sound data including information of the sound, a storage unit (20) that stores definition data (25), an extraction unit (12) that extracts a plurality of features of the sound data, and a model construction unit (15) that constructs a learned model where machine learning, based on the plurality of features of the sound data and the classification information, on a correlation between the plurality of features and the classification of the sound is performed.

Disclosed herein are systems, devices, and methods for evaluating or analyzing complex audio signals using multi-dimensional statistical signatures and machine learning algorithms. One advantage of the present disclosure is the ability for remote evaluation of respiratory tract health using speech analysis. The need for remote collection capabilities that can sensitively and reliably characterize respiratory tract function is particularly pertinent in view of the recent Covid-19 pandemic, which may adversely affect the health of individuals who could already be experiencing health problems with respiratory tract function.

Provided are computer-implemented methods and systems for generating and/or utilizing data analysis algorithm(s) for predicting and/or detecting a clinical condition related to insertion of a medical instrument toward a target in a body of a patient based, inter alia, on data related to an automated medical device and/or to operation thereof.

The present disclosure relates to a difficult airway evaluation method and device based on a machine learning voice technology. The method includes the following steps: acquiring voice data of a patient; carrying out feature extraction on the voice data, obtaining a pitch period of pronunciations, and acquiring a voiced sound feature and unvoiced sound features based on the pitch period of pronunciations; and constructing a difficult airway evaluation classifier based on the machine learning voice technology, analyzing the received voiced sound feature and unvoiced sound features by the trained difficult airway evaluation classifier, and carrying out scoring on the severity of a difficult airway to obtain an evaluation result of the difficult airway.