A clinical evaluation system and method are disclosed that facilitate the use of one or more visual features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. The visual features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.

The exemplified methods and systems (e.g., machine-learned systems) facilitate the use of respiration rate-related features, or parameters, in a model or classifier to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease, medical condition, or indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases, medical conditions, or indication of either or in the treatment of said diseases or indicating conditions. In some cases, such respiration rate-related features are generated from a synthetic respiration waveform that represents, and is used as a proxy to, the true respiration waveform. The synthetic respiration waveform may be used in its own independent diagnostic and/or control applications in some embodiments.

A method for determining a health status of a system of interest is proposed. The method comprises acquiring (S1) a time series, extracting (S2) subsequences, selecting (S3) a set of subsequences, classifying (S4) the subsequences of the set into several groups on the basis of at least one criterion of resemblance to at least one reference subsequence, and constructing (S5) a normal operating model of the system of interest. The construction includes, for each group, a modeling (S51) of a representative subsequence and a determination (S52) of an associated weight. The normal model is defined by the modeled subsequences and the associated weights. The method further includes an attribution (S6) of a normality score to each subsequence extracted by comparison with the normal model, an identification (S7) of at least one abnormal subsequence, and a determination (S8) of the health status of the system of interest.

A method for determining a health status of a system of interest is proposed. The method comprises acquiring (S1) a time series, extracting (S2) subsequences, selecting (S3) a set of subsequences, classifying (S4) the subsequences of the set into several groups on the basis of at least one criterion of resemblance to at least one reference subsequence, and constructing (S5) a normal operating model of the system of interest. The construction includes, for each group, a modeling (S51) of a representative subsequence and a determination (S52) of an associated weight. The normal model is defined by the modeled subsequences and the associated weights. The method further includes an attribution (S6) of a normality score to each subsequence extracted by comparison with the normal model, an identification (S7) of at least one abnormal subsequence, and a determination (S8) of the health status of the system of interest.

A back-mounted support device provides a stable platform for various monitoring and position support devices. The support device comprises a stiffening member configured for placement along a user's spine, the stiffening member extending from a top end to be positioned between the user's shoulder blades to a bottom end to be positioned along the spine and near a waistline of the user. A shoulder attachment system is coupled to the stiffening member to stabilize an upper portion of the stiffening member. A waist attachment system is coupled to the stiffening member to stabilize a lower portion of the stiffening member. The shoulder and waist attachment systems are configured to stabilize the stiffening member along the spine, while minimizing irritating body contact. Monitoring devices may be mounted anywhere on the support device for facilitating monitoring of body parameters.

The disclosed system receives various physiological as well as physical information concerning a patient, and operational data from a ventilation device and medication delivery device, and provides the physiological and physical information, together with the operational data, to a neural network configured to analyze the information and data. The system receives, from the neural network, an assessment classification of the patient corresponding to at least one of a pain assessment, a sepsis assessment, and a delirium assessment of the patient based on providing to the neural network the determined physiological state of the patient, the determined physical state of the patient, the determined operational mode of the ventilator, the medication delivery information, and the received diagnostic information for the patient, and adjusts, based on the assessment classification, a ventilation parameter that influences the operational mode of a ventilator providing ventilation to the patient.

A method of displaying analysis data of an electrocardiogram signal includes receiving, by an electrocardiogram data processing server, a load input with respect to a first electrocardiogram signal from a user terminal, generating, by the electrocardiogram data processing server, data including analysis data of the first electrocardiogram signal and a first expected analysis time of the first electrocardiogram signal, and transmitting, by the electrocardiogram data processing server, the analysis data to the user terminal to display, on the user terminal, a first window displaying the first electrocardiogram signal and a second window displaying the first expected analysis time.

A method of displaying analysis data of an electrocardiogram signal includes receiving, by an electrocardiogram data processing server, a load input with respect to a first electrocardiogram signal from a user terminal, generating, by the electrocardiogram data processing server, data including analysis data of the first electrocardiogram signal and a first expected analysis time of the first electrocardiogram signal, and transmitting, by the electrocardiogram data processing server, the analysis data to the user terminal to display, on the user terminal, a first window displaying the first electrocardiogram signal and a second window displaying the first expected analysis time.

The present invention provides a method and an apparatus for obtaining relevant characteristic parameters and indexes of tonoarteriogram (TAG) signals, relating to the technical field of blood pressure monitoring. A signal acquisition module acquires TAG signals from a target subject; a signal processing module obtains relevant characteristic parameters and indexes of continuous blood pressure from obtained TAG signals of target subject by calculating and processing through a predetermined mathematical model and a statistical algorithm, wherein the relevant characteristic parameters and indexes of continuous blood pressure include at least one of root mean square value of continuous blood pressure (RMSBP) and standard deviation of continuous blood pressure (SDBP), an evaluation module evaluates the status of continuous blood pressure based on characteristic parameters and indexes obtained from the target subject, an alarm module initiates an alarm when the relevant characteristic parameters and indexes of continuous blood pressure monitored to be abnormal, which allows to monitor the blood pressure of the target user at different period of time, realizing rapid blood pressure measurement, and enabling timely alarms to ensure the safety of the target user.

A medical system including processing circuitry configured to assess a blood glucose level of a patient. The processing circuitry is configured to use a cardiac signal indicative of the electrical activity of the patient's heart and a temperature signal indicative of a body temperature of the patient. The cardiac signal may be, for example, an electrocardiogram (ECG), an electrogram (EGM), or another measure. The medical system is configured to determine a representative cardiac measure indicative of the cardiac signal and determine a representative temperature measure indicative of the temperature signal. The medical system is configured to assess when a blood glucose level of the patient may be outside a euglycemic range based on the representative cardiac measure and the representative temperature measure determined.