The present invention relates to methods for assessing swallowing motor function in a subject. The methods rely on obtaining intraluminal impedance and pressure measurements from the pharynx and/or esophagus of the subject during clearance of a bolus from the mouth and/or throat of the subject. The intraluminal impedance and pressure measurements are combined to derive a value for one or more pressure-flow variables in the pharynx and/or the esophagus of the subject. The value of the one or more pressure-flow variables is compared to a predetermined pharyngeal and/or esophageal reference value for the one or more pressure-flow variables in order to provide an assessment of swallowing motor function in the subject. The intraluminal impedance and pressure measurements can also be combined to generate a swallow risk index for the subject or to generate an obstructive risk index for the subject based on a combination of a value of more than one pressure-flow variable in the pharynx and/or esophagus of the subject. In this way, swallowing motor function in the subject can be assessed by comparing the swallow risk index or obstructive risk index for the subject to a predetermined reference swallow index or predetermined reference obstructive index, respectively. Products which make use of these methods are also encompassed by the present invention.

A system including a sensor interface coupled to a processor. The sensor interface is configured to receive and process an analog electrocardiogram signal of a subject and provide a digitized electrocardiogram signal sampled over a first time period and a second time period that is subsequent to the first time period. The processor is configured to receive the digitized electrocardiogram signal, to analyze a frequency domain transform of the digitized electrocardiogram signal sampled over the first and second time periods and determine first and second metrics indicative of metabolic state of a myocardium of the subject during the first and second time periods, respectively, to compare the first and second metrics to determine whether the metabolic state of the myocardium of the subject is improving, and to indicate administration of an intervention to the subject in response to a determination that the metabolic state is not improving.

The present disclosure generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

A technology for a wearable medical device for monitoring medical parameters. Medical measurement data can be received at the wearable medical device from a medical measurement sensor attached to the wearable medical device or a medical measurement sensor in communication with the wearable medical device. A calibration coefficient can be determined for calibrating the wearable medical device based on the medical measurement data. The wearable medical device can be calibrated based on the calibration coefficient.

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.

A load cell apparatus for use with a bed includes a housing having a top portion and a bottom portion, and a load cell device held by the bottom portion of the housing. The load cell device is structured to generate a signal having a magnitude that is proportional to a first force being applied to the load cell device. The load cell apparatus also includes a button member held by the housing in a manner wherein the button member is structured to engage the load cell device and apply the first force to the load cell device in response to a second force being applied to the top portion of the housing. Also, various systems for monitoring parameters such as weight, sleep quality, fall risk, and/or pressure sore risk that may incorporate such a load cell apparatus.

A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

Cardiac electrograms are recorded in a plurality of channels. Beats are classified automatically into respective classifications according to a resemblance of the morphologic characteristics of the beats to members of a set of templates. Respective electroanatomic maps of the heart are generated from the classified beats.

Embodiments of the present disclosure relate to systems and methods for determining a subject's blood pressure using one or more implantable medical devices (IMDs). In an embodiment, a medical system comprises: at least one implantable medical device configured to sense signals associated with heart sounds of a subject and a processing unit communicatively coupled to the at least one implantable medical device. The processing unit is configured to: receive heart sound signals corresponding to the signals associated with the heart sounds; and calculate a surrogate of the subject's blood pressure using at least one heart sound signal of the received heart sound signals.

Provided is a biological information processing method and a device, a recording medium and a program that are able to predict and control changes in the state of an organism. The expression level of molecules in an organism is measured over a specific time interval; the measured time-series data is divided into a periodic component, an environmental stimulus response component and a baseline component; constant regions of the time-series data are identified from variations in the baseline component or from the amplitude or periodic variations of the periodic component; and causal relation between the identified constant regions is identified. The relation between the external environment and variations in the internal environment is identified and from the identified causal relation between the constant regions, changes in the state of the organism are inferred.