Abstract: An exemplary method is disclosed that can be used in the diagnosis of hypertrophic cardiomyopathy (HCM) using a biophysical-sensor system configured to non-invasively and concurrently acquire electrocardiographic signals, seismographic signals, photoplethysmographic, and/or phonocardiographic signals, collectively referred to herein as biophysical signals, from at least the thoracic region of a subject. The acquired biophysical signals may be assessed for one or more conditions or indicators of hypertrophic cardiomyopathy and concurrently with other cardiac diseases, conditions, or indicators of either.

Abstract: Atmospheric moisture harvester systems include two beds with water capture material, such as metal-organic framework (MOF), a heater, two fans, and a condenser having two sides, operatively configured into adsorption and desorption modes, wherein the MOF beds are interchangeable to cycle between the desorption and water adsorption modes. The systems may further include a photovoltaic panel powering the fans and condenser.

Abstract: 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.

Abstract: A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and/or VD conduction derivation Poincaré features or parameters. The conduction derivation 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, a 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.

Abstract: A clinical evaluation system and method are disclosed that facilitate the use of one or more morphologic atrial depolarization waveform-based features or parameters determined from biophysical signals such as cardiac or biopotential signals that are acquired, in preferred embodiments, non-invasively from surface sensors placed on a patient while the patient is at rest. Morphologic atrial depolarization waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including 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.

Abstract: The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal.

Abstract: A clinical evaluation system and method are disclosed that facilitate the use of features or parameters extracted from biophysical signals 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 elevated left ventricular end-diastolic pressure (elevated LVEDP), as an example indicator of a disease medical condition that could be assessed by using the system and method described herein.

Abstract: The exemplified methods and systems facilitate the use, for diagnostics, monitoring, treatment, of one or more power spectral-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The power spectral-based 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.

Abstract: The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more PPG waveform-based features or parameters determined from biophysical signals such as photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. PPG waveform-based features or parameters may include PPG waveform features or parameters, VPG waveform features or parameters, and/or APG waveform features or parameters. The PPG waveform-based features or parameters can be used in a model or classifier to estimate metrics associated with the physiological state of a patient, including 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.

Abstract: The exemplified methods and systems facilitate the use, for diagnostics, monitoring, or treatment, of one or more cycle variability based features or parameters determined from biophysical signals such as cardiac or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. 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.

Abstract: Atmospheric water harvesting systems utilize a sorbent cartridge configured to hold water capture material. The sorbent cartridge is made up of a plurality of permeable trays and a plurality of spacers that are arranged to provide cross-flow for adsorption and desorption airflow pathways. The systems are used for harvesting water from surrounding air.

Abstract: The exemplified methods and systems facilitate the quantification of cardiac cycle-variability as a metric of signal quality of an acquired signal data set and the rejection, based on that quantification, of said acquired signal data set from one or more subsequent analyses that can predict and/or estimate a metric associated with the presence, non-presence, severity, and/or localization of abnormal cardiovascular conditions or disease, including, for example, but not limited to, coronary artery disease, abnormal left ventricular end-diastolic pressure disease (LVEDP), pulmonary hypertension and subcategories thereof, heart failure (HF), among others as discussed herein.

Abstract: Provided herein are atmospheric moisture harvester systems, as well as methods using such systems, for capturing water from surrounding air. The systems and methods use adsorbents to adsorb water from the air. For example, the adsorbents may be metal-organic frameworks. The systems and methods desorb this water in the form of water vapor, and the water vapor is condensed into liquid water and collected. The liquid water is suitable for use as drinking water.

Abstract: Atmospheric moisture harvester systems include two beds with water capture material, such as metal-organic framework (MOF), a heater, two fans, and a condenser having two sides, operatively configured into adsorption and desorption modes, wherein the MOF beds are interchangeable to cycle between the desorption and water adsorption modes. The systems may further include a photovoltaic panel powering the fans and condenser.

Abstract: Provided herein are atmospheric moisture harvester systems, as well as methods using such systems, for capturing water from surrounding air. The systems and methods use adsorbents to adsorb water from the air. For example, the adsorbents may be metal-organic frameworks. The systems and methods desorb this water in the form of water vapor, and the water vapor is condensed into liquid water and collected. The liquid water is suitable for use as drinking water.

Abstract: The exemplified methods and systems employs non-invasively acquired biophysical measurements of a subject in a residue analysis that is structured as a three-dimensional volumetric object to which machine extractable features associated with a geometric associated aspect of the three-dimensional volumetric object may be derived and used for in the training and/or prediction of a disease state. The system generates a residue model from a point-cloud residue generated from an analysis of the plurality of biophysical signal data sets. The system generates a three-dimensional volumetric object from the point-cloud residue from which machine extractable features associated with the point-cloud residue maybe extracted.