Abstract: 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.

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: A system is provided that receives a signal file that includes multiple biophysical signals obtained from a patient by a signal capture or recorder device. The biophysical signals are measured from one or more sensors or probes of the signal capture device. The system executes one or more add-on modules that is each configured to generate information relevant to the health of the patient. Such information may include a score that in some embodiments represents a probability that the patient has and/or will develop a particular medical condition. The information generated for a patient from the signal file for each add-on module are provided to a health care provider and may be used to assist the healthcare provider in diagnosing the patient with respect to one or more of the medical conditions.

Abstract: The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.

Abstract: A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.

Abstract: A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.

Abstract: The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.

Abstract: A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.

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: A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.

Abstract: Systems and methods for the quantification of the quality of an acquired signal are provided for assessment and for gating the acquired signal for subsequent analysis. A signal is acquired, and a determination is made in real-time if there is a problem with the acquisition (e.g., if the acquired signal is acceptable or unacceptable; is of sufficient quality for subsequent assessment). If there is a problem, output is provided via the systems and methods described herein to indicate that signal acquisition needs to be performed again (e.g., if the acquired signal is unacceptable, reject the acquired signal and acquire a new signal).

Abstract: The exemplified methods and systems described herein facilitate the quantification and/or removal of asynchronous noise, such as muscle artifact noise contamination, to more accurately assess complex nonlinear variabilities in quasi-periodic biophysical-signal systems such as those in acquired cardiac signals, brain signals, etc.

Abstract: A facility providing systems and methods for discovering novel features to use in machine learning techniques. The facility receives, for a number of subjects, one or more sets of data representative of some output or condition of the subject over a period of time or capturing some physical aspect of the subject. The facility then extracts or computes values from the data and applies one or more feature generators to the extracted values. Based on the outputs of the feature generators, the facility identifies novel feature generators for use in at least one machine learning process and further mutates the novel feature generators, which can then be applied to the received data to identify additional novel feature generators.

Abstract: A facility for identifying combinations of feature and machine learning algorithm parameters, where each combination can be combined with one or more machine learning algorithms to train a model, is disclosed. The facility evaluates each genome based on the ability of a model trained using that genome and a machine learning algorithm to produce accurate results when applied to a validation data set by, for example, generating a fitness or validation score for the trained model and the corresponding genome used to train the model. Genomes that produce fitness scores that exceed a fitness threshold are selected for mutation, mutated, and the process is repeated. These trained models can then be applied to new data to generate predictions for the underlying subject matter.