A method for health monitoring of a subject. The method includes measuring each of a plurality of physiological parameters once per a respective time period of a plurality of time periods. Measuring each of the plurality of physiological parameters includes measuring a heart rate of the plurality of physiological parameters by installing a sensor package on a region at a right side of a chest of the subject Installing the sensor package on the region includes placing a pair of electrocardiography (ECG) electrodes and an accelerometer in the sensor package on the region which includes the anterior edge of the right serratus anterior muscle of the subject.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data/distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and/or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a high range, provide medically relevant detections including medication non-compliance where the patient is alerted through a patient alerting mechanism of an abnormality in the heart rate or ST shifts of the patient.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand/supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data/distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and/or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a high range, provide medically relevant detections including medication non-compliance where the patient is alerted through a patient alerting mechanism of an abnormality in the heart rate or ST shifts of the patient.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

The present disclosure encompasses an artifact score derived from the signal characteristics of an acquired 12-lead ECG, (2) a patient context score derived from key elements of the patient's history, presentation, and prehospital emergency care, and (3) techniques for integrating these scores into an emergency medical care system.

A method enables analysis of (e.g. bioelectric) signals acquired by measurement. The method provides N signals U for an observation space and each has a time- and space-dependent signal characteristic U. Digitized signals for a time period T have M time points and define an M?N matrix with M tuples of N signal values each. Signal values acquired at time t form an N-tuple ?.sub.t=(U.sub.1, . . . , U.sub.N).sub.t in a signal space. The method acquires all combinations of k tuples from the M tuples, and calculates distances between all tuples. Distance values are calculated and define edge lengths of a (k?1) simplex (SIM) with one simplex assigned to each combination of k time points. Quantity characteristics of the simplex (SIM) are encoded into color values (COL), and displays the colors in a combinatorial time lattice (CTL). Each lattice point (GP) is displayed with the color encoded for the assigned simplex.

A method enables analysis of (e.g. bioelectric) signals acquired by measurement. The method provides N signals U for an observation space and each has a time- and space-dependent signal characteristic U. Digitized signals for a time period T have M time points and define an M?N matrix with M tuples of N signal values each. Signal values acquired at time t form an N-tuple ?.sub.t=(U.sub.1, . . . , U.sub.N).sub.t in a signal space. The method acquires all combinations of k tuples from the M tuples, and calculates distances between all tuples. Distance values are calculated and define edge lengths of a (k?1) simplex (SIM) with one simplex assigned to each combination of k time points. Quantity characteristics of the simplex (SIM) are encoded into color values (COL), and displays the colors in a combinatorial time lattice (CTL). Each lattice point (GP) is displayed with the color encoded for the assigned simplex.

Electrical ventricular depolarization may be represented by its vectorcardiographic QRS loop, which exists in 3D space. By recognizing that the QRS loop is often a closed trajectory on a plane (2D), it is possible to change (e.g., by rotation, projection, etc.) the single-channel QRS into a different view angle along that plane to provide a viewpoint more favorable for morphologic interpretation. Any monophasic or biphasic single-channel QRS (time series) waveform can be decomposed into the form x(?,t)=sin(?t)u(t), with t spanning [0,2?], where u(t) is an unchanging intrinsic component that is generally upright and monophasic, and where sin(?t) is a changeable component of the waveform. This way, x(?,t) may be changed by replacing a parameter of the original changeable component sin(?t) with a second parameter associated with a target view of the waveform, such as the upright monophasic sin(0.5t). Meanwhile the intrinsic component remains unchanged during this change.

Electrical ventricular depolarization may be represented by its vectorcardiographic QRS loop, which exists in 3D space. By recognizing that the QRS loop is often a closed trajectory on a plane (2D), it is possible to change (e.g., by rotation, projection, etc.) the single-channel QRS into a different view angle along that plane to provide a viewpoint more favorable for morphologic interpretation. Any monophasic or biphasic single-channel QRS (time series) waveform can be decomposed into the form x(?,t)=sin(?t)u(t), with t spanning [0,2?], where u(t) is an unchanging intrinsic component that is generally upright and monophasic, and where sin(?t) is a changeable component of the waveform. This way, x(?,t) may be changed by replacing a parameter of the original changeable component sin(?t) with a second parameter associated with a target view of the waveform, such as the upright monophasic sin(0.5t). Meanwhile the intrinsic component remains unchanged during this change.