Described herein are apparatuses and methods for classifying a patient as being asleep or awake. Such an apparatus can include an accelerometer and a processor. The accelerometer, alone or in combination with the processor, is used to determine an activity level of the patient and a posture of the patient. The processor is configured to classify the patient as being asleep in response to both (i) the posture of the patient being recumbent or reclined for at least a sleep latency duration, and (ii) the activity level of the patient not exceeding an activity threshold for at least the sleep latency duration; and classify the patient as being awake in response to at least one of (iii) the posture of the patient being upright for at least an awake latency duration, or (iv) the activity level of the patient exceeding the activity threshold for at least the awake latency duration.

Described herein are methods, devices, and systems that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient's heart, such as an EGM or ECG. Variations in one or more morphological or temporal features of the signal over several cardiac cycles are determined and used to detect a potential apnea event in a measurement period. Checks can then be made for a number of factors which could result in a false detection of an apnea event and if such factors are not present, an apnea event is recorded. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients or to a set of rules developed from the database. An ST segment and one or more ST subwaveforms within the ST segment are identified from the plurality of subwaveforms and discontinuity points based on the comparison. The ECG waveform with the one or more ST subwaveforms within the ST segment is displayed.

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.

This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.

This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.

Described herein are methods, devices, and systems that use electrogram (EGM) or electrocardiogram (ECG) data for sleep apnea detection. An apparatus and method detect potential apnea events (an apnea or hypopnea event) using a signal indicative of cardiac electrical activity of a patient's heart, such as an EGM or ECG. Described herein are also methods, devices, and systems for classifying a patient as being asleep or awake, which can be used to selectively enable and disable sleep apnea detection monitoring, as well as in other manners.

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 pre-hospital emergency care, and (3) techniques for integrating these scores into an emergency medical care system.

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.