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.

Provided are a monitoring device, a method for setting reference baseline and a readable storage medium. Whether the monitoring device has a system error is determined due to at least one event, according to at least one of the moving state of the target object, the connection state of the target object and the signal acquisition device, the environmental information where the target object is located, and the analysis result of the monitoring device. When the monitoring device has a system error, the current reference baseline of the monitoring device is updated according to the preset rules, and then the monitoring is continued based on the new reference baseline. In this way, the current reference baseline of the monitoring device can be adjusted in time, false alarms and missed alarms caused by the system error can be avoided and the accuracy of the monitoring device can be improved.

Provided are a monitoring device, a method for setting reference baseline and a readable storage medium. Whether the monitoring device has a system error is determined due to at least one event, according to at least one of the moving state of the target object, the connection state of the target object and the signal acquisition device, the environmental information where the target object is located, and the analysis result of the monitoring device. When the monitoring device has a system error, the current reference baseline of the monitoring device is updated according to the preset rules, and then the monitoring is continued based on the new reference baseline. In this way, the current reference baseline of the monitoring device can be adjusted in time, false alarms and missed alarms caused by the system error can be avoided and the accuracy of the monitoring device can be improved.

An electronic device and method are disclosed herein. The electronic device includes a housing, a first, second, third and fourth electrode coupled to the housing, a communication module, and a processor. The processor implements the method, including: detect a first signal using the first electrode and the fourth electrode, detect a second signal using the second electrode and the fourth electrode, detect a third signal using the third electrode and the fourth electrode, transmit the first signal, the second signal, and the third signal to an external electronic device via the communication module, and receive, from the external electronic device, via the communication module, data for generating guidance information to correct an attachment position of the electronic device, wherein the guidance information is generated based on: a first biological signal generated based on the first signal and the second signal, a second biological signal generated based on the second signal and the third signal, and a third biological signal generated based on the third signal and the first signal.

An electronic device and method are disclosed herein. The electronic device includes a housing, a first, second, third and fourth electrode coupled to the housing, a communication module, and a processor. The processor implements the method, including: detect a first signal using the first electrode and the fourth electrode, detect a second signal using the second electrode and the fourth electrode, detect a third signal using the third electrode and the fourth electrode, transmit the first signal, the second signal, and the third signal to an external electronic device via the communication module, and receive, from the external electronic device, via the communication module, data for generating guidance information to correct an attachment position of the electronic device, wherein the guidance information is generated based on: a first biological signal generated based on the first signal and the second signal, a second biological signal generated based on the second signal and the third signal, and a third biological signal generated based on the third signal and the first signal.

Provided is a method for displaying electrocardiogram (ECG) data for the detection of myocardial ischemia, wherein a map is configured in the form of concentric circles using ST segments obtained through ECG measurement such that the inner circle consists of a “depression” origin and the outer circle consists of an “elevation” origin so as to display by connecting graphs or dots according to the measured ECG values of limb leads (frontal plane leads; I to aVF) and chest leads (precordial plane leads; V1 to V6), and the intuitive confirmation of the presence of subendocardial ischemia or transmural injury is enabled through the map, thereby making it possible to quickly and accurately recognize the patient's conditions and inducing prompt treatment.

Provided is a method for displaying electrocardiogram (ECG) data for the detection of myocardial ischemia, wherein a map is configured in the form of concentric circles using ST segments obtained through ECG measurement such that the inner circle consists of a “depression” origin and the outer circle consists of an “elevation” origin so as to display by connecting graphs or dots according to the measured ECG values of limb leads (frontal plane leads; I to aVF) and chest leads (precordial plane leads; V1 to V6), and the intuitive confirmation of the presence of subendocardial ischemia or transmural injury is enabled through the map, thereby making it possible to quickly and accurately recognize the patient's conditions and inducing prompt treatment.

An example medical device system includes memory configured to store information relating to an occurrence of a cardiac event in a patient and processing circuitry configured to determine the occurrence of the cardiac event in the patient. The processing circuitry is configured to determine at least one of a fall of the patient, a slumping posture of the patient, or a change from an upright posture to a non-upright posture of the patient and that the cardiac event is associated therewith in time. The processing circuitry is configured to, in response to determining that the cardiac event and the at least one of the fall of the patient, the slumping posture of the patient, or the change from the upright posture to the non-upright posture of the patient are associated in time, modulate a response to the cardiac event.

An example medical device system includes memory configured to store information relating to an occurrence of a cardiac event in a patient and processing circuitry configured to determine the occurrence of the cardiac event in the patient. The processing circuitry is configured to determine at least one of a fall of the patient, a slumping posture of the patient, or a change from an upright posture to a non-upright posture of the patient and that the cardiac event is associated therewith in time. The processing circuitry is configured to, in response to determining that the cardiac event and the at least one of the fall of the patient, the slumping posture of the patient, or the change from the upright posture to the non-upright posture of the patient are associated in time, modulate a response to the cardiac event.

There is provided a computer-implemented method for identifying abnormal QRS-complexes in an ECG signal comprising: receiving an ECG signal with detected QRS-complexes; calculating and normalizing a plurality of geometric measures of the QRS-complexes; constructing an RGB image with three two-dimensional matrices, each matrix corresponding to one of the calculated plurality of geometric measures, the geometric measures belonging to one QRS-complex have the same matrix index across the three two-dimensional matrices; transforming the RGB image to a plurality of gray-scale images; computing histograms of each of the plurality of gray-scale image; iteratively comparing every histogram peak to its previous one; and marking pixel intensities corresponding to histogram peaks with a difference value of equal or greater than a predefined threshold referred to as a saliency threshold, as salient pixels; using saliency detection for mapping salient pixels indices onto the ECG signal and classifying the salient pixel indices as abnormal QRS complexes.