SYSTEMS AND METHODS OF ANALYTE MEASUREMENT ANALYSIS
Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.
1 . A system comprising:
a processing device operatively connected to the electrocardiogram sensor, the processing device configured to:
receive electrocardiogram data of a user from an electrocardiogram sensor;
automatically analyze, the electrocardiogram data using a machine learning model trained using feature learning using historical electrocardiogram data and historical analyte levels, wherein analyzing the electrocardiogram data comprises:
analyzing at least a feature from the electrocardiogram data;
and
determining a potassium level associated with the electrocardiogram data as a function of the at least an output; and
display, using a user interface of a device, the potassium level.
2 . The system of claim 1 , wherein the electrocardiogram data comprises a 1 lead sensor electrocardiogram data, wherein the 1 lead sensor electrocardiogram data is collected from a remote device, wherein the remote device comprises a smart watch.
3 . The system of claim 1 , further comprising an alert service,
wherein the alert service is configured to:
generating a comparison, wherein the comparison is between the at least an output to a threshold;
generate an alert associated with the potassium level associated with the electrocardiogram data as a function of the comparison; and
transmit the alert to a remote device.
4 . The system of claim 3 , wherein the alert comprises a recommendation to contact a doctor.
5 . The system of claim 3 , wherein:
generating an alert associated with the potassium level associated with the
electrocardiogram data comprises generating a doctor alert; and
the alert service is further configured to transmit the doctor alert to a doctor.
6 . The system of claim 1 , further comprising a display device, wherein the display device is configured to display the potassium level using at least a visual element within a graphical user interface of the device.
7 . The system of claim 1 , wherein the processing device further comprises preprocessing the electrocardiogram data of the user by removing noise.
8 . The system of claim 1 , wherein the at least a feature comprises one or more of a T-wave amplitude and a T wave morphology.
9 . The system of claim 1 , wherein the machine learning model has been trained using training data comprising:
a first measurement of the potassium level at a first time; and
a set of estimated values of the potassium level over a time period subsequent to the first time based in part on the level of the potassium level at the first time.
10 . The system of claim 9 , wherein the set of estimated values of the potassium level over a time period subsequent has been derived from the first measurement of the potassium level at a first time and a second measurement of the potassium level at a second time.
11 . A method comprising:
receiving, using a processing device, electrocardiogram data of a user;
automatically analyzing, the electrocardiogram data using a machine learning model trained using feature learning using historical electrocardiogram data and historical analyte levels, wherein analyzing the electrocardiogram data comprises:
analyzing at least a feature from the electrocardiogram data to generate at least an output; and
determining a potassium level associated with the electrocardiogram data as a function of the at least an output; and
displaying, using a user interface of a device, the potassium level.
12 . The method of claim 11 , wherein the electrocardiogram data comprises a 1 lead sensor electrocardiogram data, wherein the 1 lead sensor electrocardiogram data is collected from a remote device, wherein the remote device comprises a smart watch.
13 . The method of claim 11 , further comprising an alert service,
wherein the alert service is configured to:
generate a comparison, wherein the comparison is between the at least an output to a threshold;
generate an alert associated with the potassium level associated with the electrocardiogram data as a function of the comparison; and
transmit the alert to a remote device.
14 . The method of claim 13 , wherein the alert comprises a recommendation to contact a doctor.
15 . The method of claim 13 , wherein:
generating an alert associated with the potassium level associated with the electrocardiogram data comprises generating a doctor alert; and
the alert service is further configured to transmit the doctor alert to a doctor.
16 . The method of claim 11 , further comprising a display device, wherein the display device is configured to display the potassium level using at least a visual element within a graphical user interface of the device.
17 . The method of claim 11 , further comprising preprocessing, using the processing device, the electrocardiogram data of the user by removing noise.
18 . The method of claim 11 , wherein the at least a feature comprises one or more of a T-wave amplitude and a T wave morphology.
19 . The method of claim 11 , wherein the machine learning model has been trained using training data comprising:
a first measurement of the potassium level at a first time,
a set of estimated values of the potassium level over a time period subsequent to the first time based in part on the level of the potassium level at the first time.
20 . The method of claim 19 , wherein the set of estimated values of the potassium level over a time period subsequent has been derived from the first measurement of the potassium level at a first time and a second measurement of the potassium level at a second time.