Patent ID: 11903681
Assignee: ALIVECOR, INC.
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A

Claim 7:
8. A smartwatch, comprising:
a processor;
a first sensor configured to continuously sense an activity level value of a user, wherein the first sensor is coupled to the processor;
a photopletysmogram (PPG) sensor configured to continuously sense a set of heart rate parameters of the user, wherein the PPG sensor is coupled to the processor; and
a non-transitory computer readable storage medium encoded with a computer program including instructions executable by the processor to cause the processor to:
in response to sensing a resting activity level, compare, using a machine learning (ML) algorithm, the resting activity level with the set of heart rate parameters to determine whether a discordance is present between the resting activity level of the user and the set of heart rate parameters sensed when the resting activity level is sensed, wherein the set of heart rate parameters comprise an indication of a heart rate variability and an indication of a heart rate value;
in response to determining that the discordance is present:
indicate to the user a possibility of an arrhythmia being present;
initiate an ECG of the user using ECG electrodes of the smartwatch;
receive electric signals of the user from the ECG electrodes of the smartwatch if the possibility of an arrhythmia is indicated; and
display an ECG rhythm strip from the received electric signals; and

improving an accuracy with which the ML algorithm identifies an arrhythmia by:

storing the resting activity level and the set of heart rate parameters as part of a historical set of parameter values, the historical set of parameter values including previously recorded resting activity levels and previously recorded heart rate parameters;
storing the ECG rhythm strip as part of a historical set of ECG data comprising previously recorded ECG rhythm strips; and
training the ML algorithm by comparing the historical set of parameter values with the historical set of ECG data.