Systems, Devices, and Methods for Cardiac Diagnosis and/or Monitoring

Abstract

Some embodiments of the current disclosure are directed toward cardiac diagnosis and/or arrhythmia monitoring, and more particularly, systems, devices and methods for arrhythmia monitoring with a trained classifier including at least one neural network. In some embodiments, an external heart monitoring device may include a plurality ECG electrodes to sense surface ECG activity, ECG processing circuitry to process the surface ECG activity to provide at least one ECG signal, a non-transitory computer-readable medium comprising a rhythm change classifier comprising at least one neural network, and at least one processor to receive the ECG signal(s), detect with the rhythm change classifier time data corresponding to a predetermined rhythm change in the ECG signal(s), determine based on the detected time data at least one ECG signal portion corresponding to the predetermined rhythm change, and transmit the at least one determined ECG signal portion to a remote computer system.

What is currently claimed: 
     
         1 . An arrhythmia monitoring system, comprising:
 an external heart monitoring device for a patient comprising:
 a plurality of electrocardiogram (ECG) electrodes configured to sense surface ECG activity of the patient; 
 ECG processing circuitry configured to process the surface ECG activity of the patient to provide at least one ECG signal for the patient on at least one ECG channel; and 
 at least one first processor operatively connected to the at least one ECG channel, the at least one first processor configured to:
 receive the at least one ECG signal received via the at least one ECG channel, and 
 transmit the at least one ECG signal; 
 
   a gateway device comprising:
 a non-transitory computer-readable medium comprising a rhythm change classifier, the rhythm change classifier comprising at least one neural network trained based on a historical collection of a plurality of ECG signal portions with known rhythm change information; and 
 at least one second processor operatively connected to the non-transitory computer-readable medium, the at least one second processor configured to:
 receive the at least one ECG signal from the external heart monitoring device, 
 detect with the rhythm change classifier time data corresponding to a predetermined rhythm change in the at least one ECG signal, the time data comprising at least one of a start time, a time interval, or any combination thereof, 
 determine based on the detected time data at least one ECG signal portion associated with the detected time data corresponding to the predetermined rhythm change in the at least one ECG signal, and 
 transmit the at least one determined ECG signal portion to a remote computer system. 
 
   
     
     
         2 . The arrhythmia monitoring system of  claim 1 , wherein the at least one determined ECG signal portion comprises a plurality of determined ECG signal portions, wherein the remote computer system is in communication with the gateway device, the remote computer system configured to:
 receive the plurality of determined ECG signal portions from the external heart monitoring device, and   analyze each respective determined ECG signal portion of the plurality of determined ECG signal portions to classify a respective class for each respective determined ECG signal portion, wherein the class for at least two respective determined ECG signal portions comprises a first class.   
     
     
         3 . The arrhythmia monitoring system of  claim 2 , wherein the remote computer system is further configured to transmit at least one message associated with the at least two respective determined ECG signal portions to a computing device associated with a technician. 
     
     
         4 . The arrhythmia monitoring system of  claim 3 , wherein the computing device associated with the technician is configured to display a graphical user interface for batch review of the at least two respective determined ECG signal portions of the first class. 
     
     
         5 . The arrhythmia monitoring system of  claim 2 , wherein analyzing each respective determined ECG signal portion of the plurality of determined ECG signal portions to classify the respective class for each respective determined ECG signal portion comprises bucketing the plurality of determined ECG signal portions into a plurality of buckets, wherein the first class comprises a first bucket of the plurality of buckets. 
     
     
         6 . The arrhythmia monitoring system of  claim 5 , wherein bucketing the plurality of determined ECG signal portions into the plurality of buckets comprises grouping the plurality of determined ECG signal portions based on at least one of an output of a neural network, a similarity of features of the plurality of determined ECG signal portions, a similarity of vector representations of the plurality of determined ECG signal portions, or any combination thereof. 
     
     
         7 . The arrhythmia monitoring system of  claim 1 , wherein the external heart monitoring device comprises a wearable patch. 
     
     
         8 . The arrhythmia monitoring system of  claim 1 , wherein the external heart monitoring device comprises a wearable defibrillator. 
     
     
         9 . The arrhythmia monitoring system of  claim 1 , wherein the remote computer system is in communication with the gateway device, the remote computer system configured to:
 receive the at least one determined ECG signal portion from the external heart monitoring device, and   analyze the at least one determined ECG signal portion to classify a type of arrhythmia for the rhythm change in the at least one ECG signal.   
     
     
         10 . The arrhythmia monitoring system of  claim 1 , wherein the at least one ECG channel comprises at least a first ECG channel and a second ECG channel, wherein the at least one ECG signal comprises at least a first ECG signal associated with the first ECG channel and a second ECG signal associated with the second ECG channel, and wherein the first respective ECG signal is orthogonal to the second respective ECG signal. 
     
     
         11 . The arrhythmia monitoring system of  claim 1 , further comprising:
 at least one sensor and associated sensor circuitry configured to sense non-ECG biometric data of the patient,   wherein the at least one second processor is further configured to detect with the rhythm change classifier the predetermined rhythm change based on the at least one ECG signal and the non-ECG biometric data of the patient.   
     
     
         12 . The arrhythmia monitoring system of  claim 11 , wherein the at least one sensor comprises at least one of an accelerometer, a heart sound detector, or a combination thereof, and wherein the non-ECG biometric data comprises at least one of acceleration data, heart sound data, or any combination thereof. 
     
     
         13 . The arrhythmia monitoring system of  claim 11 , wherein detecting the predetermined rhythm change is further based on at least one of:
 at least one baseline ECG signal portion of the patient;   at least one reference vector of the patient;   at least one calibration measurement of the patient, the at least one calibration measurement based on at least one second ECG signal from second surface ECG activity sensed by a second plurality of ECG electrodes, the second plurality of ECG electrodes independent of the plurality of ECG electrodes of the external heart monitoring device; or   at least one previous ECG signal portion.   
     
     
         14 . The arrhythmia monitoring system of  claim 1 , wherein the at least one ECG channel comprises a plurality of ECG channels, wherein the at least one ECG signal comprises at least one respective ECG signal associated with each respective ECG channel of the plurality of ECG channels,
 wherein the at least one neural network comprises a plurality of Siamese branches, each respective Siamese branch of the plurality of Siamese branches associated with a respective ECG channel of the plurality of ECG channels, and   wherein the at least one neural network further comprises at least one further layer connected to the plurality of Siamese branches.   
     
     
         15 . The arrhythmia monitoring system of  claim 14 , wherein each Siamese branch of the plurality of Siamese branches comprises a plurality of convolutional layers, wherein dimensions of each of the plurality of convolutional layers of each respective Siamese branch are the same as the dimensions of each of the plurality of convolutional layers of each other Siamese branch. 
     
     
         16 . The arrhythmia monitoring system of  claim 14 , wherein the plurality of ECG channels comprises a first ECG channel and a second ECG channel, wherein the at least one ECG signal comprises a first respective ECG signal associated with the first ECG channel and a second respective ECG signal associated with the second ECG channel, and wherein the first respective ECG signal is orthogonal to the second respective ECG signal. 
     
     
         17 . An arrhythmia monitoring system, comprising:
 an external heart monitoring device for a patient comprising:
 a plurality of electrocardiogram (ECG) electrodes configured to sense surface ECG activity of the patient; 
 ECG processing circuitry configured to process the surface ECG activity of the patient to provide at least one ECG signal for the patient on at least one ECG channel; 
 a non-transitory computer-readable medium comprising a rhythm change classifier, the rhythm change classifier comprising at least one neural network trained based on a historical collection of a plurality of ECG signal portions with known rhythm change information; and 
 at least one processor operatively connected to the at least one ECG channel and the non-transitory computer-readable medium, the at least one processor configured to:
 receive the at least one ECG signal received via the at least one ECG channel, 
 detect, with the rhythm change classifier, time data corresponding to a predetermined rhythm change in the at least one ECG signal, the time data comprising at least one of a start time, a time interval, or any combination thereof, 
 determine, based on the detected time data, at least one ECG signal portion associated with the detected time data corresponding to the predetermined rhythm change in the at least one ECG signal, and 
 transmit the at least one determined ECG signal portion; and 
 
   a remote computer system in communication with the external heart monitoring device, the remote computer system configured to:
 receive the at least one determined ECG signal portion from the external heart monitoring device, and 
 analyze each respective determined ECG signal portion of the at least one determined ECG signal portion to classify a respective class for each respective determined ECG signal portion. 
   
     
     
         18 . The arrhythmia monitoring system of  claim 17 , wherein the at least one determined ECG signal portion comprises a plurality of determined ECG signal portions, and wherein the class for at least two respective determined ECG signal portions comprises a first class. 
     
     
         19 . The arrhythmia monitoring system of  claim 18 , wherein the remote computer system is further configured to transmit at least one message associated with the at least two respective determined ECG signal portions to a computing device associated with a technician, and wherein the computing device associated with the technician is configured to display a graphical user interface for batch review of the at least two respective determined ECG signal portions of the first class. 
     
     
         20 . The arrhythmia monitoring system of  claim 18 , wherein analyzing each respective determined ECG signal portion of the plurality of determined ECG signal portions to classify the respective class for each respective determined ECG signal portion comprises bucketing the plurality of determined ECG signal portions into a plurality of buckets, wherein the first class comprises a first bucket of the plurality of buckets, and wherein bucketing the plurality of determined ECG signal portions into the plurality of buckets comprises grouping the plurality of determined ECG signal portions based on at least one of an output of a neural network, a similarity of features of the plurality of determined ECG signal portions, a similarity of vector representations of the plurality of determined ECG signal portions, or any combination thereof.