Sensor apparatuses, methods of operating same, and systems including same, and methods and systems for sensing and analyzing electromechanical characteristics of a heart

Abstract

Sensor apparatuses, methods of operating the sensor apparatuses, and systems including the sensor apparatuses are disclosed. Methods of analyzing electromechanical characteristics of a heart are also disclosed.

We claim: 
     
         1 . A method of analyzing electromechanical characteristics of a heart of a subject, the method comprising producing at least one inference, wherein producing the at least one inference comprises analyzing, in at least one model, at least:
 a time series of electrocardiogram (ECG) measurements of the heart and measured during a first period of time after stent-placement angioplasty of the subject; and   a time series of measurements of movement caused by the heart during a second period of time after the stent-placement angioplasty of the subject and at least overlapping with the first period of time.   
     
     
         2 . The method of  claim 1  wherein at least one of the at least one model is associated with at least one cardiac disease. 
     
     
         3 . The method of  claim 1  wherein the measurements of movement during the second period of time comprise measurements of:
 rotational movement caused by the heart around at least one axis of rotation; and 
 measurements of linear movement caused by the heart in at least one linear direction. 
 
     
     
         4 . The method of  claim 3  wherein:
 the at least one axis of rotation comprises a lateral-medial axis of rotation relative to the subject and a superior-inferior axis of rotation relative to the subject; and 
 the at least one linear direction comprises a front-to-back direction relative to the subject. 
 
     
     
         5 . The method of  claim 1  wherein the first and second periods of time are post-exercise. 
     
     
         6 . The method of  claim 1  wherein:
 the first and second periods of time are before exercise; and 
 producing the at least one inference further comprises analyzing, in the at least one model, at least: 
 a time series of ECG measurements of the heart and measured during a third period of time after the first and second periods of time and after the exercise; and 
 a time series of measurements of movement caused by the heart during a fourth period of time at least overlapping with the third period of time, after the first and second periods of time, and after the exercise.  7  The method of  claim 6  wherein: 
 the measurements of movement during the second period of time comprise measurements of rotational movement caused by the heart around at least one axis of rotation and measurements of linear movement caused by the heart in at least one linear direction; and 
 the measurements of movement during the fourth period of time comprise measurements of rotational movement caused by the heart around the at least one axis of rotation and measurements of linear movement caused by the heart in the at least one linear direction. 
 
     
     
         8 . The method of  claim 1  wherein producing the at least one inference comprises feature extraction of at least one time segment of, at least, the time series of measurements of movement. 
     
     
         9 . The method of  claim 8  wherein the at least one time segment comprises a high-energy diastole time segment defined as between:
 a start of diastolic vibration identified in the time series of measurements of movement; and 
 an immediately subsequent end of diastolic vibration identified in the time series of measurements of movement. 
 
     
     
         10 . The method of  claim 9  wherein the at least one time segment further comprises a low-energy diastole time segment defined as between:
 an end of diastolic vibration identified in the time series of measurements of movement; and 
 an immediately subsequent “Q” wave of the time series of ECG measurements. 
 
     
     
         11 . The method of  claim 8  wherein the at least one time segment comprises a low-energy diastole time segment defined as between:
 an end of diastolic vibration identified in the time series of measurements of movement; and 
 an immediately subsequent “Q” wave of the time series of ECG measurements. 
 
     
     
         12 . The method of  claim 8  wherein the at least one time segment comprises a low-energy systole time segment defined as between:
 a “Q” wave of the time series of ECG measurements; and 
 an immediately subsequent start of systolic vibration identified in the time series of measurements of movement. 
 
     
     
         13 . The method of  claim 8  wherein the at least one time segment comprises a low-energy systole time segment defined as between:
 an end of systolic vibration identified in the time series of measurements of movement; and 
 an immediately subsequent start of diastolic vibration identified in the time series of measurements of movement. 
 
     
     
         14 . The method of  claim 8  wherein the at least one time segment comprises a high-energy systole time segment defined as between:
 a start of systolic vibration identified in the time series of measurements of movement; and 
 an immediately subsequent end of systolic vibration identified in the time series of measurements of movement. 
 
     
     
         15 . The method of  claim 8  wherein the at least one time segment comprises:
 a systole time segment defined as between:
 a “Q” wave of the time series of ECG measurements; and 
 an immediately subsequent start of diastolic vibration identified in the time series of measurements of movement; and 
 
 a diastole time segment defined as between:
 a start of diastolic vibration identified in the time series of measurements of movement; and 
 an immediately subsequent “Q” wave of the time series of ECG measurements. 
 
 
     
     
         16 . The method of  claim 8  wherein:
 the feature extraction comprises extraction of at least one feature of morphology of the measurements of movement; and 
 producing the at least one inference comprises producing the at least one inference according to, at least, the at least one feature of morphology. 
 
     
     
         17 . The method of  claim 8  wherein:
 the feature extraction comprises extraction of at least one feature of frequency of the measurements of movement; and 
 producing the at least one inference comprises producing the at least one inference according to, at least, the at least one feature of frequency. 
 
     
     
         18 . The method of  claim 8  wherein:
 the feature extraction comprises extraction of at least one cross feature associated with different segments of different types of measurements of vibration; and 
 producing the at least one inference comprises producing the at least one inference according to, at least, the at least one cross feature. 
 
     
     
         19 . The method of  claim 1  wherein analyzing in the at least one model comprises analyzing one or more peak twist/untwist velocity intervals, each defined as a time interval between a time of maximum twist velocity during systole and a time of maximum twist velocity during diastole. 
     
     
         20 . The method of  claim 1  wherein analyzing in the at least one model comprises analyzing rotational twist/untwist velocity.