Personal hand-held monitor

Abstract

The present application describes a PHHM of the type described in WO 2013/002165, WO 2014/125431 and International Patent Application No. PCT/EP2015/079888 with improved aspects to find indicators of health, and other improvements that facilitate its construction and calibration.

1 . A personal hand-held monitor (PHHM) configured to derive one or more measurements of a parameter related to health of a subject from data collected by the PHHM, the PHHM comprising:
 a signal acquisition device (SAD) which includes an optical sensor and a pressure sensor; and   a processor;   wherein the processor is adapted to determine a local estimate of arterial stiffness derived from an Arterial Optical/Pressure Curve (AOPC) found from a photoplethysmography (PPG) signal acquired from the optical sensor and a pressure signal acquired from the pressure sensor.   
     
     
         2 . The PHHM of  claim 1  wherein the processor is adapted to determine the timing of cardiac functions by processing signals caused by vibrations caused by the movements of the heart. 
     
     
         3 . The PHHM of  claim 2 , wherein the processor is adapted to find the opening and closing times of the mitral (MO, MC) and aortic valves (AO, AC). 
     
     
         4 . The PHHM of  claim 3 , where the processor is adapted to use the opening and closing times to estimate any one of the following systolic diagnostic parameters:
 Left ventricular ejection time LVET=AC−AO;   Electromechanical systole QS2=AC-ECG QRS peak or QS2=PEP+LVET, where PEP is the pre-ejection period; and   Iso-volumetric contraction time IVCT=AO−MC;   
     
     
         5 . The PHHM of  claim 4 , wherein the processor is adapted to determine a Contractility Coefficient CC=PEP/LVET. 
     
     
         6 . The PHHM of  claim 2 , wherein the processor is adapted to find the timing of:
 Rapid Ventricular Ejection; and   Rapid Ventricular Filling.   
     
     
         7 . The PHHM of  claim 2 , wherein the processor is adapted to determine at least one of:
 Left Ventricular Filling Time LVFT=period from mitral valve opening−mitral valve closure;   Isovolumetric Relaxation Time IVRT=AC−MO;   Rapid Ventricular Filling Time RVFT=MO−RF; and   Myocardial Performance Index MPI=(IVCT+IVRT)/LVET.   
     
     
         8 . A personal hand-held monitor (PHHM) configured to derive one or more measurements of a parameter related to health of a subject from data collected by the PHHM, wherein the parameter comprises measurements of blood pressure at systole (SBP) and blood pressure at diastole (DBP), the PHHM comprising:
 a signal acquisition device (SAD) which includes a pressure sensor; and   a processor;   wherein the SAD is adapted to be pressed against a finger to make a pressure measurement, or have a finger pressed against it to make a pressure measurement, and wherein the processor of the PHHM is adapted to make one or more estimate(s) of the degree of rotation of the finger and/or the tilt of the finger based on the pressure measurement and to adjust the measurements of SBP and DBP in light of one or both of these estimates of the rotation and/or the tilt.   
     
     
         9 . The PHHM of  claim 8 , wherein the processor is adapted to establish empirical relationships between one or more estimate of rotation, one or more estimate of tilt and the differences between the best estimates of SBP and DBP, and the true arterial SBP and DBP. 
     
     
         10 . The PHHM of  claim 9 , wherein the best estimates of SDP and DBP are found by combining a plurality of estimates of SBP and DBP made using different techniques and wherein the combination of the plurality of estimates is found by calculating a weighted average of the separate estimates, where the weights are determined by:
 empirical analysis of a theoretical contribution that each estimate can make to the result; and   an indicated precision of each estimate.   
     
     
         11 . The PHHM of  claim 10 , wherein the processor of the PHHM is adapted to use these empirical relationships to correct the estimates of SBP and DBP to give more accurate estimates of arterial SBP and DBP. 
     
     
         12 . The PHHM of  claim 10 , wherein the processor is adapted to derive the empirical relationships using machine learning to find the optimum corrections from a large body of measured results. 
     
     
         13 . A personal hand-held monitor (PHHM) comprising a processor adapted to improve the accuracy of an estimate of blood pressure by combining a plurality of estimates of the blood pressure made using different techniques, and wherein the combination of the plurality of estimates of the blood pressure is found by calculating a weighted average of the separate estimates of the blood pressure, where the weights are determined by:
 empirical analysis of a theoretical contribution that each estimate of the plurality of estimates can make to the result; and   an indicated precision of each estimate within the plurality of estimates.   
     
     
         14 . The PHHM of  claim 13  further comprising a signal acquisition device (SAD) which includes an optical sensor that produces an optical signal and a pressure sensor that produces a pressure signal, wherein the estimates are obtained using at least one of:
 the times of diastole (TD) and systole (TS); 
 a measured pressure at TD (PD) and TS (PS); 
 a measured photoplethysmography (PPG) signal at TD (PPD) and TS (PPS); 
 PM=(PD+PS)/2; 
 a high frequency component of the optical signal in the region before TD (ACD); 
 a measure of a curvature of a plot of the optical signal in the region before TD (SHAPED); 
 a measure of a curvature of a plot of the optical signal in the region after TS (SHAPES); 
 a fraction of the values of the optical signal that lies above a straight line constructed from a half height of a falling edge of one PPG trough to the half height of the falling edge of the next PPG trough. 
 
     
     
         15 . The PHHM of  claim 13 , wherein the BP estimate is at least one of an estimate of a blood pressure at systole (SBP) or a blood pressure at diastole (DBP), wherein the weighted mean takes the form:
   SBP=Σ m=1 to n (SBP m*Wm*Qm )/Σ m=1 to n ( Wm*Qm )
     and     DBP=Σ m=1 to n (DBP m*Ym*Pm )/Σ m=1 to n ( Ym*Pm )
   where:   SBPm and DBPm are the m'th estimates of blood pressure at systole and diastole respectively;   Wm and Ym are the weights attributed to each respective m'th estimate, representing the theoretical contribution of the estimate; and   Qm and Pm are a measure of the precision of each respective m'th estimate.   
     
     
         16 . The PHHM of  claim 13 , wherein the processor is adapted to combine estimates of SBP and DBP derived from two or more analyses that have errors that are not closely correlated. 
     
     
         17 . The PHHM of  claim 13 , wherein an optimal weighting is found by machine learning from a large body of measured results. 
     
     
         18 . The PHHM of  claim 13 , wherein the processor of the PHHM is adapted to find a quality of the resulting weighted mean by calculating a discord between the separate estimates of the plurality of estimates, wherein the discord is defined as a standard deviation of the separate estimates of SBP and DBP. 
     
     
         19 . A personal hand-held monitor (PHHM) comprising a processor;
 an electrocardiogram (ECG) sensor; and   an accelerometer;   wherein the processor of the PHHM is adapted to detect a signal representative of a mechanical response of the heart to natural electrical signals which trigger the beating of the heart by holding the PHHM against the chest and processing signals from the ECG sensors and the accelerometer.   
     
     
         20 . The PHHM of  claim 19 , wherein the processor is adapted to collect data over several beats.