System process for analyzing the medical condition of a user

• US 7,289,844 B2
• Filed: 09/10/2003
• Issued: 10/30/2007
• Est. Priority Date: 06/01/2001
• Status: Active Grant

• Current Assignee: Monebo Technologies, Inc.
• Original Assignee: Monebo Technologies, Inc.
• Inventors: Bukhman, Vladislav, Misczynski, Dale J., Sychov, Oleg, Tymoshok, Sergii, Tymoshok, Dmytro
• Primary Examiner(s): EVANISKO, GEORGE ROBERT
• Application Number:
  US10/659,483
• Publication Number:
  US 20050101873A1
• Time in Patent Office: 1,511 Days
• Field of Search: 600/508, 600/509, 600515-518, 128/903
• US Class Current: 600/515
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/366 Detecting abnormal QRS complex, e.g. widening
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: G08B 25/016 Personal emergency signalling and security systems emergency non-personal manually actuated alarm activators G08B25/12
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A device and a process for analyzing a medical condition of a user. This device and process can also be used to predict a future abnormal medical condition of the user. The device includes a portable information-receiving device, an information processing device and a remote storage and processing device. These three devices may be in communication with each other via a wireless communication system. This device can include a GPS system for locating the user when the user is having an abnormal medical condition. The process is designed to take a digital signal from a plurality of ECG sensors on the portable information device and form a QRS wave. One or more points are extracted from this QRS wave are used to form a QRS complex wave. QRS complex waves are used to analyze the medical condition of the user. A plurality of parameters are calculated from these points on the QRS wave. If the medical condition of the user is in an abnormal range then an alarm will sound. However, if the user is not in an abnormal range, then the device may also predict the possibility of a future occurrence of an abnormal medical condition in the user.

Claims

1. A process for extracting and analyzing cardiac parameters of a user comprising the following steps:

a) reading at least one signal of electrical activity of the heart from the user;

b) transforming said at least one signal into at least one digital signal;

c) extracting a plurality of cardiac parameters from said at least one digital signal; and

d) predicting a possibility of a future occurrence of significant cardiac events in the user using at least one of said plurality of cardiac parameters,

wherein said step of extracting the plurality of cardiac parameters comprises the following steps;

determining characteristic points where said digital signal reaches maximums, minimums or changes direction;

analyzing noise level in said digital signal;

determining a plurality of pulsometric parameters;

determining a plurality of QRS-complex parameters;

determining significant R-R intervals; and

averaging the plurality of pulsometric and QRS-complex parameters for a number of significant R-R intervals.

2. The process as in claim 1, wherein said step of determining characteristic points includes:

extracting at least two pairs of consecutive significant dominant characteristic points of maximum signal wherein said first pair is Ri−

1, Ri and said second pair is Ri, Ri+1;

determining at least two significant R-R intervals wherein said first R-R interval is Ri−

1Ri, and said second R-R interval is RiRi+1;

determining at least one QRS-fragment comprised of two consecutive significant R-R intervals;

determining at least one QRS-complex;

extracting dominant characteristic points P, Q, J, S, and T in said QRS-fragment; and

extracting auxiliary characteristic points I, K, P1, P2, T1, and T2 in said QRS-fragment.

3. The process as in claim 2 further comprises the step of:

extracting R point from said digital signal includes using the following formula;

the point R has been identified if;

wherein V is the amplitude at a current point along the QRS fragment;

V1 is the amplitude at (t−

d1)

V2 is the amplitude at point (t−

d2)

wherein t is the current time and d1, d2, A1, and A2 are empiric constants.

4. The process as in claim 2, wherein said step of extracting dominant characteristic point Q includes using the following formula:

the point Q has been identified if;

(d1/d2)≧

Qr and (Ar−

Ai)>

ArQ

wherein d1=Ai−

Ai−

3, d2=Ai+3−

Ai, and Qr, ArQ, and DQ are empiric constants.

5. The process as in claim 2, wherein said step of extracting dominant characteristic point S includes using the following formula:

the point S has been identified if;

Ai1>

A1 and (AR−

Ai)>

ARS wherein i=R, . . . , R+DS

or

(Ai−

Ai−

3)<

Ad and (AR−

Ai)>

ARQ, i=R, . . . , R−

DS

or

(d1/d2)≧

Sr and (Ar−

Ai)>

ARS

wherein Ai is the amplitude, d1=Ai−

Ai−

3, d2=Ai+3−

Ai, ARS, Ad, DS Sr, and ARQ are empiric constants.

6. The process as in claim 2, wherein said step of extracting dominant characteristic point J includes using the following formula:

the point J has been identified if;

(Ai−

3−

Ai)<

Ad i=S, . . . , S+Dj,

wherein Ai—

amplitude, Ad and Dj are empiric constants.

7. The process as in claim 2, wherein said step of extracting dominant characteristic point T includes using the following formula:

the point T has been identified if the distance from point (i, Ai) to line (I, IN)>

TAmin,

wherein (i, Ai)=maximum point and TAmin=AN for normal T-wave and TAmin=AIN for inverse T-wave;

for flat T-wave

(Ai−

Ai+5)>

Ad, i=j, . . . , N2,

wherein Ai=amplitude and AN, AIN, and Ad are empiric constants.

8. The process as in claim 2, wherein said step of extracting dominant characteristic point P includes using the following formula:

the point P has been identified if;

Ai−

Ai−

5>

Ad and Ai−

Ai5>

Ad;

wherein Ad is an empiric constant.

9. The process as in claim 2, wherein said step of extracting auxiliary characteristic point I includes using the following formula:

the point I has been identified if

(Ai−

3−

Ai)<

Ad and I=Q, . . . , Q−

Dr

wherein Ai=amplitude and Ad and DI are empiric constants; and

wherein the point I is considered equal to point Q if(Ai−

3−

Ai)≧

Ad.

10. The process as in claim 2, wherein said step of extracting auxiliary characteristic point K includes using the following formula:

the point K has been identified as (J+Dj, Aj+Dj)

wherein Aj+Dj is amplitude in point J+Dj and Dj is a preset constant.

11. The process as in claim 2, wherein said step of extracting auxiliary characteristic points T1, T2, and P2 includes using the following formula:

the point is identified if the angle formed by the local isoline and the line between Ai and the point becomes less than the previous angle contained by the local isoline and the line between Ai−

1 and the point, and this tendency continues within a 40 ms time period.

12. The process as in claim 2, wherein said step of extracting auxiliary characteristic point P1 includes using the following formula:

P1=P−

(P2−

P).

13. The process as in claim 1, wherein said step of analyzing noise level includes:

calculating the noise level N for a current R-R interval;

comparing the noise level with a threshold value; and

excluding said current R-R interval if said noise level exceeds said threshold value.

14. The process as in claim 13, wherein said step of calculation of the noise level N for current R-R interval includes using the following formula:

believing N=0, then;

for each given point j from interval [Ri−

2+e1, Ri−

e1];

if |(Vj−

Vj−

1)>

2 m and |(Vj−

Vj+1)>

2 m,

then N=N2m, m=5, . . . ,j[Ri−

2+e1, Ri−

e1]

for each given point j from interval [Ri−

2+e2, Ri−

e2];

if(Vj−

Vj−

1)>

2m and (Vj−

Vj+1)>

2m,

then N=N+2m, m=5 . . . , 2j[Ri−

2+e2, Ri−

e2]

wherein;

e1, e2—

indentations from threshold points (threshold point are empiric values equal 75 ms and 115 ms respectively);

Vj—

amplitude in point j;

N—

noise level value.

15. The process as in claim 1, wherein said step of determining the plurality of pulsometric parameters includes determining:

a heart rate;

a heart rate maximum;

a heart rate minimum;

a heart rate variability;

a number of single premature beats;

a number of groups of consecutive premature beats; and

an atrial/ventricular fibrillation/flutter.

16. The process as in claim 15, wherein said step of determining said atrial fibrillation flutter F includes using the following formulae:

F=(F1+F2)/X % wherein F1 is a premature beat component, F2 is a variability component and X is an empiric constant;

F1=(E/G)*100 wherein E is the number of premature beats within G number of previous R-R intervals;

wherein if F1>

50%, then F1 is considered equal 50%;

FRR=(RRmax−

RRmin)/RRmax*100;

wherein if m1>

2 then F2=S2/m1 wherein,

S1 is the sum of all of the variability of the G intervals;

or

wherein if m2>

2 then S2 is the sum of all of the variability of all G intervals;

wherein if m1≦

2 AND m2≦

then F2=0; and

wherein M1 is the number of R-R intervals with variability 10%<

FRR<

30% and m2 is the number of R-R intervals with variability FRR<

10%.

17. The process as in claim 1, wherein said step of determining a plurality of QRS-complex parameters includes calculation of:

a ST-segment depression/elevation;

a width of a Q-wave (WQ);

an amplitude of the Q-wave;

a width of QRS-complex;

a width of PQ interval;

a width of QT interval;

an amplitude of R wave;

T wave inversion;

Ratio of amplitude of Q wave to amplitude of R wave; and

Standard Deviation of the average Normal-to-Normal R-R interval.

18. A process for extracting and analyzing cardiac parameters of a user comprising the following steps:

a) reading at least one signal of electrical activity of the heart from the user;

b) transforming said at least one signal into at least one digital signal;

c) extracting a plurality of cardiac parameters from said at least one digital signal; and

d) predicting a possibility of a future occurrence of significant cardiac events in the user using at least one of said plurality of cardiac parameters,

wherein said step of predicting a possibility of a future occurrence of significant cardiac events in the user using at least one of said plurality of cardiac parameters includes using the following formula;

$\mathrm{RR}=1+\sqrt[-]{K_1*{<br><br>\frac{\mathrm{STmeas}-\mathrm{STinit}}{\mathrm{STinit}.+\mathrm{STthresh}.}<br><br>}^2+{<br><br>\frac{\mathrm{QTmeas}.}{\mathrm{QTnorm}.}-1<br><br>}^2+{<br><br>\frac{N_1+K_2*N_2+K_3*N_3}{\mathrm{HR}}<br><br>}^2}$

wherein;

RR is the complex Relative Risk of sudden cardiac death and development of myocardial infarction;

HR is the heart rate;

ST it.=the initial value of ST-segment depression/elevation;

ST meas.=measured value ST-segment depression/elevation;

ST thresh.=threshold value of ST-segment depression/elevation;

QT meas.=measured value of QT interval;

QT norm.=normal value of QT interval calculated using Bazett'"'"'s formula;

QTnorm=k*√

{square root over (60/HR)}

k is a constant coefficient of 0.4 for males and 0.37 for females;

N1 is the number of single ventricular premature beats per min;

N2 is the number of groups of ventricular premature beats per min;

N3 is the number of ventricular fibrillation/flutter episodes per min.

19. The process as in claim 18, further comprising the step of adjusting constants K1, K2 and K3 depending upon a set of clinical data obtained by predicting said abnormal medical condition, so that as more experiments and trials are performed, said constants may be modified to provide more accurate forecasting.

20. The process as in claim 19, wherein initial values of said constant K1 is approximately 1.49, K2 is approximately 34.91, and K3 is approximately 73.68.