Method of predicting acute cardiopulmonary events and survivability of a patient

• US 8,951,193 B2
• Filed: 03/06/2014
• Issued: 02/10/2015
• Est. Priority Date: 03/15/2010
• Status: Active Grant

• Current Assignee: Singapore Health Services Pte Limited (MOH Holdings Pte Limited), Nanyang Technology University
• Original Assignee: Singapore Health Services Pte Limited (MOH Holdings Pte Limited), Nanyang Technology University
• Inventors: Ong, Marcus Eng Hock, Lin, Zhiping, Ser, Wee, Huang, Guangbin
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/199,495
• Publication Number:
  US 20140257063A1
• Time in Patent Office: 341 Days
• Field of Search: None
• US Class Current: 600/301
• CPC Class Codes: A61B 34/10 Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/327 Generation of artificial ECG signals based on measured signals, e.g. to compensate for missing leads
• CPC Class Codes: A61B 5/346 Analysis of electrocardiograms
• CPC Class Codes: A61B 5/347 Detecting the frequency distribution of signals
• CPC Class Codes: A61B 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/361 Detecting fibrillation
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/4824 Touch or pain perception evaluation A61B5/4821 takes precedence
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/6801 specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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: A61B 5/742 : using visual displays A61B5/339 takes precedence
• CPC Class Codes: G06N 3/04 : Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16Z 99/00 : Subject matter not provided for in other main groups of this subclass

Abstract

A method of producing an artificial neural network capable of predicting the survivability of a patient, including: storing in an electronic database patient health data comprising a plurality of sets of data, each set having at least one of a first parameter relating to heart rate variability data and a second parameter relating to vital sign data, each set further having a third parameter relating to patient survivability; providing a network of nodes interconnected to form an artificial neural network, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight; and training the artificial neural network using the patient health data such that the associated weight of the at least one input of each artificial neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data.

Claims

1. A system for the detection of impending acute cardiopulmonary medical events that, left untreated, would with a reasonable likelihood result in either severe injury or death comprising:

an electro-cardiogram (ECG) module including a plurality of electrodes for sensing a patient'"'"'s ECG and having an ECG output;

a sensor for sensing a patient'"'"'s physiologic parameter other than ECG;

a first input for receiving the ECG output;

a second input for receiving signals from the sensor for sensing a patient'"'"'s physiologic parameter other than ECG;

a third input constructed and arranged to receive;

parametric information describing at least one element of a patient'"'"'s demographic information; and

parametric information describing a patient'"'"'s medical history;

a digitizing unit for digitizing the ECG and the physiologic signal other than ECG;

a housing containing a memory unit and processing unit, for storing and processing, respectively, the ECG, the physiologic signal other than ECG, patient demographic information and medical history; and

a user communication unit;

wherein the processing unit calculates at least one measure of heart rate variability (HRV), combines that at least one measure of HRV with at least one parameter each of patient demographic information and medical history, and calculates a statistical probability of the patient having an acute cardiopulmonary (ACP) event within the next 72 hours of the calculation.

2. The system of claim 1 constructed and arranged to be carried by the patient in a wearable configuration.

3. The system of claim 1 wherein the sensor measures the perfusion status of the microvasculature.

4. The system of claim 3 wherein the sensor is a pulse oximeter.

5. The system of claim 1 further comprising:

an electromagnetic stimulator of physiologic tissue.

6. The system of claim 5 wherein the electromagnetic stimulator stimulates cardiac tissue.

7. The system of claim 6 wherein the stimulation is pacing.

8. The system of claim 6 wherein the stimulation is defibrillation.

9. The system of claim 6 wherein the stimulation is magnetic stimulation.

10. The system of claim 1 wherein the user communication unit has key entry.

11. The system of claim 7 wherein the third input is a key entry.

12. The system of claim 1 wherein the user communication unit is in the main housing.

13. The system of claim 1 wherein the user communication unit is separate from main housing.

14. The system of claim 1 wherein the user communication unit is a display.

15. A system for predicting mortality of a patient being treated for trauma or as part of a mass casualty occurrence, comprising:

an electro-cardiogram (ECG) module including a plurality of electrodes for sensing a patient'"'"'s ECG and having an ECG output;

a sensor for sensing a patient'"'"'s physiologic parameter other than ECG;

a first input for receiving the ECG output;

a second input for receiving signals from the sensor for sensing a patient'"'"'s physiologic parameter other than ECG;

a third input constructed and arranged to receive;

parametric information describing at least one element of a patient'"'"'s demographic information; and

parametric information describing a patient'"'"'s medical history;

a digitizing unit for digitizing the ECG and the physiologic signal other than ECG;

a housing containing a memory unit and processing unit, for storing and processing, respectively, the ECG, the physiologic signal other than ECG, patient demographic information and medical history; and

a user communication unit;

wherein the processing unit calculates at least one measure of heart rate variability (HRV), combines that at least one measure of HRV with at least one parameter each of patient demographic information and medical history, and calculates a statistical probability of mortality of the patient within the next 72 hours of the calculation.

16. The system of claim 15 constructed and arranged to be carried by the patient in a wearable configuration.

17. The system of claim 15 wherein the sensor measures the perfusion status of the microvasculature.

18. The system of claim 17 wherein the sensor is a pulse oximeter.

19. Apparatus for predicting a likelihood of survival of a patient to one or more selected time limits due to cardiac causes, comprising:

a heart rate sensor having a heart rate output;

a vital sign sensor having a vital sign output;

a computational module receiving the heart rate output and the vital sign output, and performing;

computing heart rate variability (HRV) related measures from the heart rate output received; and

computing the likelihood of survival of the patient to the one or more selected time limits due to cardiac causes, from a combination of the HRV related measures computed and the vital sign output; and

,

an output device displaying to a user the likelihood of survival of the patient to the one or more selected time limits due to cardiac causes, wherein the one or more selected time limits is between four and seventy two hours.

20. The apparatus of claim 19, further comprising:

a data input device constructed and arranged to collect at least one of patient demographic information and patient history information; and

computing the likelihood of survival additionally in view of the collected patient demographic information and patient history information.

21. The apparatus of claim 19, wherein the one or more selected time limits is between four and twenty four hours.