Predicting acute cardiopulmonary events and survivability of a patient

• US 9,295,429 B2
• Filed: 12/12/2014
• Issued: 03/29/2016
• 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:
  US14/569,150
• Publication Number:
  US 20150223759A1
• Time in Patent Office: 473 Days
• Field of Search: None
• US Class Current: 1/1
• 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 predicting survivability of a patient. The method includes storing in an electronic database patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data including at least one of ST segment elevation and depression, a second parameter relating to vital sign data, and 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 neurons, each having at least one input with an associated weight; and training the neural network using the patient health data such that the associated weight of the at least one input of each neuron is adjusted in response to respective first, second and third parameters of different sets of data from the patient health data, such that the neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.

Claims

1. A method of producing an artificial neural network capable of predicting the survivability of a patient, the method comprising:

storing in an electronic database patient health data, the patient health data comprising a plurality of sets of data, each set having a first parameter relating to heart rate variability data, a second parameter relating to vital sign data, a third parameter relating to patient survivability, and a fourth parameter including at least one of ST segment elevation and depression;

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 of the plurality of artificial neurons is adjusted in response to respective first, second, third, and fourth parameters of different sets of data from the patient health data, such that the artificial neural network is trained to produce a prediction on the survivability of a patient within the next 72 hours.

2. The method of claim 1, further comprising classifying the first parameter, the second parameter or a combination of the first parameter and the second parameter as feature vectors of the patient health data and training the artificial neural network with the feature vectors.

3. The method of claim 1, wherein the artificial neural network is implemented as instructions stored in a memory that when executed by a processor cause the processor to perform the functions of the artificial neural network.

4. The method of claim 3, wherein the artificial neural network is based on support vector machine architecture, wherein the associated weight of the at least one input of each artificial neuron of the plurality of artificial neurons is initialized from a library used by the support vector machine.

5. The method of claim 3, wherein the artificial neural network is based on an extreme learning machine architecture, wherein the associated weight of the at least one input of each artificial neuron of the plurality of artificial neurons is initialized through random selection by the extreme learning machine.

6. The method of claim 1, wherein each set of the plurality of sets of data further comprises a fifth parameter relating to patient characteristics.

7. The method of claim 6, wherein the patient characteristics comprises any one or more of the following:

age, gender and medical history.

8. The method of claim 1, wherein the plurality of sets of data further comprises a fifth parameter relating to at least one of a presence of ectopic beats occurring at regular intervals and changes in ectopic beats over time.

9. The method of claim 1, wherein the heart rate variability data comprises time domain data, frequency domain data and geometric domain data.

10. The method of claim 1, wherein the vital sign data comprises any one or more of the following:

systolic blood pressure, diastolic blood pressure, pulse rate, pulse oximetry, respiratory rate, glasgow coma scale (GCS), pain score, temperature and age.

11. The method of claim 1, wherein the patient health data used to train the artificial neural network are the at least one of ST segment elevation and depression, standard deviation of the instantaneous heart rate (STD_HR), power in a low frequency range (0.04 to 0.15 Hz) in normalized units (LFnorm), age, pulse rate, pulse oximetry, systolic blood pressure and diastolic blood pressure.

12. The method of claim 1, wherein the prediction on the survivability of the patient within the next 72 hours is either death or survival of the patient.

13. A method of predicting the survivability of a patient, the method comprising:

measuring a first set of parameters relating to heart rate variability data of a patient;

measuring a second set of parameters relating to vital sign data of the patient;

measuring a third set of parameters relating to at least one ECG feature of the patient, the at least one ECG feature including at least one of ST segment elevation and depression;

providing an artificial neural network comprising a network of interconnected nodes, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight adjusted by training the artificial neural network using an electronic database having a plurality of sets of data, each set having a parameter relating to heart rate variability data, a parameter relating to vital sign data, a parameter relating to patient survivability, and a parameter relating to the at least one ECG feature;

processing the first set of parameters, the second set of parameters, and the third set of parameters to produce processed data suitable for input into the artificial neural network;

providing the processed data as input into the artificial neural network; and

obtaining an output from the artificial neural network, the output providing a prediction on the survivability of the patient within the next 72 hours.

14. The method of claim 13, wherein the processed data of the first set of parameters and the processed data of the second set of parameters are represented as a feature vector.

15. The method of claim 13, wherein the processed data is the first set of parameters and the second set of parameters being represented as normalized data.

16. The method of claim 13, wherein:

the processed data is partitioned into non-overlapping segments, so that the input into the artificial neural network comprises sets of one or more of the non-overlapping segments of processed data; and

wherein a result is obtained for each of the sets of one or more of the non-overlapping segments of processed data, so that each of the results is considered to predict the survivability of the patient within the next 72 hours.

17. The method of claim 13, wherein the result of the artificial neural network is coded as a two class label, so that the method further comprises:

applying a label-based algorithm to each of the two class label results to decide the output from the artificial neural network, thereby providing a prediction on the survivability of the patient within the next 72 hours.

18. A patient survivability prediction system comprising:

a first input to receive a first set of parameters relating to heart rate variability data of a patient and at least one ECG feature of the patient including at least one of ST segment elevation and depression;

a second input to receive a second set of parameters relating to vital sign data of the patient;

a memory module storing instructions to implement an artificial neural network comprising a network of interconnected nodes, the nodes comprising a plurality of artificial neurons, each artificial neuron having at least one input with an associated weight adjusted by training the artificial neural network using an electronic database having a plurality of sets of data, each set having a parameter relating to heart rate variability data, a parameter relating to vital sign data, a parameter relating to patient survivability, and a parameter relating to the at least one ECG feature;

a processor to execute the instructions stored in the memory module to perform the functions of the artificial neural network and output a prediction on the survivability of the patient within the next 72 hours based on the first set of parameters, the second set of parameters, and the at least one ECG feature; and

a display for displaying the prediction on the survivability of the patient within the next 72 hours.

19. The patient survivability prediction system of claim 18, further comprising a port to receive the first set of parameters from the first input and the second set of parameters from the second input.

20. The patient survivability prediction system of claim 18, further comprising:

a first port to receive the first set of parameters from the first input; and

a second port to receive the second set of parameters from the second input.