System and method for predicting acute cardiopulmonary events and survivability of a patient

• US 9,420,957 B2
• Filed: 02/06/2015
• Issued: 08/23/2016
• Est. Priority Date: 03/15/2010
• Status: Active Grant

• Current Assignee: Singapore Health Services Pte Limited (MOH Holdings Pte Limited), Nanyang Technological University
• Original Assignee: Singapore Health Services Pte Limited (MOH Holdings Pte Limited), Nanyang Technological University
• Inventors: Ong, Marcus Eng Hock, Lin, Zhiping, Ser, Wee, Huang, Guangbin
• Primary Examiner(s): Gedeon, Brian T
• Application Number:
  US14/616,057
• Publication Number:
  US 20150150468A1
• Time in Patent Office: 564 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 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• 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 5/352 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 patient survivability prediction system comprising:

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

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

a memory module to store the first set of parameters and the second set of parameters;

at least one processor coupled to the memory module and configured to analyze the first set of parameters and the second set of parameters and output a prediction on the survivability of the patient over the next 72 hours based on the first set of parameters and the second set of parameters; and

a display coupled to the at least one processor to display the prediction on the survivability of the patient over the next 72 hours.

2. The patient survivability prediction system of claim 1, wherein the patient survivability system further includes a third input to receive a third set of parameters including at least one of demographic information of the patient and medical history information of the patient, wherein the at least one processor is further configured to analyze the first set of parameters, the second set of parameters, and the third set of parameters, and output the prediction on the survivability of the patient over the next 72 hours based on the first set of parameters, the second set of parameters, and the third set of parameters.

3. The patient survivability prediction system of claim 1, wherein the patient survivability system further includes a third input to receive a third set of parameters including demographic information of the patient and medical history information of the patient, wherein the at least one processor is further configured to analyze the first set of parameters, the second set of parameters, and the third set of parameters, and output the prediction on the survivability of the patient over the next 72 hours based on the first set of parameters, the second set of parameters, and the third set of parameters.

4. The patient survivability prediction system of claim 3, wherein the demographic information includes at least one of an age and a gender of the patient.

5. The patient survivability prediction system of claim 4, wherein the vital sign data includes at least one of a blood oxygen saturation level of the patient, a temperature of the patient, a respiration rate of the patient, a pulse rate of the patient, a systolic blood pressure of the patient, a diastolic blood pressure of the patient, a Glasgow Coma Scale score of the patient, and a pain score of the patient.

6. The patient survivability prediction system of claim 5, wherein the heart rate variability data includes at least one of time domain data, frequency domain data, and geometric domain data.

7. The patient survivability prediction system of claim 1, wherein the vital sign data includes at least one of a blood oxygen saturation level of the patient, a temperature of the patient, a respiration rate of the patient, a pulse rate of the patient, a systolic blood pressure of the patient, a diastolic blood pressure of the patient, a Glasgow Coma Scale score of the patient, and a pain score of the patient.

8. The patient survivability prediction system of claim 1, wherein the heart rate variability data includes at least one of time domain data, frequency domain data, and geometric domain data.

9. The patient survivability prediction system of claim 1, wherein the heart rate variability data includes time domain data, frequency domain data, and geometric domain data.

10. The patient survivability prediction system of claim 1, wherein the patient survivability prediction system is constructed and arranged to be carried by the patient in a wearable configuration.

11. The patient survivability prediction system of claim 1, wherein the prediction on the survivability of the patient over the next 72 hours is either death or survival of the patient.

12. A patient survivability prediction system comprising:

a signal acquisition module having a first input to receive electro-cardiogram (ECG) signals from a patient and a second input to receive at least one physiological signal from the patient other than the ECG signals;

a signal processing module, coupled to the signal acquisition module, to receive the ECG signals and determine heart rate variability data of the patient based on the ECG signals;

a vital sign module, coupled to signal acquisition module, to receive the at least one physiological signal and determine vital sign data of the patient based on the at least one physiological signal; and

an analysis module coupled to the signal processing module and the vital sign module configured to analyze the heart rate variability data and the vital signal data and output a prediction on the survivability of the patient over the next 72 hours based on the heart rate variability data and the vital sign data.

13. The patient survivability prediction system of claim 12, further comprising a patient information module, coupled to the analysis module, to receive at least one of demographic information of the patient and medical history information of the patient, wherein the analysis module is further configure to analyze the heart rate variability data, the vital signal data and the at least one of the demographic information and the medical history information and output the prediction on the survivability of the patient over the next 72 hours based on the heart rate variability data, the vital sign data, and the at least one of the demographic information and the medical history information.

14. The patient survivability prediction system of claim 13, wherein the at least one of the demographic information and the medical history information includes both the demographic information and the medical history information, and wherein the demographic information includes at least one of an age, a gender, and an ethnicity of the patient, and the medical history information includes at least one of a Glascow Coma Scale score, a history of any medical conditions including diabetes, high blood pressure, and myocardial infarction, and any specific medications the patient is using.

15. The patient survivability prediction system of claim 13, wherein the signal processing module and the analysis module are implemented in a digital signal processor, and wherein the patient information module is implemented in a general purpose processor.

16. The patient survivability prediction system of claim 12, further comprising at least one of a temperature sensor, a respiration rate sensor, a pulse rate sensor, a blood pressure sensor, and a blood oxygen saturation sensor to provide the at least one physiological signal to the signal acquisition module.

17. The patient survivability prediction system of claim 12, wherein the heart rate variability data includes at least one of time domain data, frequency domain data, and geometric domain data.

18. The patient survivability prediction system of claim 12, wherein the vital sign data includes at least one of a blood oxygen saturation level of the patient, a temperature of the patient, a respiration rate of the patient, a pulse rate of the patient, a systolic blood pressure of the patient, a diastolic blood pressure of the patient, a Glasgow Coma Scale score of the patient, and a pain score of the patient.

19. The patient survivability prediction system of claim 12, wherein the heart rate variability data includes time domain data, frequency domain data, and geometric domain data.

20. The patient survivability prediction system of claim 12, wherein the patient survivability prediction system is constructed and arranged to be carried by the patient in a wearable configuration.