Method of predicting acute cardiopulmonary events and survivability of a patient

• US 8,932,220 B2
• Filed: 03/06/2014
• Issued: 01/13/2015
• 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:
  US14/199,479
• Publication Number:
  US 20140187988A1
• Time in Patent Office: 313 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 5/316 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 method of treating a cardiac condition of a patient, comprising:

measuring heart rate variability (HRV) of the patient;

measuring vital sign data of the patient;

predicting, using a computing apparatus constructed and arranged for the purpose, a likelihood of survival of the patient to one or more selected time limits based on HRV in combination with the measured vital sign data, the one or more selected time limits being between four and seventy two hours; and

treating the cardiac condition as indicated by the vital sign data when the likelihood of survival of the patient to the one or more selected time limits is below a desired threshold.

2. The method of claim 1, further comprising:

collecting at least one of patient demographic information and patient history information;

wherein predicting the likelihood of survival further includes computing the likelihood of survival additionally in view of the at least one of the collected patient demographic information and the collected patient history information.

3. The method of claim 1, further comprising:

collecting patient demographic information and patient history information;

wherein predicting the likelihood of survival further includes computing the likelihood of survival additionally in view of the collected patient demographic information and the collected patient history information.

4. The method of claim 3, further comprising:

selecting a time limit of between 4 and 24 hours as the one or more selected time limits.