HEART FAILURE EVENT PREDICTION USING CLASSIFIER FUSION

• US 20150157273A1
• Filed: 11/13/2014
• Published: 06/11/2015
• Est. Priority Date: 12/06/2013
• Status: Abandoned Application

• Current Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Original Assignee: Cardiac Pacemakers Incorporated (Boston Scientific Corporation)
• Inventors: An, Qi, Thakur, Pramodsingh Hirasingh, Zhang, Yi, Hotson, Guy R.
• Application Number:
  US14/540,834
• Publication Number:
  US 20150157273A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• 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 5/02028 Determining haemodynamic parameters not otherwise provided for, e.g. cardiac contractility or left ventricular ejection fraction
• 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 5/0215 by means inserted into the body catheters A61M25/00
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/053 Measuring electrical impedance or conductance of a portion of the body
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0826 Detecting or evaluating apnoea events A61B5/4818 takes precedence
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/14546 for measuring analytes not otherwise provided for, e.g. ions, cytochromes
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• 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 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 7/00 : Instruments for auscultation
• CPC Class Codes: A61B 7/04 : Electric stethoscopes microphones, acoustic transducers therefor H04R
• CPC Class Codes: A61N 1/36521 : the parameter being derived from measurement of an electrical impedance
• CPC Class Codes: A61N 1/3655 : controlled by body or blood temperature
• CPC Class Codes: A61N 1/36557 : controlled by chemical substances in blood
• CPC Class Codes: A61N 1/36564 : controlled by blood pressure
• CPC Class Codes: A61N 1/36571 : controlled by blood flow rate, e.g. blood velocity or cardiac output
• CPC Class Codes: A61N 1/36585 : controlled by two or more physical parameters
• CPC Class Codes: A61N 1/36592 : controlled by the heart rate variability
• CPC Class Codes: G16H 40/63 : for local 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

Systems and methods for detecting a heart failure (HF) event indicative of worsening of HF, or for identifying patient at elevated risk of developing future HF event, are described. The system and methods can detect an HF event or predict HF risk using a multitude of fusion algorithms or classifiers, each employing one or more physiologic sensor signals. A system can comprise two or more partial predictor circuits each can adaptively generate a dynamic computational model (DCM). Each partial predictor circuit can determine a partial risk index indicating a likelihood of the patient developing a precursor physiologic event indicative or correlative of a future HF event. The system can include a prediction fusion circuit that can combine the partial risk indices and generate a composite risk indicator for detecting or predicting a likelihood of the patient developing a future HF event.

Claims

1. A system, comprising:

a physiologic signal receiver circuit configured to receive at least one physiologic signal obtained from a patient;

two or more partial predictor circuits, each including;

a feature generator circuit configured to generate one or more candidate signal features from the at least one physiologic signal;

a dynamic computational model circuit configured to adaptively generate a dynamic computational model; and

a partial risk calculator circuit configured to calculate a partial risk index using the one or more candidate signal features and the dynamic computational model, the partial risk index indicating a likelihood of the patient developing a precursor physiologic event indicative or correlative of a future target physiologic event; and

a prediction fusion circuit coupled to the two or more partial predictor circuits, the prediction fusion circuit configured to generate a composite risk indicator using the partial risk indices produced by the two or more partial predictor circuits, the composite risk indicator indicative of a likelihood of the patient developing the future target physiologic event.

2. The system of claim 1, wherein the two or more partial predictor circuits differ from each other by at least one of the one or more candidate signal features or the dynamic computational model.

3. The system of claim 1, wherein the physiologic signal receiver circuit is configured to receive one or more physiologic signals including a thoracic impedance signal, a heart sound (HS) signal, respiration signal, a posture signal, an activity signal, a heart rate signal, or a physiologic response to activity (PRA) signal.

4. The system of claim 1, wherein the two or more partial predictor circuits include first and second partial predictor circuits, the first partial predictor circuit including a first partial risk calculator circuit configured to calculate a first partial risk index indicating a likelihood of the patient developing a first type of precursor physiologic event, the second partial predictor circuit including a second partial risk calculator circuit configured to calculate a second partial risk index indicating likelihood of the patient developing a second type of precursor physiologic event different from the first type of precursor physiologic event.

5. The system of claim 4, wherein the first partial risk calculator circuit is configured to calculate the first partial risk index indicating a likelihood of the patient developing a pulmonary event, and the second partial risk calculator circuit is configured to calculate the second partial risk index indicating a likelihood of the patient developing a cardiac event.

6. The system of claim 4, wherein the first partial risk calculator circuit is configured to calculate the first partial risk index indicating a likelihood of the patient developing a peripheral congestion, and the second partial risk calculator circuit is configured to calculate the second partial risk index indicating a likelihood of the patient developing a central congestion.

7. The system of claim 1, wherein the two or more partial predictor circuits include first and second partial predictor circuits, the first partial predictor circuit including a first dynamic computational model circuit configured to adaptively generate a first dynamic computation model using a first data portion, the second partial predictor circuit including a second dynamic computational model circuit configured to adaptively generate a second dynamic computation model using a second data portion, the first and second data portions respectively selected from one or more physiologic signals.

8. The system of claim 7, wherein the physiologic signal receiver circuit is configured to receive patient historical physiologic data, and wherein the first and second partial predictor circuits are configured to select the respective first and second data portions from the patient historical physiologic data.

9. The system of claim 7, wherein the first data portion is non-identical to the second data portion.

10. The system of claim 1, wherein the dynamic computational model circuit is configured to adaptively generate the dynamic computational model including one or a combination of two or more of a rule-based model, a decision tree, a regression model, a neural network model, a random forest, a voting model, a fuzzy logic model, or a support vector machine model.

11. The system of claim 1, wherein the dynamic computational model circuit is configured to initialize the dynamic computational model to a randomly-selected structure.

12. The system of claim 1, wherein:

the two or more partial predictor circuits each is configured to calculate a partial risk index including a categorical decision indicating occurrence of the physiologic event; and

the prediction fusion circuit is configured to generate the composite risk indicator using voting among the categorical decisions.

13. The system of claim 1, wherein:

the two or more partial predictor circuits each is configured to calculate a partial risk index including a probability value indicating a likelihood of the physiologic event; and

the prediction fusion circuit is configured to generate the composite risk indicator using a linear or a non-linear combination of the probability values.

14. A system, comprising:

a dynamic computational model unit, including;

a memory circuit configured to receive and store physiologic data; and

a model update circuit adaptively generate two or more dynamic computational models using the stored physiologic data; and

an ambulatory medical device communicatively coupled to the dynamic computational model unit, the ambulatory medical device including;

a receiver circuit configured to receive from the dynamic computational model unit the two or more dynamic computational models;

a physiologic signal receiver circuit configured to receive at least one physiologic signal obtained from a patient;

two or more partial predictor circuits configured to generate one or more candidate signal features from the at least one physiologic signal, and to calculate a partial risk index using the one or more candidate signal features and the two or more dynamic computational models, the partial risk index indicating a likelihood of the patient developing a precursor physiologic event indicative or correlative of a future target physiologic event; and

a prediction fusion circuit coupled to the two or more partial predictor circuits, the prediction fusion circuit configured to generate a composite risk indicator using the partial risk indices produced by two or more partial predictor circuits, the composite risk indicator indicative of a likelihood of the patient developing the future target physiologic event.

15. The system of claim 14, wherein the dynamic computational model unit is configured to adaptively generate the two or more dynamic computational models including one or a combination of two or more of a rule-based model, a decision tree, a regression model, a neural network model, a random forest, a voting model, a fuzzy logic model, or a support vector machine model.

16. The system of claim 14, wherein the two or more partial predictor circuits include a first partial predictor circuit and a second partial predictor circuit different from the first partial predictor circuit by at least one of the one or more candidate signal features or the dynamic computational model.

17. A method, comprising:

adaptively generating at least first and second dynamic computational models;

receiving at least one physiologic signal obtained from a patient;

generating one or more candidate signal features using the at least one physiologic signal;

calculating a first partial risk index using first signal features and the first dynamic computational model and calculating a second partial risk index using second signal features and the second dynamic computational model, the first and second signal features respectively selected from the one or more candidate signal features, the first partial risk index indicating a likelihood of the patient developing a first precursor physiologic event, the second partial risk index indicating a likelihood of the patient developing a second precursor physiologic event, the first and second precursor physiologic events indicative or correlative of a future target physiologic event;

generating a composite risk indicator using one or both of the first and second partial risk indices, the composite risk indicator indicative of a likelihood of the patient developing the future target physiologic event.

18. The method of claim 17, wherein adaptively generating the at least first and second dynamic computational models includes generating the first dynamic computational model having different type or different structure than the second dynamic computational model, the types of the models including a rule-based model, a decision tree, a regression model, a neural network model, a random forest, a voting model, a fuzzy logic model, or a support vector machine model.

19. The method of claim 17, wherein calculating the first partial risk index includes calculating a risk index indicating a likelihood of the patient developing a pulmonary event, and wherein calculating the second partial risk index includes calculating a risk index indicating a likelihood of the patient developing a cardiac event.

20. The method of claim 17, wherein generating the composite risk indicator includes taking a linear or nonlinear combination of the first and second partial risk indices.