System and method for diagnosing sleep apnea based on results of multiple approaches to sleep apnea identification

• US 8,758,243 B2
• Filed: 02/02/2011
• Issued: 06/24/2014
• Est. Priority Date: 02/02/2010
• Status: Active Grant

• Current Assignee: Covidien LP (Medtronic Plc)
• Original Assignee: Covidien LP (Medtronic Plc)
• Inventors: Wang, Hui, Amundson, Scott, Boyce, Robin, Ochs, James, Li, Li, Madere, Tonia, Vargas, Steven
• Primary Examiner(s): Jackson, Gary
• Assistant Examiner(s): Archer, Marie
• Application Number:
  US13/019,882
• Publication Number:
  US 20110190599A1
• Time in Patent Office: 1,238 Days
• Field of Search: 706/20, 706/21, 706/924, 600300-301, 600/323, 600/324, 600/364, 600/484
• US Class Current: 600/301
• CPC Class Codes: A61B 2505/01 Emergency care
• CPC Class Codes: A61B 2505/03 Intensive care
• CPC Class Codes: A61B 5/0806 by whole-body plethysmography measuring blood flow using plethysmography A61B5/0295; impedance plethysmography A61B5/053; measuring volume of the body or parts thereof A61B5/1073
• 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 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/377 using evoked responses
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06F 18/256 of results relating to different input data, e.g. multimodal recognition
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16H 40/67 for remote 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
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients
• CPC Class Codes: Y10S 128/92 : Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/923 : by comparison of patient data to other data
• CPC Class Codes: Y10S 128/924 : using artificial intelligence
• CPC Class Codes: Y10S 128/925 : Neural network
• CPC Class Codes: Y10S 706/924 : Medical

Abstract

Various embodiments may provide methods and systems capable of evaluating physiological parameter data. The methods and systems may include a receiver capable of collecting a signal representative of a physiological status of a patient, a plurality of data analysis components, wherein each of the plurality of data analysis components is capable of generating a metric based on the signal, and an arbitrator communicatively coupled to each of the plurality of data analysis components and capable of generating a single metric from the metrics generated by the plurality of data analysis components.

Claims

1. A method of determining the physiological status of a patient, comprising:

monitoring the patient with at least one sensor to produce physiological parameter data comprising a sequence indicative of blood oxygen saturation over a time period;

analyzing the physiological parameter data with a first pattern recognition feature to identify indications of sleep apnea;

calculating a first sleep apnea metric with the first pattern recognition feature based on indications of sleep apnea identified by the first pattern recognition feature in the physiological parameter data;

analyzing the physiological parameter data with a second pattern recognition feature to identify indications of sleep apnea;

calculating a second sleep apnea metric with the second pattern recognition feature based on indications of sleep apnea identified by the second pattern recognition feature in the physiological parameter data;

applying an arbitration with an arbitrator to the first and second sleep apnea metrics to determine a single sleep apnea metric, wherein applying the arbitration further comprises assigning a weighted value to each of the sleep apnea metrics based on a type of algorithm performed by the pattern recognition feature associated with each metric; and

communicating the single sleep apnea metric to an output feature to display as an indication of the patient'"'"'s status and/or to control a therapeutic device.

2. The method of claim 1, comprising monitoring the patient with a plurality of sensors to produce physiological parameter data comprising an encephalogram, an electromyogram, an extraoculogram, a chest wall plethysmogram, and/or an electrocardiogram.

3. The method of claim 1, comprising monitoring the patient with a plurality of sensors to produce physiological parameter data comprising measurements of nasal and/or oral airflow.

4. The method of claim 1, comprising determining that an indication of sleep apnea is present with the first pattern recognition feature and/or the second pattern recognition feature when a number of desaturation patterns within the time period is identified in the physiological parameter data.

5. The method of claim 1, wherein applying the arbitration comprises selecting a one of the sleep apnea metrics as the single sleep apnea metric.

6. The method of claim 1, wherein applying the arbitration comprises combining the first and second sleep apnea metrics to determine the single sleep apnea metric.

7. The method of claim 6, wherein applying the arbitration further comprises assigning weights to each of the sleep apnea metrics based on a purpose for the single sleep apnea metric.

8. The method of claim 1, comprising controlling an air supply device based on the single sleep apnea metric.

9. The method of claim 1, wherein applying the arbitration further comprises increasing the weighted value as more data becomes available.

10. The method of claim 1, comprising analyzing the physiological parameter data with a third pattern recognition feature to identify indications of sleep apnea and calculating a third sleep apnea metric with the third pattern recognition feature based on indications of sleep apnea identified by the third pattern recognition feature in the physiological parameter data.

11. The method of claim 10, comprising applying the arbitrator to the first, second, and third sleep apnea metrics to determine the single sleep apnea metric.

12. The method of claim 10, wherein analyzing the physiological parameter data with the first pattern recognition feature to identify indications of sleep apnea comprises analyzing a portion of the physiological parameter data.

13. A medical system, comprising:

a microprocessor configured to process physiological parameter data; and

a memory configured to store computer-readable instructions, wherein the contents of the memory comprises computer-readable instructions that if executed are configured to direct the microprocessor to;

analyze the physiological parameter data to identify patterns indicative of sleep apnea using a first procedure for sleep apnea pattern recognition;

quantify patterns identified using the first procedure with a first calculation of a first metric indicative of sleep apnea;

analyze the physiological parameter data to identify patterns indicative of sleep apnea using a second procedure for sleep apnea pattern recognition, wherein the first procedure and the second procedure are different;

quantify patterns identified using the second procedure with a second calculation of a second metric indicative of sleep apnea;

apply arbitration to the first and second metrics to determine a single reporting metric, wherein applying the arbitration further comprises assigning a weighted value to each of the sleep apnea metrics based on a type of algorithm performed by the pattern recognition feature associated with each metric; and

communicate the single reporting metric to an output feature.

14. The medical system of claim 13, comprising a sensor configured to produce the physiological parameter data.

15. The medical system of claim 13, wherein the physiological parameter data comprises a sequence indicative of blood oxygen level over a time period.

16. The medical system of claim 13, wherein the first procedure for sleep apnea pattern recognition is configured to analyze data comprising an encephalogram, an electromyogram, an extraoculogram, a chest wall plethysmogram, and/or an electrocardiogram.

17. The medical system of claim 13, wherein the first procedure for sleep apnea pattern recognition takes into account a portion of the physiological parameter data.