Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
  • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
  • A61B5/087—Measuring breath flow
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
  • A61M16/0051—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes with alarm devices
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
  • A61M16/021—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes operated by electrical means
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/74—Details of notification to user or communication with user or patient ; user input means
  • A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
  • A61M16/22—Carbon dioxide-absorbing devices ; Other means for removing carbon dioxide
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
  • A61M16/0003—Accessories therefor, e.g. sensors, vibrators, negative pressure
  • A61M2016/0027—Accessories therefor, e.g. sensors, vibrators, negative pressure pressure meter
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M16/00—Devices for influencing the respiratory system of patients by gas treatment, e.g. mouth-to-mouth respiration; Tracheal tubes
  • A61M16/0003—Accessories therefor, e.g. sensors, vibrators, negative pressure
  • A61M2016/003—Accessories therefor, e.g. sensors, vibrators, negative pressure with a flowmeter
  • A61M2016/0033—Accessories therefor, e.g. sensors, vibrators, negative pressure with a flowmeter electrical
  • A61M2016/0036—Accessories therefor, e.g. sensors, vibrators, negative pressure with a flowmeter electrical in the breathing tube and used in both inspiratory and expiratory phase
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
  • A61M2230/00—Measuring parameters of the user
  • A61M2230/40—Respiratory characteristics
  • A61M2230/43—Composition of exhalation
  • A61M2230/432—Composition of exhalation partial CO2 pressure (P-CO2)
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
  • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
  • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

• the present invention relates to devices and methods for detecting abnormal conditions with respect to patients having attached medical support or diagnostic devices for ventilating the human body.
• the present invention relates to a device and method for detecting and identifying alarm conditions associated with the delivery of anesthesia to a patient or to a patient's breathing circuit.
• critical conditions arising during mechanical ventilation of a patient fall within two categories.
• One set of events involves those occurrences which are physiological within the patient. These can often be detected by monitoring parameters within the breathing circuit; however, they may not be caused by mechanical ventilation per se. Other problems such as blocked air passages, leaking valves or hoses, sticking valves, disconnections or other mechanical failures of the system make up the second category of critical events. Typically, these occurrences can be immediately corrected if known.
• alarm rules can be established based on a model breathing circuit. Safe operational ranges may be identified, with alarms to be fired when measured parameters deviate outside this normal model.
• alarms are based on generalizations as opposed to specific patient needs.
• a second form of smart alarm system is commonly referred as an expert system.
• rules are applied in a collective rather than individual sense. In other words, many situations may be coordinated within an expert system, rather than depending only on a preprogrammed series of events, as is common with traditional smart systems.
• the expert system searches for and links together all the rules that apply in each specific situation.
• threshold levels may represent maximum or minimum values on given parameters which must be arbitrarily fixed. Obviously, the assignment of such threshold values must be an estimation based on an individual's judgment. Although an experienced expert can surely reduce the error of deviation, the process is inherently flawed by the fact that some minimum or maximum value must be specifically fixed. In reality, the actual measured values will be affe-cted by many parameters which frustrate even experts' efforts in attempting to focus on specific ranges of acceptable measurement or performance.
• a further object of the present invention is to provide a device and method for detecting critical events within a breathing circuit by monitoring sensor output as a whole in contrast to individual sensor readings and determination within ranges.
• a still further object of the present invention is to detect and identify critical events within a breathing circuit by identifying a composite measurement of numerous individual parameters which make up a total picture of the patient's environment.
• An additional object of the present invention is to provide a device and method for detecting and identifying critical events which relieve the anesthesiologist of much of the evaluation effort by both detecting and identifying the cause of a given problem.
• a further object of the present invention is to provide a diagnostic approach within the field of ventilation assistance which can be both self-training and self-correcting while reducing both complexity and cost.
• This method comprises the steps of identifying at least one physiological function to be monitored with medical support or diagnostic devices and identifying a plurality of features within data to be generated by such- support or diagnostic devices during operation.
• These diagnostic devices are attached to the mach set up, test animal or patient and are operated to generate data representing the various identified features to be monitored. This data is inputted into input nodes of a neural network capable of generating a single coordinated image for the combined input data.
• At least one alarm condition associated with the physiological function or device attached is identified. This identification serves to program a system for future detection of the stated alarm condition.
• the alarm condition is then empirically created within the mach set up or is observed and measured within the test animal or actual patient while such data is being inputted to the input nodes of the neural network. Accordingly, this data representing the identified features produces a single picture or coordinated image which corresponds to the alarm condition to be detected in the future. This coordinated image is correlated within an associated alarm activating signal in memory for future recall on comparison. Upon re-occurrence of a similar coordinated image at the input nodes of the neural network, the alarm signal automatically activates and identifies the pre-determined or trained condition corresponding to that coordinated image.
• This system allows the monitoring equipment to compare the total picture provided by input data with a library of pre- determined pictures which were generated empirically and assigned to certain critical events. Upon identification of a similar picture, the monitoring system is able to both detect and identify the problem and cause with surprising accuracy.
• Figure 1 shows a graphic representation of a breathing circuit typically applied in mechanical ventilation of a patient.
• Figure 2 shows a graphic representation of a neural network in accordance with the present invention.
• Figure 3 graphically illustrates the operation of a neuron within the neural network of Figure
• Figure 4 is a graph of a decision boundary based on a single layer of neurons.
• Figure 5 is a graph of a neural network decision boundary having two layers as input and output respectively for defining linear boundary edges.
• Figure 6 graphically illustrates an actual boundary generated by increasing the number of neurons in each layer to provide boundaries defined by secondary equations.
• Figure 7 is a graph illustrating exclusive — or boundary areas generated by a neural network having an intermediate layer of neurons as illustrated in Figure 3.
• Figure 8 depicts a block diagram of the general procedure of the present invention as applied to training a neural network to recognize specific critical medical events.
• Figure 9 provides a block diagram illustration of the present invention for purposes of applying the trained data network to detect and identify actual critical events with relation to an actual patient.
• Figure 1 illustrates a patient breathing system in a process of mechanical ventilation such as would be used in the administration of anesthesia to a patient.
• the system includes a ventilator 10 coupled to an inspiratory valve 11 and through an inspiratory hose 12 to a pneumotach 13.
• the patient is coupled via an endotracheal tube 14.
• the expiratory circuit extends from the hose 15 through an expiratory valve 16 and CO2 absorption canister 17. Actual operation of this system is well known to those skilled in the art and needs no further explanation.
• the patient breathing system shown in Figure 1 is intended to generally represent any system for mechanical ventilation of a patient. This may be a support system or an anesthesia delivery device. More generally, the illustrated breathing system is intended to represent medical support or diagnostic devices which are generally used to monitor physiological functions of the patient. In the present case, the physiological function being monitored is breathing. It is envisioned, however, that the concepts to be discussed hereafter may be applied to other unrelated medical areas such as cardiac support and diagnostic systems wherein the physiological function being monitored is the heart and circulatory system. These are, of course, considered exemplary and not limiting.
• the present invention involves a method for detecting and identifying abnormal conditions in a realtime environment. These conditions may occur within a mach setup such as an oil/water lung model or other similar mach systems where multiple inputs of data may be applied to represent a given condition of the medical support or diagnostic device. More importantly, this method can be applied to a test animal or a patient with attached medical support or diagnostic devices to monitor conditions which may constitute critical events or abnormal circumstances requiring immediate medical attention.
• Such conditions are typically identified by monitoring certain features representing data output from the medical support or diagnostic device during operation.
• the system of Figure 1 is fitted with three sensors which respectively monitor different features of the physiological function of breathing.
• the CO2 sensor 20 provides ongoing measurement of the feature of CO2 concentration in the inspired and expired air. In the present instance, these measurements are taken by a Nihon Kohden (Model OIR) infrared C0 2 sensor; however, other sensor devices may be equally suitable.
• An additional feature is airway pressure. This is monitored by a Sensym (SCXOIDN) transducer represented in block diagram as a pressure sensor 21.
• SCXOIDN Sensym
• a third feature of the breathing function to be monitored by the present invention is gas flow rates as measured by a Fleisch #0 Pneumotach coupled to a differential pressure sensor 22 comprising a Validyne (MP 45-22) pressure transducer 22. Additional sensors and diagnostic devices may be applied within the breathing circuit; however, actual use of this system has confirmed the adequacy of monitoring these primary features of the breathing function.
• the analog signals from the three transducers are sampled at 60 hertz with 12 bit resolution by a Zenith 386 computer 23 or some other form of computerized data controlled system which receives input data from 20, 21 and 22.
• These three primary features are broken into component features which have been classified into two categories.
• the first category comprises 25 features selected for their high information content. These features are set forth in the following Table I. TABLE I
• These features are selected from the flow-volume curve, pressure-volume curve and from the CO2 wave form generated by the respective sensors. As will be explained in greater detail hereafter, these 25 differential features are used by the present invention to identify the actual critical event which has occurred to cause a change in the patient breathing. This is in contrast to mere detection of the critical event.
• phase I volume - phase III volume /inspired tidal volume 5.
• F ET C0 2 - F ⁇ C0 2 /F ET CO
• a neural network as is illustrated in Figure 2 provides the mechanism for determining the occurrence of the abnormal conditions or critical events to be monitored.
• the neural network is a mathematical model similar to neural cells which are linked together to create a network which can be taught or trained to identify sets of inputs which appear to be similar to the example input sets previously supplied to the network in a training situation.
• the system learns to recognize critical events by seeing the events as represented by input data at the neural network during such a training session.
• Each cell or neuron 25 has an input side and an output side as illustrated in Figure 3.
• the input side receives multiple signals while the output comprises a single signal.
• neural network applied in the present invention is a Backward Error Propagation system and is well known to those skilled in the art. Its basic building block is the neuron 30, corresponding to the cells or neurons 25 in Figure 2. Input data registers as X Q through X N . With this input data, a single output signal Y is generated.
• the neuron 30 separates data into two classes using a linear decision boundary 31 as shown in Figure 4.
• Data mapped onto one side of the boundary are mapped as belonging to one group (i.e. group A) and data mapped to the other side are classified as belonging to the other group (group B) .
• the enclosed boundary condition provided in Figure 5 is a product of including four neuron cells in adjacent configuration and by interlinking all of their outputs, as is illustrated in Figure 2.
• input level 24 registers five input sources a, b, c, d and e. These represent sources of input data.
• input data at "a” would correspond to the upstroke slope of the gas curves.
• Input "b” would correspond to the downstroke slope, "c” to the phase III slope etc.
• These data signals are then interlinked with the first layer of neurons 25 by interlinking connections 28.
• input 24 "a” has seven connections each tied respectively to neurons f, g, h, i, j, k and 1.
• Input from b similarly is interlinked to each respective neuron f through 1. Therefore, neuron f in layer 25 generates an output signal based on the cumulative effect of the five input signals received from inputs a through e.
• the parallel influence of input data registered from a, b, c, d and e is concurrently sensed at each of the respective neurons f through 1, the five inputs shown in the example of Figure 2 would generate a five dimensional graphic with a closed boundary of at least seven sides based on seven neurons at level 25, in contrast to the four sides illustrated in Figure 5. More complex decision boundaries such as those shown in Figure 6 can be generated with more complex neurons configured in multilevels. Such examples are shown in Figures 6 and 7.
• FIG. 7 shows an exclusive— or boundary condition wherein groups 35 are exclusively separated by members of the second group B.
• This separated boundary condition is accomplished by utilizing a third layer of neurons.
• This third layer allows data to be classified into a particular class if data points are located in one region £. if they are located in a separate region of the feature space such as areas 36 and 37.
• each neuron in level 25 receives the same signals "a” through “e", the respective neurons are trained to assign different weights of significance to input data received. This technique of whhting input values is also well known to those skilled in the art. For example, neuron “f” receives input from a through “e” but may not give equal value to each input. Input from “a” may be determined more significant for a particular critical event than input from “d”. By differentiating or weighting the respective inputs from “a” through “e” at each of the neurons "f” through “1", thousands of combinations or rules can be generated which relate input from the transducers as a whole to the output of the neural network at neurons m, n, o, p and q.
• a neural network such as that illustrated in Figure 2 is implemented in the present invention with respect to the physiological function of breathing by using input data identified in Tables I and II. Each of these data items is considered to be a feature monitored by the medical support or diagnostic device during operation.
• the neural network operates within the subject breathing apparatus in a manner similar to the human brain. It learns by experience to associate certain combinations of input data received from many sources with a certain type of emergency condition or critical event. Initially, the neural network must be taught to associate the critical event with data input. This is accomplished by attaching the medical support or diagnostic such as the mechanical ventilating system of Figure 1 to a mach setup, test animal or patient. This system is then set in operation to generate data representing the identified features (Table I or Table II) . This data enters through a set of neural network inputs at level 24, elements "a” through “e”.
• each of these data inputs is identified as a feature, such as the slope of a curve, volume of flow measurement, pressure measurements or other data forms which are extracted from the transducers 20, 21 and 22.
• the neural network receives this data at its input nodes a, b, c, d and e and networks its signals to all of the neurons at the next level 25 (f-1) .
• the operator identifies an alarm condition or critical event which is to be associated with the physiological functions or medical support/diagnostic device which is to be programmed for detection.
• an alarm condition or critical event which is to be associated with the physiological functions or medical support/diagnostic device which is to be programmed for detection.
• Table III lists 14 conditions which are considered critical events requiring immediate attention.
• exp. hose disconnection One of these critical events is selected and identified as an alarm condition for detection. This condition is then empirically created or measured within the mach setup, clinical test animal or actual patient such that the input data being inputted to the input nodes 24 (a through e) produce a coordinated image at output nodes 27 which correspond to the alarm condition. For example, if the identified condition is an obstruction in the endotracheal tube as identified by item 1 in Table III, and the device is attached to a mach setup, the tube would be obstructed and data would be introduced at the input side 24. The algorithm would then train the neural network memory with respect to the identified condition which would be represented by the coordinated image or output signal produced by the combined output of neurons m through q.
• This coordinated image unique to the alarm condition would be associated with an alarm activating signal which is stored in the computer's memory for future recall and comparison.
• the computer would automatically generate the alarm activating signal.
• This signal would identify not only the occurrence of a critical event, but would in fact identify the specific event based on the ability of a neural network to associate and recall that information which it was formerly trained.
• the coordinated image representing the data output 27 is analogous to a snapshot at a given point in time reflecting all of the inner relationships and weighting factors assigned to the input data.
• the neural network assimilates all the information in parallel as opposed to serial form and merely looks for that coordinated training image which most resembles the actual reading taken on-line with the patient.
• the present invention monitors critical events to detect its occurrence. This may be accomplished as a threshold consideration which must arise or it may be accomplished concurrently with the second object of identifying the specific critical event or cause.
• these separate classifications are implemented with two separate neural networks, each having a separate set of inputs. The systems may then operate to detect events independently or in concert.
• Each of the two classifiers uses input data derived from the same monitored physiological signals; however, these signals supply different types of information or features to accomplish different objectives.
• the input to the first classifier are totally concerned with the current signal in its absolute form.
• the first feature identified in Table II provides data on C0 2 upstroke slope.
• Each time the patient takes a breath a new set of signals are generated which are independent from signals generated by both the previous and subsequent breaths.
• the first classifier network does not compare the absolute value of the new data being received with previous data.
• An advantage of including an absolute classifier is the ability of the device to detect events whose effect on the monitored signal occurs slowly, over a significant duration of time. For example, it is important to be able to detect alarm conditions which are present at the immediate commencement of medical procedures. Furthermore, the device must have the ability to prevent false alarms due to adjustments in the monitored system as may be developed by the patient, the physician or by attendant circumstances. Without this absolute classifier, an event causing very gradual changes may go undetected. Conversely, if an anesthesiologist must make an adjustment in patent ventilation, a system of differential classification would see the adjustment as a critical event, again sounding the alarm.
• the value of the present invention is that it provides a two level classification system which first registers the occurrence of an event based on absolute values, but also evaluates changes in conditions during the course of system operation. Without this second classifier capability, the system would lack specificity because of differences between patients. An absolute value for one patient may not be indicative of an absolute value for another. Similarly, a physiologic signal corresponding to one type of critical event for one patient may be easily confused with that of another event for a different patient. This confusion makes the absolute classifier inadequate for determining the specific type of . critical event which may have occurred. Therefore, the present system embodies a second classifier which compares a current coordinated image with previous coordinated images of earlier readings.
• the strength of this differential classifier is its ability to identify specific events in spite of interpatient differences. This is because the difference between a monitored signal in Table I from a previous signal has less patient-to-patient variation than do the absolute signal values of Table II. Therefore, the differential classifier has greater specificity.
• the weakness of the differential classifier is that it fails to detect a critical event if the event changes very slowly over time. Also, the differential classifier fails to detect an event if the classifier system starts during the occurrence of the critical event or alarm situation. As implied earlier, the differential classifier also would respond to variations caused by adjustment of the system by the anesthesiologist. This is so because the classifier sees only the change and ignores the fact that the monitored signal, both before the system adjustment and after the adjustment, were both normal and acceptable.
• the generated coordinated images are subject to both absolute evaluation and comparative evaluation. This greatly enhances the ability of the system to give full protection to the patient by optimizing the ability of the attending physician to detect and correct both absolute changes and those which are a function of time.
• the present invention can be trained to recognize the reoccurrence of an earlier coordinated image which was assigned a given alarm condition. Therefore, the 14 alarm conditions listed in Table III would be conditions which must be taught to the neural network and programmed for recall and comparison.
• This comparison need not be an identical coordinated image in that the signals could be superimposed, but could simply be that coordinated image and memory which is most similar to the newly detected image. Accordingly, upon occurrence of an absolute feature characteristic of problems with the breathing system, the neural network would automatically compare the present coordinated image with its prior experience generated during teaching incidents. It would then identify the closest or most similar coordinated image and register the associated alarm signal.
• this alarm signal is generally an audible warning indicating the need for immediate attention and corrective action. Concurrent with this warning is the designation of any component of the medical support or diagnostic device which is now functioning.
• the system is adapted to generate a graphic display on a computer screen showing medical support or diagnostic device with an identifier element designating the component which the neural network has identified as the possible malfunctioning element. Similar warning sequences may be applied to other critical events and causes as well.
• Step 1 The specific physiological function (such as breathing) which is to be monitored with a particular medical support or diagnostic device should be identified.
• the medical support device comprises the anesthesia delivery system as represented in Figure 1.
• the physiological function is breathing.
• the present invention is applied by using the medical support device comprises the anesthesia delivery system as represented in Figure 1.
• the physiological function is breathing.
• the present invention is applied by using the medical support or diagnostic device as the data source to a neural network to monitor the breathing function.
• Step 2 Several separate but related data characteristics or "features" which are generated by the medical support device must be identified which are indicative of the breathing function. Such features may be differential in nature such as the various air flow, volume and pressure data set forth in Table I or may be absolute features as described in Table II. In any event, these features are selected because of their utility in identifying changes or problems with respect to the breathing function.
• Step 3 With the medical support device attached to a patient, test animal or mach set-up, and adapted with the means for generating the desired data including the identified features to be evaluated, the system is set into operation and data is generated representing the plurality of features.
• Step 4 Generated data corresponding to each of the selected features is concurrently transmitted and inputted to input nodes of the neural network provided by a computer and an operating algorithm which is capable of generating a single coordinated output "image" or signal for the combined input data.
• the incoming data representing (i) 25 different features for differential comparison or (ii) 14 absolute features is processed by the neural network to generate a single output signal which will represent an alarm condition to be detected during future use.
• Step 5 The specific alarm condition (such as an obstructed endotracheal tube ) is identified. Obviously, these have some relationship to the physilogical function or medical support or diagnostic device which is to be monitored. In the illustrated example previously described, 14 alarm conditions or critical events were identified and listed in Table III.
• Step 6 In order to obtain data ouptut from the medical support device which is indicative of the alarm condition, one must create this condition within the mach set up, clinical test animal or actual patient in order to generated the type of data relevant to this condition. In other words, an intentional block is made in the endotracheal tube corresponding to critical event No. 1 in Table III. Incoming data to the neural network will correspond to the blocked tube and create the desired coordinated output or image which is to be recorded in the neural network memory. Accordingly, this step involves empirically creating the alarm condition such that the generated data concurrently inputted to the input nodes of the neural network will correctly represent and identify the created alarm condition.
• Step 7 This data now needs to be labeled within the computer memory as representing the specific alarm condition. To accomplish this, the single coordinated output signal or image generated by the neural network is correlated or associated with the identified alarm condition and a corresponding activating signal. These are all stored in the computer memory for future recall upon recurrence of a similar coordinated output signal.
• this method is applied in repetitive manner with respect to each alarm condition to generate a large statistical base of single output signals which serve as indicators of this alarm condition.
• This statistical gathering is made possible by identifying which features are most significant with respect to an identified alarm condition, and providing weighting factors with respect to those features to realize the single output for the neural network which corresponds to the alarm condition.
• Neural network 23a would have 14 inputs, corresponding to the 14 features identified in Table II.
• Neural network 23b would have 25 inputs, corresponding to the 25 features of Table I.
• the system would be trained by identification 50 of an alarm condition corresponding to one of the alarm conditions set forth in Table III (such as an obstructed endotracheal tube) .
• the presence of an alarm condition will correspond to an abnormal breath 51 is identified with a negative response 52.
• the amch system 53 is configured to simulate the alarm condition.
• the endotracheal tube might be intentionally obstructed or clamped off so that the attached medical support device 54 and array of sensors 20, 21 and 22 submit data to the computer and neural network 23 corresponding to the simulated condition.
• This input data is evaluated by the neural network to develop a single coordinated data output or image for the Table II features 55 and
• Table I features 56 These respective coordinated output signals are manipulated by an appropriate algorithm for data association 57 and 58.
• This data association links the single coordinated ouptut signal representing Table I and Table II features, with the identification of alarm condition 50 and abnormalcy of breath 51/52.
• This combined data association is stored in the computer as training data 59. This data is used as part of a statistical collection of single coordinated output signals representing the given alarm condition. Future correlation of realtime data with the stored training data 59 is compared when the system is operated in a realtime detection mode (as contrasted with the training mode of Figure 8) .
• the detecting mode is depicted in Figure 9 by a similar arrangement of blocked diagrams, wherein the directional arrows for the lower two tiers of blocks are reversed.
• a patient 60 has an attached endotracheal cuff and a supply line 61 which connects to a medical support device 62 in a manner similar to that illustrated in Figure 1.
• Sensor input is transmitted to the computer 23 for processing of input data 20, 21 and 22 and through the neural network.
• the computer does not preassign any status of normalcy or abnormalcy to the incoming data.
• the data is processed through the neural network by a computer evaluation of the identified 14 and 25 features represented in Tables II and I. As indicated in block diagram, these evaluations respectively will identify the "occurrence" of a critical event, and the
• the single coordinated output signal for Table II evaluations is represented by arrow 63.
• the single coordinated output signal for Table I features is similarly represented by arrow 64.
• the data output 63 of Table II is compared with the training data 59 previously stored in the computer 23.
• the data association 65 compares data ouptut from the neural network 63 in the form of single coordinated output signals 59 which were previously created during the referenced training sequence.
• the computer 23 detects whether the realtime output signal 63 correlates or is similar to any of the stored signals 59 representing an abnormal breath condition. If correlation is detected, the negative assessment 66 is registered and activates identification of the type of alarm condition 67 which may be causing the abnormal breath. In the absence of any correlation between the realtime signal 63 and the training data 59, the system is automatically programmed to respond with an affirmative status 68 of normalcy.
• the identification of specific alarm conditions is accomplished by comparing the output signal 64 with the training data 59 corresponding to the Table I features.
• the computer algorithm sill select that coordinated output signal which have been stored in the training data 59.
• the computer algorithm will select that coordinated ouptut signal from the training data which most closely corresponds to the realtime signal 64.
• This activity is represented by the data association block 70.
• the alarm condition 67 is identified by reason of a previously stored alarm activating signal contained within the computer memory 23. This activating signal triggers a display and audio alarm 71 which gives appropriate warning to the attending personnel.
• the present alarm system was trained using an oil/water lung model. Fourteen critical events were created as identified in Table III. Each of these events was simulated 20 times to generate a strong statistical base of coordinated images representing each condition. After completing the training, the system was able to correctly identify 99.5% of the 14 different breathing circuit critical events which were recreated using the lung model.
• the system received further training utilizing two mongrel dogs where 14 events were created, ten times each.
• the system was then tested on seven mongrel dogs having weights between 20 to 30 kilograms. 1,029 occurrences of 13 alarm conditions were created under controlled ventilation. In this test, the system correctly identified the cause 89.3% of the times. During spontaneous breathing, the system was able to detect 75.8% of ten separate alarm conditions that had been created as 236 repetitions.
• the principal advantage of the neural network approach to alarm recognition is its ability to create the equivalent of hundreds of decision rules which previously were arbitrarily set by experts or other persons using ranges and threshold values as alarm signals.
• the present system simply monitors the coordinated images or output signals generated by the neural network and classifies these with respect to coordinated images generated in earlier training sessions.
• the system is not dependent upon assignment of arbitrary values, ranges or threshold levels. Instead, the present invention selects that critical event from the network's memory which is most similar to the coordinated image generated by the parallel input data.

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  • 1990-09-14 WO PCT/US1990/005250 patent/WO1991003979A1/fr not_active Application Discontinuation
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