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WO2007131066A2 - Recueil décentralisé de données physiologiques et système et procédé associé - Google Patents

Recueil décentralisé de données physiologiques et système et procédé associé Download PDF

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Publication number
WO2007131066A2
WO2007131066A2 PCT/US2007/068073 US2007068073W WO2007131066A2 WO 2007131066 A2 WO2007131066 A2 WO 2007131066A2 US 2007068073 W US2007068073 W US 2007068073W WO 2007131066 A2 WO2007131066 A2 WO 2007131066A2
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WIPO (PCT)
Prior art keywords
facility
data
physiological data
sensor
ecg
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Ceased
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PCT/US2007/068073
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WO2007131066A3 (fr
Inventor
Mike Hooper
Gari D. Clifford
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PHYSIOSTREAM Inc
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PHYSIOSTREAM Inc
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Publication of WO2007131066A2 publication Critical patent/WO2007131066A2/fr
Publication of WO2007131066A3 publication Critical patent/WO2007131066A3/fr
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/346—Analysis of electrocardiograms
    • A61B5/349—Detecting specific parameters of the electrocardiograph cycle
    • A61B5/35—Detecting specific parameters of the electrocardiograph cycle by template matching
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0031—Implanted circuitry
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording for evaluating the cardiovascular system, e.g. pulse, heart rate, blood pressure or blood flow
    • A61B5/021—Measuring pressure in heart or blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A61B5/333—Recording apparatus specially adapted therefor
    • A61B5/335—Recording apparatus specially adapted therefor using integrated circuit memory devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • 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
    • A61B5/7232—Signal processing specially adapted for physiological signals or for diagnostic purposes involving compression of the physiological signal, e.g. to extend the signal recording period
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B7/00—Instruments for auscultation
    • 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
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Definitions

  • the invention is directed to a process and system For improving the efficiency and economics of research and development that requires the coSlection and analyses of phys ⁇ ologica! data and, more particularly, to improving efficiency and economics by enabling research in off-site research facilities.
  • the invention provides a reduced cost research and development facility that allows for the acquisition of research data along with an automated analyses system.
  • a system for the collection and analysis of physiological data obtained from a remote facility includes a sensor system collecting physiological data from at least one of a wearable sensor and an implantable sensor configured to sense characteristics of a subject located in a facility, a computer system disposed remote from the facility and configured to receive the physiological data from the sensor system via a network, a storage device configured to archive the physiological data received by the computer system, and wherein the computer system is configured to stream the physiological data to a plurality of locations for the collaborative analysis thereof.
  • the computer system and/or a processor may be configured to execute an algorithm to analyze the physiological data.
  • the sensor may be structured and arranged to sense at least one of blood pressure, central venous pressure, pulmonary arterial pressure, pulse oximetry (SAO2), cardiac sounds, non- cardiovascular signals such as EEG K-complexes, muscular activity, neural activity, acoustic waveforms, and speech waveforms.
  • the physiological data may be m ECG and the system further may include a processor to generate a nonlinear signal model based on the ECG signal, fit the nonlinear signal model to the ECG signal based on an optimization algorithm, and determine at least one feature of the ECG with the nonlinear signal model, and an output device to output the at least one feature of the ECG based on the nonlinear signal model.
  • the computer system may be configured to receive physiological data from a plurality of wearable sensors and/or implantable sensors from the network.
  • the computer system may include a platform.
  • the platform may include a Hermes platform
  • the storage may include a redundant array of independent disks.
  • the sensor may transmit the physiological to the computer system via one of a wired connection and a wireless transceiver.
  • the subject may be one of a human and an animal.
  • the facility may include an off-site research facility
  • the plurality of locations may include at least one of a research center, academic facility, physician's office, and clinician ' s office.
  • a process for the collection and analysis of physiological data obtained from a remote facility includes the steps of obtaining physiological data concerning at least one subject from a sensor associated with a subject located in a facility, transmitting the physiological data to a centralized location remote from the facility, streaming the physiological data from the central location to at least two locations remote from the facility and the centralized location, and analyzing the physiological data with at least one algorithm during, prior to, and/or subsequent to the one or more of the obtaining, streaming, and analyzing steps,
  • the process may further include the step of analyzing the data at one of the plurality of locations.
  • the transmitting step may include transmitting the physiological data from a sensor via at least one of wireless transmission &n ⁇ wired transmission.
  • the obtaining step may include obtaining the physiological data from at least one of an implantable sensor and a wearable sensor.
  • the process may further include the step of archiving the physiological data at the centralized location.
  • the process may further include the step of visualizing the physiological data at the central location.
  • the process may further include the step of enabling the collaborative interaction of a plurality of physicians or analysts at a plurality of locations.
  • the subject may be one of a human and an animal.
  • the facility may include an off-site research facility.
  • the plurality of locations may include one of a research center, academic facility, physician's office, and clinician's office.
  • the physiological data may include an ECG and the process may further include the steps of generating a nonlinear signal model based on the ECG signal, fitting the nonlinear signal model to the ECG signal based on an optimization algorithm, determining at least one feature of the ECG with the nonlinear signal model, and outputting the at least one feature of the ECG based on the nonlinear signal model.
  • a system for the collection and analysis of physiological data obtained from a remote facility includes means for collecting physiological data from at least one of a wearable sensor and an implantable sensor configured to sense characteristics of a subject located in a facility, means for receiving the physiological data from the sensor system via a network disposed remote from the facility, means archiving the physiological data received by the collecting means, and means for streaming the physiological data to a plurality of locations for the collaborative analysis thereof.
  • a computer readable medium having instructions stored thereon that when executed by a processor provides for the collection and analysis of physiological data obtained from a remote facility includes instructions for obtaining physiological data concerning at least one subject from a sensor associated with a subject located in a facility, instructions for transmitting the physiological data to a centraiized location remote from the facility, instructions for streaming the physiological data from the central location to at least two locations remote from the facility and the centralized location, and instructions for analyzing the physiological data with at ieast one algorithm during, prior to, and/or subsequent to the execution of one or more of the obtaining instructions, streaming instructions, and analyzing instructions
  • FIG. 1 schematically illustrates exemplary details of one embodiment of an off-site research facility constructed according to the principles of the invention
  • Figure 2 schematically illustrates one embodiment of the overall system for the collection and analysis of physiological data from the off-site research facility shown in Figure 1 ;
  • Figure 3 is a flowchart schematicaSSy illustrating the collection and analysis of physiological data from a off-site research facility operating according to the principles of the invention: [0018]
  • Figure 4 is a flowchart schematically illustrating a generalized exemplary analysis process for constructing a mode! fit signal according to the principles of the invention, which may be used with the system of Figure 1 ;
  • Figure 5 shows an onginal (clean) graphed ECG signal, a mode! fit signal constructed according to the principles of the invention and the residual error between the two signals;
  • Figure 8 shows a mode! fit to an ECG signal using the principles of the invention under high noise conditions. The underlying signa! before noise was added and is shown. Note that the model fit preserves the overall morphology &n ⁇ placement of the onset and offset of the main features;
  • Figure 7 shows a ST-elevated waveform and model fit constructed according to the principles of the invention.
  • the examples used herein are intended merely to facilitate an understanding of ways in which the invention may be practiced and to further enable those of skill in the art to practice the embodiments of the invention. Accordingly, the examples and embodiments herein should not be construed as limiting the scope of the invention, which is defined solely by the appended claims and applicable law. Moreover, it is noted that like reference numerals represent similar parts throughout the several views of the drawings. [0024]
  • the invention is directed generally to a virtual laboratory process and system by which traditional "brick and mortar' ' contract research organizations (CROs) are replaced by a decentralized, dispersed "fiat" model in which the cost of research and development is greatly reduced. The cost of such a "virtuaP laboratory.
  • CROs "brick and mortar' ' contract research organizations
  • Off site research facility may be reduced by locating the off-site research facility in a location, off-site from the higher cost location of the researchers, business unit, and so on.
  • Off-site as used herein, may include any remote site where efficiencies are obtained from such location due to decreased costs or other advantages, such as areas outside of urban areas, in other cities, or in other countries.
  • the virtual laboratory may include a plurality of remote processes and business functions that trad ⁇ tionaliy would be centralized about the current contract CRO business models.
  • the remote capability of these processes and functions may be enabled by three technologies described in greater detail videow. These techno ⁇ ogies inciude instrumentation and its sensors 102, a data archiving visualization, and a streaming platform 104, and algorithms for automated analyses 106.
  • the invention may enable the remote collection of physiological data from sensors including wearable or implantable sensors, such as sensors disposed on the surface of the skin, in test subjects, such as animals or humans, in an off-site research facility, such as a preclinical trial facility.
  • the sensor may be wireless or wired.
  • the type of facility contemplated in the invention may be lower in cost and simpler than most traditional laboratory environments since it need not employ highly trained, high cost clinicians to implant sensors, care for animals, and collect data. This reduces the size, complexity, and cost of the facility.
  • the off-site facility may be a simple, low cost "animal farm" administered by personnel having lower labor costs for example.
  • [O027J Data may be collected from implantable wired or wireless sensors that may convert the physiological data into a format for network transmission.
  • the data may be converted into rentable internet protocol (IP) packets or the like.
  • Figure 1 schematically illustrates exemplary details of one embodiment of an off-site research facility constructed according to the principles of the invention.
  • the data may be sent to a Remote Communications Processor (RCP) 250 that buffers the data for transmission over a standard, low cost data communications circuit 252 such as DSL modem.
  • RCP 250 may perform several communication and administrative functions to allow centralized control over the remote processes.
  • a physiological data collection and visualization platform 104 may be the central data collection technology that may gather streams of networked data from the sensors at many off-site laboratories or locations simultaneously. The capacity and throughput of the platform 104 may allow the platform to replace many standalone remote PCs, thus lowering the overall cost of capture and storage. [0030] The physiological data collection and visualization platform 104 may permit users in various remote locations to peruse r analyze, and annotate the physiological data in a centralized data base 114 as discussed in greater detail below. This permits the use of lower cost, highly trained clinicians in remote parts of the world where wages are typically much lower than in the US or portions of Europe or Asia.
  • the invention may include various sensor technologies, a platform For collection of data, a network for connecting the platform as discussed below with an off-site research facility and the off-site research facility 299 itself, in particuiar, the sensor technologies may be used in humans, various types of mammalians, or other types of animals and may be implanted or attached thereto.
  • the sensor technology may be wired or wireless including a wireless fidelity (Wi-Fi), ceSlular, or the like.
  • Wi-Fi wireless fidelity
  • ceSlular ceSlular
  • the sensor technologies may be low cost, implantable, and wearable.
  • the sensor technologies may allow for large amounts of high speed Song term physiologicai data to be collected,
  • the sensor technology may be an implantable wireless sensor 132 with a stimulator that may be implanted into the subject. Wired sensors are also contemplated.
  • the implantable wireless sensor may have bidirectional communication 254 to a transceiver 256 through, for example, a high gain directional antenna 258 and/or multiple input multiple output type of device.
  • the transceiver 256 may be a multiple channel receiver having both receive and transmit functions.
  • the multipSe channel transceiver 266 may use a ZiGBEE type of network configuration setting.
  • the multiple channel transceiver 256 may be connected to the RCP 250.
  • the RCP 250 may ha ⁇ /e a small form factor and/or single board computer.
  • the RCP 250 may include a MINJ-STX or a NANO-STX layout. Moreover, the RCP 250 may operate using a Linux or Windows OS operating system for example only.
  • the RCP 250 may include a CPU and a hard disk drive to provide sensor data buffer functions.
  • the RCP 250 may include a router that may form a network connection, such as a DSL modem, that may be configured separate from the RCP 250. The router may connect to a locaS LAN network an ⁇ may also connect to the internet via the network connection. Accordingly, the pSatform 104 may also connect to the internet providing communications therebetween.
  • the RCP 250 may provide a web server interface for remote configuration control may also allow routing configurations and settings, alternate route/path settings, date storage buffer settings, Communications protocol settings, sensor controls, and ZiGSEE networking configuration settings.
  • a system 100 and associated process may include a combination of instrumentation and sensors 102: data archiving, visuaiization, and streaming 104; and aigorithms for automated anaiysis 106, as discussed in greater detaii beiow. More specificaiiy the instrumentation and sensors 102 may include wearabie and implantable sensor technology, or other sensors known in the art. This sensor technology may include wired or wireless type transmission of sensor data.
  • the data archiving, visualization, and streaming 104 may aiiow for the data acquisition from the sensors that may be digitized, stored, and streamed to remote archiving, analysis servers.
  • the algorithms for automated analysis 106 may inciude the capabiiity to analyze data automaticaiiy and may further include the capability to have the data reviewed by one or more clinicians remotely at separate locations via internet or other type of network.
  • the instrumentation and sensors 102 of the invention may include wearable devices 122 such as a holter monitor as is known in the art to permit continuous iong- term subject monitoring.
  • the holter monitor is also referred to as an ambuiatory electrocardiography device that may be a portable device and may aliow for continuous monitoring of the heart for up to or more than 24 hours.
  • the holter monitor may include a series of electrodes and may provide recording of the output of the electrodes to a flash memory or the like.
  • the sensors noted above may aiso be implantable 132,
  • the sensor may be implanted and may contain a self-sufficient energy source or battery.
  • the battery may have the ability to be recharged subcutaneo ⁇ sly.
  • the sensor may contain a radio frequency responsive and/or powered circuit energy source.
  • Such sensors allow for ECG monitoring, autonomic monitoring, peripheral nerve monitoring, systemic glucose monitoring and so on.
  • the implantable type sensors 132 are configured to be small and operate subcutaneously.
  • the implantabie type sensor 132 may non-invastve arrangement. Both the wearable 122 and implantable sensors 132 provide a robust event monitoring anaiysss with realtime data capture and streaming.
  • the implantable sensor 132 may have a self- contained form factor, multi-modal sensor and/or anaiysts capabiiity, and aliow for longitudinal data analysis. Both types of sensors may include a housing, memory, operating circuitry, a battery and other components known in the art. The sensors may also be implemented as battery-iess sensors that receive power through inductive type circuits. Furthermore, in order to provide wireiess transmission of the data, the sensors may include a transceiver and the like. The sensor, may include various input/output connections and the like.
  • the sensor technology used in conjunction with the invention may sense any type of physiological phenomena including electrical, acoustic, vibratory and so on.
  • any known sensor technology may be used in conjunction herewith, although it is preferred to use electrical sensing sensors.
  • a sensor may be used to measure any physiological signal, and, for example, may include blood pressure, central venous pressure, pulmonary arterial pressure,installe oximetry (SAO2). cardiac sounds, non-cardiovascuiar signals such as EEG K-compiexes, muscular activity, neural activity, acoustic waveforms, and speech waveforms.
  • Such types of sensors may include the ability to automatically detect events and transmit data, and may further include real-time data streaming.
  • the data transmission may include transmission directiy from a patient's device to an analysis platform by a cellular data network.
  • the platform as described in greater detail below, may include one or more of a software application, operating system, and hardware.
  • the transmission may include any known protocol including GPRS, EVDO. UNTS, Wimax, WiFi, Bluetooth or the like.
  • the data archiving visualization and streaming platform 104 may provide for the long-term physiological collection and analysis of the data collected by the instrumentation and sensors 102 noted above in particular, the data archiving, visualization, and streaming platform 104 may ailow for the collection, formatting, storage, visualization, automated analysis, annotation, event detection and the like of the physiological data collected by the instrumentation and sensors 102.
  • the data archiving, visualization and streaming platform 104 may include a platform 108 allowing for the high-speed, high-throughput, multi-channel data collection from the instrumentation and sensors 102. For example, collection may be carried out over, in part, the internet or other type of data transmission network 116.
  • the platform 104 may allow for efficient high-volume formatting of the data and RAID-based (RAID-Redundant Array of independent Disks) storage 1 14 for multiple-user, m ⁇ ltipie-site access and archiving. Moreover, the platform 104 may al ⁇ ow for locaS or remote retrospective or real-time graphical visualization using such processes as a web server process. Additionally, the platform 104 may allow for single or multiple users 110 (collaborative), multiple location annotation of the data, Finally, the platform 104 may include various event detections such as heart rate detection for notification and alarming.
  • the coliaborative web-based technology model enables collection of physiological data and analysis of results at many different locations 112, 112', 112".
  • the platform 104 may also allow for real-time streaming of physioiogical data from a large number of remote sensors.
  • the sensors being one or more sensors described above or others as are weli known in the art.
  • the platform further may allow for the use of screening algorithms.
  • the screening algorithms may be able to quickly highlight areas of interest in the data that is held by the data archiving, visualization, and streaming platform 10S that was obtained through the instrumentation and sensors 102.
  • the platform may provide a very high volume of capture, storage, and screening of incoming sensor data for automated, Song-term testing. Examples of data archiving, visualization, and streaming platforms that may be used in the invention are disclosed in copending United States Patent Application
  • PHYSIOLOGICAL DATA having attorney docket No, 2048120-5008US, the disclosure of which is incorporated by reference in its entirety herein and includes the known HermesTM type platform, which includes HermesTM servers, operating system, and so on.
  • the HermesTM platform may be provided by Hermes Medical Solutions, inc. Chicago, Illinois, U.S.A.
  • the HermesTM platform 108 may include any type of processor such as a PC or server, storage system such as a RAID level one (mirror) configuration or the like.
  • the HermesTM system may further include any form of seamiess network integration.
  • the algorithms for automated analysis 106 may include any known type of analysis that is algorithm-based or otherwise, in one particular aspect, the analysis may include an ECG-type anaiysis as discussed in greater detaii below. However, the invention contemplates any type of automated analysis whether based on an algorithm or otherwise. Moreover, the invention contemplates analysis of any type of physiologicai data including blood pressure, centra! venous pressure,131rnonary arterial pressure,installe oximetry (SAO2), cardiac sounds, non-cardiovascular signals such as EEG K-complexes, muscular activity, neural activity, acoustic waveforms, speech waveforms, and so on.
  • Figure 3 is a flow chart schematicaily iiiustrating the coiiection and anaiysis of physiologicai data process according to the principles of the invention.
  • Figure 3 shows a coiiection and anaiysis process 200 that may be performed by the system 100 in Figure 2 or any other equivending type system or arrangement.
  • step 202 subjects are provided with wearable type sensor arrangements or implantable type sensor arrangements or other sensors known in the art at the off- site research facility.
  • the sensors provide sensor output as noted above.
  • the wireless type sensors may output to a wireless access point, c ⁇ liuiar tower or the like.
  • the wired type sensors may be configured to connect to some form of network type connection such as the internet.
  • the data that is acquired from the subjects may then be transmitted over a network such as the Internet to a data archiving, visualization, and streaming type platform 108.
  • a network such as the Internet
  • the data which is the output from the sensors may be archived and stored in a large database, it also may be modified to provide an additionai level of analysis and reporting such as a visual imaged-based report.
  • the data may be streamed to other locations.
  • the other location can include one or more remote or local medical facilities, physicians, analysts, clinicians, and the like, which may be located e.g., in higher cost, more urban locations than the off-site research facility.
  • the data archiving, data visualization, streaming platform or the various medical analysts may then be abSe to further apply an algorithm or other type of analysis to the data as shown in step 208.
  • the various physicians and analysts and the like may then further collaborate together with the information obtained as described above, even if they are in different locations from each other and the off-site research facility.
  • the flowchart of Figure 4 shows a generalized exemplary analysis process for constructing a model according to the invention.
  • the process of the invention provides a genera! framework for deriving models of quasi-stationary signals for robust filtering, compression and segmentation of a signal and for identifying the location of regions of change.
  • the process can be viewed as a type of nove! adaptive filter or as a process for correlated source separation in the time domain
  • the approach is suited to physiological signals, which are often characterized by oscillations at specific frequencies, and contaminated by in- band noise (which is both periodic and statistical). This approach is set forth in greater detail in copending United Patent Application No. 11/470,506.
  • the signal model is a dynamic model, where each turning point in a signal is represented by a Gaussian of varying width and amplitude, centered at different points in time.
  • This nove! implementation extends the model by adding a new Gaussian for each asymmetric turning point, then adaptiveiy modifying the parameters to fit a distinct observation.
  • the concept is generalized to model any signal and provide an automatic method for deriving the mode parameters.
  • a transient feature such as a K complex
  • M + 2N Gaussians are required to describe the feature (since a Gaussian is symmetric).
  • an asymmetric turning point requires two Gaussians to be accurately represented.
  • the segment of the signal z which describes the feature under analysis is given by:
  • the coefficients a govern the magnitude of the turning points and the b ; define the width (time duration) of each turning point.
  • the model is therefore fuily described by 3(M + 2N) parameters.
  • Fiducial markers may then be located at various points in time that provide time- specific reference markers for each candidate feature (segment of signai) as shown in step 302 of Figure 4.
  • a first template class is generated as shown by step 304.
  • possible artifacts or patterns belonging to other feature classes may be rejected using a suitable threshold such as a cross-correlation as shown in step 306, £0053]
  • the first feature class may then be modified to be the average of the non- rejected individual features (to construct a more specific feature).
  • the rejected candidate may then be averaged to form a second feature ciass tempiate and the process repeated (see arrows A and B) until the number of possible remaining candidates (which were not included in the previous classes) are crizow some predefined threshold, or the inter-pattern variance between the remaining candidate patterns becomes too high to allow the formation of any more distinct groups.
  • the first feature class is likely to be a sinus beat (as long as it is the dominant morphology in the time series). Abnormal beats may be rejected and the dominant abnormal beat may become the second feature class- High correiations between the average of this rejected set and each member of the set may identify the new members of the set. Rejected beats may cascade down to the next candidate class.
  • One method is as foSSows: if there are enough feature candidates to form a smooth. Sow noise template, the number of turning points may be calculated by numericaSiy differentiating the feature and iocating the zero crossing points (after allowing for delays in the numerical differentiation function) as shown in step 308. [0057] The degree of asymmetry for each turning point may then be found by squaring the resultant differential &n ⁇ comparing the resultant two peaks (one for the upslope and one for the downslope) as shown in step 310 if a given pair of peaks are similar in height and width, then the peak is symmetric an ⁇ oniy one set of a, b,, and t s are required for the peak.
  • Equation (1) may be solved using an (3M + 8M)-dimensionai nonlinear gradient descent on the parameter space, in general, the problem of mu ⁇ tidimensiona! nonlinear least squares fitting requires the minimization of the squared residuals of n functions.
  • ali algorithms for achieving the minimization may proceed from an initiai guess using the linearization.
  • the invention may be appiied in a novel technique for fitting a nonlinear ECG model (a sum of temporally shifted Gaussian waveform morphologies) to the ECG using a nonlinear least squares optimization.
  • Figure 5 illustrates the performance of the fitting procedure for a typical ECG with no noise in the original signal.
  • Figure 4 iliustrates the performance of the technique when fitting the model to m extremely noisy beat.
  • the model-based fitting of an ECG allows one to more precisely determine the locations of the P, Q, R, S and T features of each beat, and their respective onsets and offsets (determined as a certain number of standard deviations away from the central point). Furthermore, since noise may not be explicitly encoded in the waveform, the fitting procedure makes for an excellent noise suppression technique. Although the representation of the beat as just 18 coefficients in a nonlinear mode! means that (iossy) compression is possible, the clustering of these coefficients allows one to classify beats on this basis. However, perhaps the most usef ⁇ i and immediate application of this model-fitting procedure is in the determination of wave boundaries in noisy conditions to aliow robust and accurate QT analysis.
  • the model consists of a sum of Gaussians centered on each wave of the ECG (P, Q, R, S, and T).
  • Each Gaussian is fuliy specified by three parameters: location in time, ampiitude, and broadness. Therefore, the representation of the ECG as a series of Gaussians is aiso a form of (lossy) compression.
  • the parameters for each beat may be compared to a norma! set of parameters and a classification made.
  • an efficient method of fitting the ECG model described above to an observation s(t), is to minimize the squared error between the s(t) and ⁇ . That is, one may find
  • Equation (4) may then be soived using an eighteen-dimensional gradient descent in the parameter space.
  • the MatSab function Isqnoniin.m or the like may perform the required implementation of this nonlinear least squares optimization.
  • a simpie peak-detection and time-aligned averaging to form an average beat morphology template is formed over, for example, at ieast the first 60 beats centered on their R-peaks.
  • the template window is unimportant, as long as it contains a ⁇ i the PQRST features and does not extend into the next beat).
  • Cross correlation is then performed between each beat and the template to remove outliers (with a linear cross-correlation coefficient less than, for example, (X 95). If more than about 20% of the beats are removed, then another 60 beats may be allowed into the average template, and the outlier rejection procedure is re-iterated. When less than about 20% of the beats are discarded, another average template is then made of the remaining beats. Peak and trough detection is then performed on this template (using refactory constraints for each wave) to find the relative locations of the turning points in time (and hence the ⁇ t ).
  • the values T ' and T' may be initialized ⁇ 40 ms either side of ⁇ , . By measuring the heights of each peak (or trough) an estimate of the O 1 may also be made.
  • Each b t may be initialized with a value 10 + 5// , where ⁇ is a uniform distribution on the interval [0, . . . , 1],
  • Each of the values, a i , and ⁇ i were initialized with random perturbations of ⁇ and 20 ⁇ respectively.
  • Figure 5 shows an original (clean) graphed ECG signal, a model fit signal constructed according to the invention and the residual error between fit and model signals, in particular, Figure ⁇ illustrates a real beat (recorded from a V ⁇ lead), a typical fit to a template of real beat, and the residual error
  • Figure 8 illustrates the results of fitting the mode! to a segment of ECG cleanly recorded and contaminated by electrode motion noise.
  • the above-described method for simultaneously filtering, compressing, and classifying a physiological signal, such as the ECG, from a subject may work in real time on a modern desktop PC and the like.
  • the PC may execute a stgnal processing program such as MatiabTM ⁇ Available from: The Math Works, Inc. Natick. MA 01780- 2098) or the like to perform the above-noted method as is known in the art.
  • MatiabTM ⁇ Available from: The Math Works, Inc. Natick. MA 01780- 2098
  • One advantage of using prior knowledge concerning beat morphology is that a fitting error may be calculated with respect to the model, and thus we have an in-line measure of how well the procedure has filtered the ECG segment.
  • the model-based filter may introduce insignificant clinical distortion in the GT intervai an ⁇ QRS width down to an SNR ⁇ OdB for 1/f ⁇ Beta noise for Beta ⁇ 2,
  • the fiduciai point location may be insignificantly distorted ⁇ 1 ms) for an SNR ⁇ 2dB, and the ST-!ev ⁇ l may be stabie down to SNR > 12dB,
  • the PR-interval may be more sensitive to noise due to the low amplitude nature of the P-wave, but still robust to noise, in general, the filter performance may be degraded by increasing Beta.
  • the method of producing confidence intervals for a particular fit, or classification is an important step in determining the performance of a particular algorithm.
  • In-line methods such as these may facilitate the robust interpretation of data and algorithms, reducing the number of juice alarms that are triggered.
  • the smooth nature of the fitted waveform allows for simple an ⁇ robust detection of clinical features such as the iso-eiectric point, QT-interval, and ST level.
  • the residua! error from the fitting procedure then provides a confidence measure for the model-derived values of these features.
  • the above-described model has been generalized to aiiow modeling of turning points that exhibit asymmetries (such as the T-wave) by allowing such a feature to be described by two Gaussians, The mode! as such, may now be used to represent any waveform.
  • the model complexity increases considerably for stochastic processes that inherently have many fluctuations compared to the sampling frequency.
  • the main utility of the method detailed herein lies in the fact that the model represents smooth osculations with few turning points compared to the sampling frequency, and therefore has a morphology-specific multi-band pass filtering effect leading to a iossy transformation of the data into a set of integrate Gaussians distributed over time.
  • Each ciinicai feature of the ECG waveform is represented by a known and limited set of parameters. This allows for a very compact representation of the ECG morphology and makes the description mathematically tractable and completely generaiizable to any semi-periodic signal. [007Sj Testing of the invention has resulted in accurate QT interval estimates. In contrast, it has been found that ECG analysts consistently pick the T offset to be early, since the analysts are unable to discern T-wave ends from the noise in the data. Accordingly, adaptation of the Gaussian model-based algorithm to locate Q- onset and T-offset points in a robust fashion, allows an accurate method for QT interval measurement, even in high noise situations,
  • the invention may utilize extra information with 12 leads with the use of a multi-channei QT anaiysis system, with noise rejection using Independent Component Analysis, Principa! Component Analysis and Frank lead reconstruction (using the (inverse) Dower transform).
  • the sensitivity of QT analysis to varying ievels and types of noise may be evaluated, to provide a principled on-line confidence index for each QT interval evaiuation.
  • the relationship between the QT interval, preceding and foilowing RR intervals, and other ECG mode! parameters (P, Q, R, S, and T amplitude and duration) such as ⁇ wave detection and characterization, T-wave height, and T-wave asymmetry are also contempiated by the invention.
  • each wave may be well mode ⁇ ed by a log- normal distribution. Therefore, other embodiments of this approach may consider log-normal distributions aSso. Disadvantages exist in that the probabiiistic interpretation is not so well defined, but there are fewer parameters to Rt.
  • T-wave amplitude or relative T ⁇ wave amplitude (such as the R-peak divided by T-wave peak height);
  • SQTI caused by a gain of function substitution in the HERG (IKr) channel
  • SGT2 caused by a gain of function substitution in the KvLQTI (Iks) channel
  • SQT3 which has a unique ECG phenotype characterized by asymmetrical T waves. See, e.g.
  • QT dispersion is defined as the difference between the maximum and minimum QT intervals of any of 12 leads.
  • QTd is sometimes thought to be a marker of myocardial electrical instability and has been proposed as a marker of the risk of death for those awaiting heart transpiantafion. See e g., "Development of Automated 12 ⁇ Lead QT Dispersion Algorithm for Sudden Cardiac Death/ ' M. 8. Malarvifi, S, Hussain, Ab. Rahsm Ab. Rahman, The Internet Journai of Medicai Technology, 2005, Volume 2 Number 2.
  • QTd takes a Gaussian histogram of vaiues for a particular population. There is a significant cross-over between norma! and those at risk of sudden cardiac death (SCD)
  • the mean value of QTd+ISD is 37.28 ⁇ 11.13ms (p ⁇ 0 05) for a non-MI group and 66.17 ⁇ 13.95ms (p ⁇ 0.05 ⁇ for the Mi group.
  • QTd ⁇ 50ms is the threshoid for normality, but this wouid lead to 20-30% of the normals being classified as Mi and -20% being classified as non-MI.
  • Using the height, skew, width and kurtosis variables as above would improve the sensitivity significantly.
  • the methods described herein are intended for operation with dedicated hardware implementations including, but not limited to, PCs. PDAs, semiconductors, application specific integrated circuits (ASIC), programmable logic arrays, and other hardware devices constructed to implement the methods described herein.
  • various embodiments of the invention described herein are intended for operation as software programs running on a computer processor.
  • alternative software implementations including, but not limited to, distributed processing, component/object distributed processing, parallel processing, virtual machine processing, any future enhancements, or any future protocols thereof may also be used to implement the methods described herein.
  • the software implementations of the invention as described herein are optionally stored on a tangible storage medium, such as: a magnetic medium such as a disk or tape; a magneto-optical or optical medium such as a disk; or a solid state medium such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories.
  • a digital file attachment to email or other self- contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. Accordingly, the invention is considered to include a tangible storage medium or distribution medium, as listed herein and including art-recognized equivalents and successor media, in which the software implementations herein are stored.
  • the invention may fit a set of aiternate basis functions to the signai, perhaps using some other form of optimization, may use other signais other than physiological signais; may use any set of basis functions, not just Gaussians; may use any optimization routine to fit the basis functions to the observation - ieast squares, non ⁇ inear least squares, gradient descent with any cost function and any activation function (such as tanh or softmax in a neural networK).
  • any optimization routine to fit the basis functions to the observation - ieast squares, non ⁇ inear least squares, gradient descent with any cost function and any activation function (such as tanh or softmax in a neural networK).
  • IIR/FIR filters independent Component Anaiysis (ICA); Principai Component Analysis (PCA) / Singular Vaiue Decomposition (SVD) / Karhunen Loeve Transform (KLT) / Hoteliing Transform; Auto-Regressive (AR) modeiing - equivended to Fourier Transform; and Wavelet Analysis (Laguna et al, Hughes et al.) approaches may also be used for further pre-processing or post- processing.
  • ICA independent Component Anaiysis
  • PCA Principai Component Analysis
  • KLT Karhunen Loeve Transform
  • Hoteliing Transform Hoteliing Transform
  • AR Auto-Regressive
  • Wavelet Analysis Lasera et al, Hughes et al.

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Abstract

L'invention porte sur un système et un procédé de recueil et d'analyse de données physiologiques provenant d'une installation distante et concernant au moins un sujet présent dans l'installation. Les données sont transmises en continu à un site centralisé distant de l'installation qui les transmet à son tour à deux sites distants de l'installation et du site centralisé, qui les analysent au moyen d'au moins un algorithme avant, pendant, et/ou après leur obtention, leur transmission et leur analyse.
PCT/US2007/068073 2006-05-02 2007-05-02 Recueil décentralisé de données physiologiques et système et procédé associé Ceased WO2007131066A2 (fr)

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US8870783B2 (en) 2011-11-30 2014-10-28 Covidien Lp Pulse rate determination using Gaussian kernel smoothing of multiple inter-fiducial pulse periods
US8923945B2 (en) 2009-09-24 2014-12-30 Covidien Lp Determination of a physiological parameter
US9031793B2 (en) 2001-05-17 2015-05-12 Lawrence A. Lynn Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions
US9415125B2 (en) 2012-05-02 2016-08-16 Covidien Lp Wireless, reusable, rechargeable medical sensors and system for recharging and disinfecting the same
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US10058269B2 (en) 2000-07-28 2018-08-28 Lawrence A. Lynn Monitoring system for identifying an end-exhalation carbon dioxide value of enhanced clinical utility
US11439321B2 (en) 2001-05-17 2022-09-13 Lawrence A. Lynn Monitoring system for identifying an end-exhalation carbon dioxide value of enhanced clinical utility
US9031793B2 (en) 2001-05-17 2015-05-12 Lawrence A. Lynn Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions
US10032526B2 (en) 2001-05-17 2018-07-24 Lawrence A. Lynn Patient safety processor
US10366790B2 (en) 2001-05-17 2019-07-30 Lawrence A. Lynn Patient safety processor
US10297348B2 (en) 2001-05-17 2019-05-21 Lawrence A. Lynn Patient safety processor
EP2410909A4 (fr) * 2009-03-27 2013-07-31 Cardionet Inc Traitement ambulatoire et centralisé d'un signal physiologique
US9655518B2 (en) 2009-03-27 2017-05-23 Braemar Manufacturing, Llc Ambulatory and centralized processing of a physiological signal
US8923945B2 (en) 2009-09-24 2014-12-30 Covidien Lp Determination of a physiological parameter
US8870783B2 (en) 2011-11-30 2014-10-28 Covidien Lp Pulse rate determination using Gaussian kernel smoothing of multiple inter-fiducial pulse periods
US9415125B2 (en) 2012-05-02 2016-08-16 Covidien Lp Wireless, reusable, rechargeable medical sensors and system for recharging and disinfecting the same
EP3654835A1 (fr) * 2017-07-19 2020-05-27 Endotronix, Inc. Système de surveillance physiologique
US11622684B2 (en) 2017-07-19 2023-04-11 Endotronix, Inc. Physiological monitoring system
US12213760B2 (en) 2017-07-19 2025-02-04 Endotronix, Inc. Physiological monitoring system
CN114098748A (zh) * 2020-08-27 2022-03-01 株式会社理光 生理信号的处理方法、装置及计算机可读存储介质
CN116226605A (zh) * 2023-04-25 2023-06-06 北京领创医谷科技发展有限责任公司 一种刺激器参数的拟合方法及系统
CN116226605B (zh) * 2023-04-25 2023-07-25 北京领创医谷科技发展有限责任公司 一种刺激器参数的拟合方法及系统

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