Patent Description:
<CIT> discloses a method of displaying signals containing a spatial and a temporal aspect, where multiple signals are received by multiple sensors.

<CIT> discloses an apparatus and method for determining a signal quality of an input signal representing a repetitious phenomena derived from at least one sensor connected to a patient.

An outpatient, as used herein, is a patient who is not hospitalized overnight after visiting a healthcare facility (e.g., a hospital, a clinic, etc.) for a diagnosis and/or a treatment of an unhealthy condition. Mobile physiological sensors as known in the art of the present disclosure provide an efficient, accurate and economic method for monitoring a health of outpatients, particularly for supporting energy expedition calculations of outpatients.

Examples of such mobile physiological sensors include, but are not limited to, electrocardiogram ("ECG") monitors, respiration electrode patches, pulse oximeters, blood pressure monitors, galvanic skin response sensors, skin temperature sensors, heat flux sensors and near body temperature sensors. Future mobile physiological sensors may include, but not be limited to, arterial blood gas sensors, electroencephalography ("EEC") sensors and electromyogram ("EMG") sensors.

A central monitoring station, as the term is used herein, implements a communication protocol for exchanging information with remote sensors like mobile physiological sensors. A communication protocol standard of particular relevance in the domain of a physiological signal monitoring and analyzing by a central monitoring station is the global ISO/IEEE <NUM> Personal Health Device (PHD) Communication family of standards. Current implementations of this family of standards involve the exchange of source identifying information of a mobile physiological sensor whereby a central montioring station knows the type of physiological signal being communicated by the mobile physiological sensor. More particularly, an encrypted payload may be sent by the mobile physiological sensor across a communication channel established between the mobile physiological sensor and the central monitoring station whereby the payload includes bit information explicitly mapping the mobile physiological sensor and whereby the central monitorting station decrypts and deciphers the bit information to identify the type of physiological signal being communicated by the mobile physiological sensor. This dependency by the central monitoring station of source identifying information of the physiological sensor facilitates an applicable monitoring and analyzing of the physiological signal by the central monitoring station.

For example, <FIG> illustrates a central electrocardiogram ("ECG") monitoring station <NUM> for exchanging information with eight (<NUM>) ECG monitors <NUM>. Central ECG monitoring station <NUM> and ECG monitors <NUM> implement a communication protocol (e.g., the ISO/IEEE <NUM>-<NUM> bi-directional communication protocol) involving the exchange of source identifying information of ECG monitors <NUM> via payloads explicitly mapping ECG monitors <NUM> on a procotocol layer (e.g., a transport layer) to thereby prepare central ECG monitoring station <NUM> and ECG montiors <NUM> for application layer messaging of ECG signals <NUM> from ECG monitors <NUM> to station <NUM>. This dependency by ECG central monitoring station <NUM> of source identifying information of ECG monitors <NUM> facilitates applicable monitoring and analyzing of ECG signals <NUM> via workstations <NUM> of central ECG monitoring station <NUM> (e.g., a normality/abnormality interpretation of ECG signals <NUM>).

A variety in the types of mobile physological sensors has been increasing in view of an ever increasing poplultion of outpatient requiring long-term care due to a rising average life expectancy, a higher ratio of seniors among the general population and an increased prevalence of chornic diseases. With the increasing varitey of types of mobile physiolocial sensors entering the healthcare market, there is a need for a central monitoring station having an independency on physiological sensor identifying information for signal monitoring and/or analysis.

To improve upon the advantages and benefits of central monitoring stations for monitoring and/or analyzing one or more physiological signals, the present disclosure provides systems, stations, and methods premised on an implementation of an independency of physiological sensor identifying information for signal monitoring and/or analysis of physiological signals, particularly physiological signals generated by varied types of physiological sensors.

For purposes of the present disclosure, the term "central monitoring station" broadly encompasses all central monitoring stations, known prior to the present disclosure, having a dependency on physiological sensor identifying information for monitoring and/or analyzing one or more types of physiological signals (e.g., a need for payload bit informaitoin explicitly mapping physiological sensors generating the physiological signals). Examples of such central montioring stations include, but are not limited to, the IntelliVue Information Center iX (PIIC).

Also for purposes of the present disclosure, the term "central signal segregation station" broadly encompasses a central monitoring station having a structural configuration incorporating inventive principles of the present disclosure as exemplary described herein for implementing an independency of physiological sensor identifying information for signal monitoring and/or analysis of a plurality of physiological signals, particularly physiological signals generated by varied types of physiological sensors, and the term "central signal segregation method" broadly encompasses all methods that incorporate the inventive principles of the present disclosure as exemplary described herein for implementing an independency of physiological sensor identifying information for signal monitoring and/or analysis of physiological signals by a central signal segregation station.

Various embodiments described herein include a central signal segregation station employing a signal acquisition controller and a signal segregation controller. In operation, the signal acquisition controller receives a plurality of different types of physiological signals from a plurality of unknown physiological sensors (e.g., electrocardiogram ("ECG") monitors, respiration electrode patches, pulse oximeters, blood pressure monitors, galvanic skin response sensors, skin temperature sensors, heat flux sensors and near body temperature sensors).

For a monitoring of the physiological signals, the signal segregation controller identifies a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model among a plurality of physiological signal models derived from known types of physiological sensors. The distinct signal features of each physiological signal establish an independency by the signal segregation controller of physiological sensor identifying information for signal monitoring and/or analysis of the physiological signals (e.g., the signal segregation controller identifies each physiological signal without any assistance or need for payload bit information explicitly mapping physiological sensors generated the physiological signals).

For an analysis of the identified physiological signals with or without monitoring, the central signal segregation station further employs a signal analyzing controller. Subsequent to the type identification of each physiological signal by the signal segregation controller, the signal analyzing controller may further:.

Various embodiments described herein include a central signal segregation system employing a plurality of unknown physiological sensors and a central signal segregation station. In operation, each unknown physiological sensor transmits a different type of physiological signals to the central signal segregation station (e.g., electrocardiogram ("ECG") signal, a respiration signal <NUM>/ΩR, an oxygen saturation signal SO<NUM>, a blood pressure signal (systolic/diastolic), a galvanic skin response signal <NUM>/ΩS, a skin temperature signal °F/°C, a heat flux signal W/m<NUM> and a near body temperature signal °F/°C).

For a monitoring of the physiological signals, the central signal segregation station identifies a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model among a plurality of physiological signal models derived from known types of physiological sensors. The distinct signal features of each physiological signal establish an independency by the signal segregation controller of physiological sensor identifying information for signal monitoring and/or analysis of the physiological signals (e.g., the signal segregation controller identifies each physiological signal without any assistance or need for payload bit information explicitly mapping physiological sensors generated the physiological signals).

For an analysis of the identified physiological signals with or without monitoring, the central signal segregation station may further:.

Various embodiments described herein include a central signal segregation method involving a central signal segregation station receiving a plurality of different types of physiological signals from a plurality of unknown physiological sensors (e.g., electrocardiogram ("ECG") monitors, respiration electrode patches, pulse oximeters, blood pressure monitors, galvanic skin response sensors, skin temperature sensors, heat flux sensors and near body temperature sensors).

For a monitoring of the physiological signals, the central signal segregation method further involves the central signal segregation station identifying the type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model among a plurality of physiological signal models derived from known types of physiological sensors. The distinct signal features of each physiological signal establish an independency by the signal segregation controller of physiological sensor identifying information for signal monitoring and/or analysis of the physiological signals (e.g., the signal segregation controller identifies each physiological signal without any assistance or need for payload bit information explicitly mapping physiological sensors generating the physiological signals).

For an analysis of the identified physiological signals with or without monitoring, the central signal segregation method may further involve the central signal segregation station:.

The foregoing forms and other forms of the inventions of the present disclosure as well as various features and advantages of the inventions of the present disclosure will become further apparent from the following detailed description of various embodiments of the inventions of the present disclosure read in conjunction with the accompanying drawings.

To facilitate an understanding of the embodiments of the present disclosure, the following description of <FIG> and <FIG> teaches inventive principles of central signal segregation systems and central signal segregation methods of the present disclosure. While the description of <FIG> and <FIG> are provided in the context of a plurality of mobile physiological sensors including an electrocardiogram ("ECG") monitor, a respiration electrode patch, a pulse oximeter, a blood pressure monitor, a galvanic skin response sensor, a skin temperature sensor, a heat flux sensor and a near body temperature sensor, those having ordinary skill in the art of the present disclosure will appreciate how to apply the inventive principles of the present disclosure to make and use a variety of central signal segregation systems and central signal segregation methods of the present disclosure in the context of numerous physiological sensors as shown and of additional physiological sensors, mobile and/or immobile.

Referring to <FIG>, one embodiment of a central signal segregation system of the present disclosure employs, as known in the art of the present disclosure, a plurality of physiological sensors including:.

Still referring to <FIG>, the physiological sensors transmit the physiological signals via a network <NUM> (e.g., an intranet or an internet) to a central signal segregation station <NUM> as shown. As will be understood, the central signal segregation station may be implemented in various hardware arrangements such as a stand-alone server or as one or more virtual machines hosted on hardware in a cloud computing environment or distributed among multiple cloud computing environments. In practice, the physiological sensors may be sequentially or concurrently transmitting a subset or an entire set of the physiological signals, and two or more of the physiological sensors may be monitoring the same individual outpatient.

Also in practice, the physiological sensors and central signal segregation station <NUM> implement a communication protocol (e.g., the ISO/IEEE <NUM>-<NUM> bi-directional communication protocol) excluding the exchange of source identifying information of the physiological sensors of any layer of the communication protocol (e.g., excluding payload bit information explicitly mapping physiological sensors generating the physiological signals). To address this independency by central signal segregation station <NUM> of source identifying information of the "unknown" physiological sensors, central signal segregation station <NUM> identifies a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a different physiological signal model among a plurality of physiological signal models <NUM> derived from the known types of the physiological sensors.

Physiological signal models <NUM> provide for an estimation of a probability of a particular type of a physiological signal based on one or more signal features of the physiological signal as will be further described herein. In the context of the unknown physiological sensors of <FIG>, central signal segregation station <NUM> is trained to generate:.

From the type identification of the physiological signals, central signal segregation station <NUM> processes the physiological signals via the application layers of the communication protocol and operators of central signal segregation station <NUM> may access monitoring application(s) via workstations <NUM> to thereby visually monitor the physiological signals.

Additionally, central signal segregation station <NUM> may execute analyzing application(s) that provide analytical information to the operators via workstations <NUM> and/or as feedback to the physiological sensors as will be further described herein.

Also, context aware information may be appended by the physiological sensors to the physiological signals in support of the analyzing application(s) of central signal segregation station <NUM> as will be further described herein.

More particularly, <FIG> illustrates a flowchart <NUM> representative of one embodiment of a central signal segregation method of the present disclosure executable by central signal segregation station <NUM> (<FIG>).

Referring to <FIG> and <FIG>, central signal segregation station <NUM> executes flowchart <NUM> responsive to a concurrent reception of physiological signals <NUM> from the unknown physiological sensors.

Specifically, a stage S112 of flowchart <NUM> encompasses central signal segregation station <NUM> identifying a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a particular physiological signal model among a plurality of physiological signal models <NUM> derived from the known types of the physiological sensors.

Upon completion of stage S112, if central signal segregation station <NUM> is not structurally configured for signal quality processing of the identified physiological signals (e.g., a signal-to-noise quality measurement), then central signal segregation station <NUM> proceeds to a stage S122 of flowchart <NUM> to manage a stream storage of the physiological signals within a database for monitoring purposes.

Otherwise, if central signal segregation station <NUM> is structurally configured for signal quality processing of the identified physiological signals (e.g., a signal-to-noise quality measurement), then central signal segregation station <NUM> proceeds to a stage S116 of flowchart <NUM> to execute signal quality processing of the identified physiological signals and provide signal-specific feedback to any physiological sensor(s) communicating low quality physiological signal(s) as will be further described herein.

Prior to proceeding to stage S116, central signal segregation station <NUM> may initiate stage S122 for a stream storage of the physiological signals within a database as symbolized by the dashed arrows for monitoring purposes.

Upon completion of stage S116, if central signal segregation station <NUM> is not structurally configured for signal-specific diagnostic analyzing of the identified physiological signals of high quality, then central signal segregation station <NUM> proceeds to stage S122 of flowchart <NUM> to manage a stream storage of the physiological signals within the database.

Otherwise, if central signal segregation station <NUM> is structurally configured for signal-specific diagnostic analyzing of the identified physiological signals of high quality, then central signal segregation station <NUM> proceeds to a stage S120 of flowchart <NUM> to annotate specific regions of each identified physiological signal of high quality having maximum diagnostic information, and/or to perform a confirmatory diagnosis of identified physiological signal of high quality, particularly in view of context aware information appended to the physiological signals (e.g., a geolocation and any accelerated motion of a physiological sensor) as will be further described herein.

Prior to proceeding to stage S120, central signal segregation station <NUM> may initiate or continue stage S122 for a stream storage of the physiological signals and associated processing information within the database as symbolized by the dashed arrows for monitoring and analytical purposes.

Upon completion of stage S120, central signal segregation station <NUM> may initiate or continue stage S122 for a stream storage of the physiological signals and associated processing and diagnostic information within the database for monitoring and analytical purposes.

Flowchart <NUM> is executed by central signal segregation station <NUM> in sequential or overlapping cycles. Thus, at any time during flowchart <NUM> or upon completion of stage S122, central signal segregation station <NUM> returns to stage S112 to initiate a new cycle until such time flowchart <NUM> is terminated (e.g., central signal segregation station <NUM> is powered down for maintenance and/or upgrade).

From the descriptions of <FIG> and <FIG>, those having ordinary skill in the art of the present disclosure will appreciate the implementation by central signal segregation station <NUM> (<FIG>) and flowchart <NUM> (<FIG>) of an independency of physiological sensor identifying information for signal monitoring and optional analysis of the physiological signals.

To facilitate a further understanding of the present disclosure, the following description of <FIG> further teaches inventive principles of central signal segregation stations of the present disclosure. While the description of <FIG> are provided in the context of the plurality of mobile physiological sensors shown in <FIG>, those having ordinary skill in the art of the present disclosure will appreciate how to apply the inventive principles of the present disclosure to make and use a variety of central signal segregation stations of the present disclosure in the context of numerous physiological sensors as shown in <FIG> and of additional physiological sensors, mobile and/or immobile.

Referring to <FIG>, a signal acquisition module <NUM> of the present disclosure is installed on each unknown physiological sensor and structurally configured to transmit an associated physiological signal <NUM> in accordance with a communication protocol exclusive of any physiological sensor identifying information. If an associated physiological sensor in practice incorporates devices for generating context aware information <NUM> (e.g., geolocation and accelerated motion of the physiological sensor), then a signal acquisition module <NUM> will append the context aware information <NUM> with the physiological signal <NUM>. Alternatively, context aware information <NUM> may be derived from a mobile phone or any connected device to the subject.

Still referring to <FIG>, an embodiment 100a of central signal segregation station <NUM> (<FIG>) employs a signal acquisition controller <NUM>, a signal segregation controller <NUM>, a signal analyzing controller <NUM>, and a database <NUM>. In practice, controllers <NUM>-<NUM> may be segregated as shown, or partially or fully integrated. Also, in practice, database <NUM> may be segregated from controllers <NUM>-<NUM> as shown or integrated/distributed within one or more of controllers <NUM>-<NUM>.

Signal acquisition controller <NUM> is structurally configured for receiving physiological signals <NUM> in accordance with the communication protocol exclusive of any physiological sensor identifying information (e.g., excluding any payload bit information explicitly mapping the associated physiologic sensor). In practice, signal acquisition controller <NUM> controls transmitters, receivers and/or transceivers (not shown) of central signal segregation station <NUM> as known in the art for receiving physiological signals <NUM> in accordance with the communication protocol and provides physiological signals <NUM> to signal segregation controller <NUM>.

Signal segregation controller <NUM> is structurally configured in accordance with the inventive principles of the present disclosure for executing stage <NUM> of flowchart <NUM> (<FIG>) and for executing stage S122 of flowchart <NUM> (if applicable). In practice, signal segregation controller <NUM> is trained to generate physiological signal models <NUM> as previously described herein (<FIG>) and to apply each received physiological signal <NUM> to each physiological signal model <NUM> to identify a particular type of each physiological signal <NUM>.

In practice, any embodiment of signal segregation controller <NUM> will be dependent upon various operational factors as will be appreciated by those having ordinary skill in the art of the present disclosure including, but not limited to, a variance in the types of physiological signals to be monitored and/or analyzed.

Referring to <FIG>, one embodiment 104a of signal segregation controller <NUM> employs a signal source identifier <NUM> and a database manager <NUM>.

Signal source identifier <NUM> is structurally configured in accordance with the inventive principles of the present disclosure to generate the physiological signal models <NUM> (<FIG>) for an estimation of a probability of a particular type of each physiological signal <NUM> based on one or more signal features of physiological signal <NUM>. In practice, signal source identifier <NUM> may generate physiological signal models in the form of a logistic regression model, a linear support vector machine model and/or a decision tree model involving an extraction of signal features including, but not limited to:.

For example, in a training phase of signal source identifier <NUM>, <FIG> illustrates a feature extraction 150a by signal source identifier <NUM> of training physiological signals 133a-<NUM> in the form of feature vectors <NUM> being an n-dimensional vector of signal features (s), n ≥ <NUM> or a vector loop. Signal source identifier <NUM> thereafter applies a machined learning device <NUM> (e.g., a support vector machine) to each feature vector <NUM> for model building 150b of physiological signal models <NUM> whereby the signal features of a feature vector <NUM> serve as independent variables of models <NUM> and the dependent variable provides a probability estimation of the corresponding training physiological signal <NUM>.

By further example, in an identification phase of signal source identifier <NUM>, <FIG> illustrates a feature extraction 150c by signal source identifier <NUM> of received physiological signals <NUM> in the form of a feature vector <NUM> being an n-dimensional vector of signal features(s), n ≥ <NUM> or a vector loop. Signal source identifier <NUM> thereafter applies feature vector <NUM> to each physiological signal model <NUM> to obtain a similarity measurement <NUM> (e.g., <NUM> ≤ SM ≤ <NUM>). Physiological signal <NUM> is deemed to correspond to the physiological signal model <NUM> providing the higher similarity measurement <NUM>. For example, upon sufficient training, if physiological signal <NUM> is an ECG signal, then similarity measurement <NUM>(<NUM>) of ECG model <NUM>(<NUM>) should have the highest similarity measurement among all similarity measurements <NUM>.

Referring back to <FIG>, signal analyzing controller <NUM> is structurally configured in accordance with the inventive principles of the present disclosure for executing stages S116-S122 of flowchart <NUM> (<FIG>). In practice, signal analyzing controller <NUM> executes analyzing application(s) that provide analytical information and/or signal feedback to the user(s)/monitor(s) of the physiological sensor(s) and/or operator(s) of the central signal segregation station will be further described herein.

In practice, any embodiment of signal analyzing controller <NUM> will be dependent upon various operational factors as will be appreciated by those having ordinary skill in the art of the present disclosure including, but not limited to, any and all delineated analytical techniques to be applied to the physiological signals.

Referring to <FIG>, one embodiment 105a of signal analyzing controller <NUM> employs a signal quality manager <NUM>, an annotation manager <NUM>, a diagnostic analyzer <NUM> and a database manager <NUM>.

Signal quality manager <NUM> is structurally configured to execute signal quality processing of the identified physiological signals and provides signal-specific feedback to any physiological sensor(s) communicating low quality physiological signal(s). In practice, signal quality manager <NUM> may detect a required signal-to-noise (SNR) for each identified physiological signal. Further, signal quality manager <NUM> may ascertain if all the relevant fiducial points, which are needed for diagnosis level prediction, are present in each identified physiological signal. Also, if the signal quality is poor, then signal quality manager <NUM> tries to identify the source of degradation either due to, for example, a wrong placement, an electromagnetic interference and/or a device malfunctioning of the associated physiological sensor. Signal quality manager <NUM> provides the feedback to a user/monitor of the physiological sensor and/or an operator of central signal segregation station on the proper usage of the device/ modification within the device parameter for obtaining proper SNR.

For example, as shown with a data flow in <FIG>, signal quality manager <NUM> executes a signal quality processing of a physiological signal <NUM> based on the associated similarly measurement <NUM> or other signal identifier to yield a high quality check <NUM> or a low quality feedback <NUM> for database storage. Examples of the signal quality processing include, but are not limited to, a signal-to-noise quality measurement 170b and a fiducial point detection 170c. If physiological signal <NUM> exhibits low quality (e.g. high signal-to-noise ratio, failure to detect fiducials, etc.), then signal quality manager <NUM> executes a signal degradation analysis 170d involving a communication of low quality feedback signal <NUM> informative of the low quality nature of the physiological signal <NUM> with or without an identification of a determined reason for the signal degradation.

Referring back to <FIG>, annotation manager <NUM> is structurally configured to annotate specific regions of each identified high quality physiological signal having maximum diagnostic information. In practice, after an identified physiological signal has been denoted as have appreciable quality by signal quality manager <NUM>, annotation manager <NUM> identifies the relevant part of the signal for analysis.

More particularly, the present disclosure recognizes that continuous monitoring of a physiological signal provides repeated signal trends which typically may or may not have any significant diagnostic value. For example, for an ECG signal, when the trend is continuously monitored, the change in ST segment, or the periodic shift of fiducial locations are of primary importance for the analysis rather than entire signal. Annotation manager <NUM> therefore identifies the signal regions which are unique and carry potential information for the analysis using information gain and modelling approach. Also, if content information is received (e.g., geolocation and any accelerated motion), annotation manager <NUM> further annotates the activity being performed during the signal acquisition with the diagnostic region(s).

For example, as shown in <FIG>, if a quality check <NUM> is received for a physiological signal <NUM>, annotation manager <NUM> executes a diagnostic region annotation 180a involving a delineation of diagnostic region(s) (e.g., diagnostic region 131b) and non-diagnostic region(s) (e.g., non-diagnostic regions 131a and 131c) of physiological signal <NUM> of a signal cycle. The exemplary diagnostic region 131b may be identified based on historical understanding of a diagnostic nature of physiological signal <NUM> and/or the predictive elements of the associated physiologic signal model. Annotation manager <NUM> generates a diagnostic region annotation <NUM> for database storage appended with, if applicable, content aware information <NUM> occurring during a diagnostic region 131b.

Referring back to <FIG>, diagnostic analyzer <NUM> is structurally configured to perform a diagnosis of an identified high quality physiological signal of based on the associated diagnostic region annotation(s), particularly in view of any context aware information appended to diagnostic region annotation(s). In practice, after an annotation of an identified high quality physiological signal, diagnostic analyzer <NUM> extracts a trend among the physiological signals to correlate an observation to auto diagnose any pertinent ailments whereby a personalized care plan may be pushed to a user/monitor of the physiological device and/or an operator of the central signal segregation station.

For example, as shown in <FIG>, diagnostic analyzer <NUM> executes a trend diagnosis 190a of physiological signals <NUM> from an individual subject based on the associated diagnostic region annotations <NUM>. Any trend identification will be informative of any pertinent aliments and diagnostic analyzer <NUM> within the context aware information whereby a personalize care plan (PCC) may be communicated to a user/monitor of the physiological device and/or an operator of the central signal segregation station.

Referring to <FIG>, those having ordinary skill in the art will appreciate numerous benefits of the various embodiments of the present disclosure including, but not limited to, an independency of physiological sensor identifying information for signal monitoring and/or analysis of physiological signals by central signal segregation stations and methods.

Furthermore, as one having ordinary skill in the art will appreciate in view of the teachings provided herein, features, elements, components, etc. described in the present disclosure/specification and/or depicted in the Figures may be implemented in various combinations of electronic components/circuitry, hardware, executable software and executable firmware and provide functions which may be combined in a single element or multiple elements. For example, the functions of the various features, elements, components, etc. shown/illustrated/depicted in the Figures can be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions can be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which can be shared and/or multiplexed. Moreover, explicit use of the term "processor" should not be construed to refer exclusively to hardware capable of executing software, and can implicitly include, without limitation, digital signal processor ("DSP") hardware, memory (e.g., read only memory ("ROM") for storing software, random access memory ("RAM"), non-volatile storage, etc.) and virtually any means and/or machine (including hardware, software, firmware, circuitry, combinations thereof, etc.) which is capable of (and/or configurable) to perform and/or control a process. Further, the term "processor" will be understood to encompass various types of hardware such as microprocessors, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), and other hardware capable of performing the functions described herein. Further, as used herein, the term "non-transitory medium" will be understood to encompass both volatile memories (e.g., DRAM, SRAM, etc.) and nonvolatile memories (e.g., flash, magnetic, and optical storage) but to exclude transitory signals.

Moreover, all statements herein reciting principles, aspects, and embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (e.g., any elements developed that can perform the same or substantially similar function, regardless of structure). Thus, for example, it will be appreciated by one having ordinary skill in the art in view of the teachings provided herein that any block diagrams presented herein can represent conceptual views of illustrative system components and/or circuitry embodying the principles described herein. Similarly, one having ordinary skill in the art should appreciate in view of the teachings provided herein that any flow charts, flow diagrams and the like can represent various processes which can be substantially represented in computer readable storage media and so executed by a computer, processor or other device with processing capabilities, whether or not such computer or processor is explicitly shown.

Furthermore, exemplary embodiments of the present disclosure can take the form of a computer program product or application module accessible from a computer-usable and/or computer-readable storage medium providing program code and/or instructions for use by or in connection with, e.g., a computer or any instruction execution system. In accordance with the present disclosure, a computer-usable or computer readable storage medium can be any apparatus that can, e.g., include, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus or device. Such exemplary medium can be, e.g., an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include, e.g., a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), flash (drive), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk - read only memory (CD-ROM), compact disk - read/write (CD-R/W) and DVD. Further, it should be understood that any new computer-readable medium which may hereafter be developed should also be considered as computer-readable medium as may be used or referred to in accordance with exemplary embodiments of the present disclosure and disclosure.

Claim 1:
A central signal segregation station (<NUM>), comprising:
a signal acquisition controller (<NUM>) structurally configured to receive a plurality of different types of physiological signals from a plurality of unknown physiological sensors, wherein the unknown physiological sensors comprise mobile and immobile physiological sensors excluding source identifying information within a communication protocol implemented by the physiological sensors (<NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>; <NUM>); and
a signal segregation controller (<NUM>) in communication with the signal acquisition controller (<NUM>), characterized in that:
responsive to the signal acquisition controller (<NUM>) receiving the plurality of different physiological signals, the signal segregation controller (<NUM>) is structurally configured to control an identification of a particular type of each physiological signal based on distinct signal features of each physiological signal corresponding to a particular physiological signal model (<NUM>) among a plurality of physiological signal models (<NUM>) derived from known types of physiological sensors,
wherein the physiological signal models (<NUM>) comprise models for estimating and provide for an estimation of a probability of a particular type of a physiological signal based on one or more signal features of the physiological signal,
wherein the signal features comprise distinctive features of the physiological signal facilitating an identification of the physiological signal, and
wherein the signal segregation controller (<NUM>) is further structurally configured to determine a similarity measurement (<NUM>) of each physiological signal to each physiological signal model, wherein the physiological signal is deemed to correspond to the physiological signal model (<NUM>) providing the higher similarity measurement (<NUM>).