Abstract:
Embodiments of the present disclosure include a blink monitor for detecting blink occurrence in a living subject. For example, a blink sensor comprising a snap or tab electrode is positioned over one or more eye muscles known to assist in closing the eye. The electrode detects the electrical current in the muscle(s) and transmits a signal representative of the electrical current to a signal processing device. The device processes the signal to determine the occurrence of a blink, thereby producing an accurate blink electromyogram (EMG). The device and/or a caregiver may advantageously monitor the blink EMG, before and/or after occurrence processing, to determine the onset or actual occurrence of a patient condition. In an embodiment, the device monitors the blink EMG to determine the onset or occurrence of drowsiness in, for example, a driver, pilot, captain, doctor, soldier, or the like. In an embodiment, a caregiver monitors the blink EMG for one or more muscles in one or both eyes to determine the onset or occurrence of eye disease, such as, for example, strabismus.

Description:
PRIORITY CLAIMS  
       [0001]     The present application claims a priority benefit of U.S. Provisional Pat. App. Ser. Nos. 60/614,208, filed Sep. 29, 2004, entitled “Apparatus and Method for Determining Drowsiness in a Living Subject,” and 60/646,045, filed Jan. 20, 2005, entitled “Apparatus and Method for Ophthalmology Monitoring in a Living Subject.” The foregoing disclosure is expressly incorporated herein by reference. 
     
    
     BACKGROUND OF THE INVENTION  
       [0002]     1. Field of the Invention  
         [0003]     The present disclosure relates to the field of patient or driver monitoring. More specifically, the invention relates to the acquisition and processing of a blink electromyogram (EMG).  
         [0004]     2. Description of the Related Art  
         [0005]     The onset and occurrence of drowsiness has been long a problem for drivers. Studies indicate that fatigue could be a significant factor in the 100,000 U.S. commercial/passenger vehicle crashes per year. For example, some estimate that fatigue is a factor in approximately fifteen percent of fatal large truck-related crashes. In a 2005 study conducted by the Insurance Institute of Highway Safety, forty two percent (42%) of large truck drivers drove while sleepy during the previous week before being surveyed.  
         [0006]     To combat this problem, the automobile industry developed several technologies to attempt to detect errant driving behavior. For example, lane departure systems, such as that disclosed in U.S. Pat. No. 6,930,593, often include a camera mounted near the interior rear view mirror that views the road ahead and detects lane markings. When the system determines that the vehicle is drifting out of its lane, a warning buzzer sounds and an icon may flash on the instrument panel. Embodiments of these types of systems are found at least in various Infiniti M sedans and were in part developed by Valeo and Iteris. However, lane departure systems usually require minimum speeds and a view of well marked lane markings. These system often struggle on roads with worn paint and roads covered with dirt, snow, rain, or the like, or in actual storm or wind conditions. Moreover, such systems often confuse desired lane changes, inattentive driving, and drowsiness.  
         [0007]     Other systems turn the camera on the driver. Originally, because image processing of the face was difficult, Carnegie Mellon researcher Richard Grace transmitted a wavelength of light to a driver&#39;s eye from the dashboard, and received the reflected light as disclosed in U.S. Pat. No. 6,082,858. Grace focused on monitoring the percentage of time a driver&#39;s eye is closed, or PERCLOS. The PERCLOS monitor alarms when it detects a PERCLOS pattern that Grace recognizes as being associated with a driver getting drowsy. However, such cameras include significant drawbacks of being unusable and/or inaccurate when a driver wears sunglasses, subject to significant errors, and subject to interference when objects are placed between the emitter or lens and the face of the driver.  
         [0008]     In an attempt to correlate a physiologic signal with drowsiness, Ulrika Svensson explored the use of the electrooculogram (EOG) in her thesis at the University of Linkoping. In general, the EOG comprises a record of the standing voltage of the retina, the layers of cells at the back of the eye that conduct vision processing. The EOG is correlated with eyeball movement and obtained by electrodes placed on the skin above and below, or left and right of, the eye. In Svensson&#39;s drowsiness system, the use of two sensors complicate blink monitor processing through the addition of noise. Further, because Svensson employs primitive curve fit-, threshold-type algorithms for detection, the system has difficulty accommodating motion artifact or a large range of EOG morphologies.  
       SUMMARY OF THE INVENTION  
       [0009]     Accordingly, a need exists for a reliable blink sensor monitor for detecting blink occurrences in a living subject, such as a driver or a patient. Therefore, the present disclosure includes embodiments of systems and methods for acquiring and analyzing an accurate blink electromyogram (EMG). For example, snap or tab electrodes may advantageously be positioned over one or more muscles of one or both eyes, where the muscles are known to assist in closing the eye. The electrodes detect electrical current in the muscle(s) and transmits signal(s) representative of the electrical current to a signal processing device. The device processes the signal to determine the occurrence of a blink.  
         [0010]     The signal processing device and/or a caregiver may advantageously monitor the short or long trending of the blink EMG, before and/or after the foregoing blink occurrence processing, to determine the onset or actual occurrence of a patient condition. In an embodiment, the device monitors the blink EMG to determine the onset or occurrence of drowsiness in, for example, people who need to be attentive, such as, for example, a driver, pilot, captain, doctor, soldier, or the like. In another embodiment, the device monitors the blink EMG for one or more muscles in one or both eyes to determine the onset or occurrence of eye diseases, such as, for example, strabismus (more commonly known as crossed-eyes where a person can not align both eyes simultaneously under normal conditions).  
         [0011]     Based on the foregoing, an aspect of the disclosure includes an apparatus for measuring the drowsiness of a living subject. For example, the apparatus comprises a system having one or two electrode pairs, an amplifier for scaling the measured voltage between each electrode pair, an analog-to-digital converter for digitizing the voltage, a transmitter or transceiver for sending the digital voltage, a miniature battery or other power source for powering these components, and a receiver for sensing the amplified voltage. The individual snap or tab electrodes in each electrode pair are spaced using a predetermined distance, such as, for example zero (0) to two (2) or more cm, preferably one (1) cm. Use of this predetermined distance may advantageously provide a stable range of measured voltages across a patient population. Moreover, use of the range provides that when a voltage is measured outside this range, it may advantageously be determined that the electrode pair is disconnected from the driver or patient. The electrode pairs may advantageously be placed at locations below the lower eyelash of the subject, on both the right and left sides, or the like. The transmitter/receiver pair operate at a convenient frequency, such as one within the Industrial Scientific and Medical band, or other acceptable bands recognizable to one of ordinary skill in the art from the disclosure herein.  
         [0012]     Another aspect of the disclosure includes a method of monitoring drowsiness of a living subject. The method generally comprises analyzing one, both separately, the mean, or other advantageous combination of the two blink sensor signals to determine drowsiness. In an embodiment, discrete wavelet transforms can advantageously be used to identify the onset of each blink and drowsiness parameters of each blink. The drowsiness parameters may be input to a fuzzy model to determine when drowsiness is present. In one exemplary embodiment, Haar detail coefficients can advantageously be used to isolate blink onset, generally characterized by high frequency blinking, and Haar approximation coefficients can be used to isolate drowsy blink-blink intervals, generally characterized by different low frequency content than alert blink-blink intervals. The decision as to whether the interval is associated with awake or drowsy can be made using a linguistic fuzzy model.  
         [0013]     Another aspect of the disclosure includes a drowsiness module adapted to determine drowsiness. The module may advantageously comprise a receiver adapted to sense blink sensor signals, processing circuitry that outputs a drowsiness decision, and an interface to a host or monitoring device. In one exemplary embodiment, the module of the present invention comprises a receiver that sends blink sensor signals to a processor-based system, including program and data memory capable of implementing processing algorithms, such as, for example, those described herein. In an embodiment, the drowsiness decision can be communicated via a standard hospital communication protocol, customized manufacturer protocol, or the like to a hospital patient monitor. In another embodiment, a receiver sends blink sensor signals to a processor-based system, including program and data memory capable of communication via a cellular standard protocol to an emergency system. Yet another embodiment of the drowsiness module includes an EMG module apparatus, comprising a receiver, digital processor-based system, and an interface. The EMG module apparatus can advantageously be configured to operate in conjunction with an intelligent vehicle system. In yet another embodiment of the drowsiness module, an EMG module comprises a plug-in module that communicates with a host device such as a personal computer or dedicated display.  
         [0014]     Yet another aspect of the disclosure includes a drowsiness monitor that interfaces to other devices. For example, a standalone drowsiness monitor comprises a receiver that sends received blink sensor signals to a digital processor-based device adapted to process EMG and other signals derived from one or more living subjects. In response to the drowsiness decision, the monitor advantageously outputs signals and alarms to its own display, the display of supervising personnel, emergency services, or the like.  
         [0015]     Yet another aspect of the disclosure includes a software environment adapted for use with the aforementioned drowsiness monitor or module. In one exemplary embodiment, the software environment includes initialization, operating, and processing modules adapted to instruct a processing device to perform various boot-up, signal processing, communication, and error detection functions.  
         [0016]     Yet another aspect of the disclosure includes a method of determining ophthalmology disorders in a living subject. For example, the method generally comprises analyzing blink sensor signals indicative of the electrical activity in the muscles of both eyes to determine differences between the sensor signals. Discrete wavelet transforms can advantageously identify the onset of each blink or focus response and ophthalmology disorder parameters between each blink or focus response. These disorder parameters can be input into a fuzzy model to classify the disorder. In an embodiment, Haar detail coefficients can advantageously be used to isolate the onset of blink or focus response and onset timing differences, generally characterized by high frequency blinking, and Haar approximations coefficients can be used to isolate muscle stimulus intervals, which include different low frequency content. The parameters are input for determining strabismus, or eye misalignment. Strabismus affects about 3% of the U.S. population, and, if not treated in infants, may lead to disabling double vision, loss of depth perception, and visual loss. Although the mechanism for strabismus is under review, various opinions hold that abnormalities of the extraocular muscles cause the eyes to move in abnormal directions. These abnormalities may be distinguishable in the blink EMG. The decision for ophthalmology disorder classification can be made using a linguistic fuzzy model.  
         [0017]     Yet another aspect of the disclosure includes an ophthalmology module comprising a receiver that sends received blink sensor signals to a processor-based device adapted to process blink and other signals derived from one or more living subjects. In response to ophthalmology disorder classification, the module outputs signals and alarms to a display. The module also may advantageously communicate to, for example, a centralized accounting system. The communication may advantageously be over one or more communication networks including computer, telephone, and cellular networks. In an embodiment, the module informs the accounting system of information desired by the same, such as, for example, a number of disorders classification attempts, patient information and data, or the like. Thus, the module may advantageously be adapted for fee-per-use or fee-per-function. In an embodiment, the determination of strabismus is performed by the module. In other embodiments, the module in concert with a caregiver determines the onset, occurrence, and/or severity of strabismus or other disorder.  
         [0018]     Further aspects of the disclosure include a method of determining a patient condition including at least one of drowsiness and ocular disorder. The method comprises receiving at least one signal from a sensor capable of detecting electrical activity in one or more muscles in a body of a person, the electrical activity being indicative of a blink occurrence, determining which data from the at least one signal represents the blink occurrence, and evaluating the blink occurrence data to determine an onset or actual occurrence of a patient condition, wherein the patient condition includes at least one of drowsiness and an ocular disorder. Moreover, an aspect of the disclosure includes a device for identifying a blink occurrence within a blink electromyogram (EMG). The device comprises an electrode pair configured to be positioned with respect to one or more muscles used to blink at least one eye in order to output a signal indicative of a changing biopotential associated with the one or more muscles and a processing device configured to receive the signal and identify blink occurrences within the signal.  
         [0019]     Yet another aspect includes a blink monitoring system comprising a sensor comprising an electrode pair capable of outputting a signal indicative of a biopotential related to blink occurrences, a transmitting device capable of wirelessly transmitting the signal, and a flexible circuit housing the electrode and the transmitting device. The system further comprises a monitor capable of determining where the blink occurrences appear in the signal. Other aspects include a monitor for monitoring the alertness of a person comprising a receiver configured to receive data indicative of a blink electromyogram (EMG) of a person, a processor configured to determine blink occurrences within the blink EMG and evaluate the blink occurrences to determine an onset or occurrence of diminished alertness, and an alarm signal capable of activating an alarm configured to rectify the diminished alertness in the person.  
         [0020]     For purposes of summarizing the invention, certain aspects, advantages and novel features of the invention have been described herein. Of course, it is to be understood that not necessarily all such aspects, advantages or features will be embodied in any particular embodiment of the invention. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0021]     The following drawings and the associated descriptions are provided to illustrate embodiments of the present disclosure and do not limit the scope of the claims.  
         [0022]      FIG. 1  illustrates an exemplary block diagram of a blink monitoring system according an embodiment of the disclosure.  
         [0023]      FIG. 2  illustrates an exemplary flow chart of a blink detection process performed using the blink monitoring system of  FIG. 1 , according to an embodiment of the disclosure.  
         [0024]      FIG. 3  illustrates an exemplary block diagram of an application of a blink sensor according an embodiment of the disclosure.  
         [0025]      FIG. 4  illustrates an exemplary block diagram of a blink monitoring system, including an embodiment of the blink sensor of  FIG. 1 , according an embodiment of the disclosure.  
         [0026]      FIG. 5  illustrates an exemplary block diagram of a blink monitoring system, including an embodiment of the signal processor of  FIG. 1 , according an embodiment of the disclosure.  
         [0027]      FIG. 6  illustrates an exemplary block diagram of a blink monitor, according an embodiment of the disclosure.  
         [0028]      FIG. 7 ( a ) illustrates a graph of a typical ECG signal.  
         [0029]      FIG. 7 ( b ) illustrates a graph of a blink EMG signal obtained from a subject in an alert state, according to an embodiment of the disclosure.  
         [0030]      FIG. 7 ( c ) illustrates a graph of a blink EMG signal obtained from the same subject, but in a drowsy state, according to an embodiment of the disclosure. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0031]     Embodiments of the present disclosure include a blink monitoring system for detecting blink occurrences in a living subject. For example, a blink sensor communicates to a blink monitor a signal indicative of electrical current detected in one or more muscles of the eye. The blink monitor processes the signal to determine a blink occurrence. Moreover, the blink monitor and/or a caregiver may advantageously monitor a long or short trend of blink occurrences to determine the onset or actual occurrence of a patient condition. In an embodiment, the blink monitor monitors the blink occurrences to determine the onset or occurrence of drowsiness in, for example, a driver. In another embodiment, the blink monitor monitors the blink occurrences to determine the onset or occurrence of eye disorder, such as, for example, strabismus.  
         [0032]     As used herein, the term “processor” and “digital processor” are meant to have their ordinary broad meaning to an artisan, including, for example, digital signal processors (DSPs), reduced instruction set computers (RISC), general-purpose (CISC) processors, microprocessors, application-specific integrated circuits (ASICs), or the like. Such digital processors may be contained on a single unitary IC die, or distributed across multiple components.  
         [0033]     As used herein, the terms “monitor” and “monitoring device” are meant to have their ordinary broad meaning to an artisan, including, for example, generally to refer to devices adapted to perform monitoring, display, user interface, and/or control functions. Such devices may be dedicated to a particular function, or multi-purpose devices adaptable to performing a variety of functions and/or interfacing with a number of functional modules.  
         [0034]     As used herein, the term “electromyogram” or “EMG” are meant to have their ordinary broad meaning to an artisan, including, a record of the electrical activity of muscles. For example, when muscles are active, they produce an electrical current that is generally proportional to the level of the muscle activity. EMGs have generally been employed in the medical industry to detect abnormal muscle electrical activity that can occur in patients having various diseases and conditions, including muscular dystrophy, inflammation of muscles, pinched nerves, peripheral nerve damage (damage to nerves in the arms and legs), amyotrophic lateral sclerosis (ALS) (also known as Lou Gehrig disease), myasthenia gravis, disc herniation, and others. EMGs are generally acquired through surface electrodes placed on (not into) the skin overlying the muscle to detect the electrical activity of the muscle. Intramuscular EMGs (the most commonly used type) involve inserting a needle electrode through the skin into the muscle to detect the electrical activity therein.  
         [0035]     To facilitate a complete understanding of the invention, the remainder of the detailed description describes the invention with reference to the drawings.  
         [0036]      FIG. 1  illustrates an exemplary block diagram of a blink monitoring system  100  according an embodiment of the disclosure. The system  100  includes a blink sensor  102  communicating one or more signals indicative of electrical activity of one or more muscles usable to close the eye, usable to indicate an eye closure, or the like, to a signal processor  104 . In an embodiment the blink sensor  102  comprises one or more snap or tab electrode pairs capable of measuring a voltage between the terminals of the pair, although a skilled artisan may recognize from the disclosure herein a variety of potential blink sensors capable of providing the one or more signals to the monitor  104 . In an embodiment, gels may be used to assist in the conductivity between the skin and the electrode pair(s). In an embodiment, the blink sensor  102  wirelessly communicates with the signal processor  104 .  
         [0037]     The signal processor  104  comprises a processor capable of performing signal processing methodologies on the input signal data. As will be understood by an artisan from the disclosure herein, the processor  104  may include program and data memory, may access computer readable memory, or the like to execute one or more software modules, instruction sets, programs or the like. In an embodiment, the processor executes instructions for wavelet and fuzzy model processing on the signal(s) received from the blink sensor  102  to determine blink occurrences from within the signal(s). The signal processor may output a variety of advantageous information, including, for example, a signal indicative of the received signal(s), one or more of the received signals, the blink occurrences in the form of an accurate blink EMG, a determination of the onset or occurrence of drowsiness or optical disorders, or the like.  
         [0038]      FIG. 1  also shows the processor  104  communicating with an alarm  106 . Upon determination of the onset or occurrence of a patient condition, the processor  104  may advantageously instruct the alarm  106  to provide an audio or visual indication of the detected condition, a control instruction, or the like. For example, the alarm  106  may include an audio and/or visual signal to a driver that he or she is showing signs of drowsiness, that he or she should no longer drive, or the like. The alarm  106  may advantageously contact a control station, management personnel, emergency personnel, police, or the like, to encourage the driver to rest. Moreover, the alarm  106  may be part of a multi-parameter patient monitoring system.  
         [0039]      FIG. 2  illustrates an exemplary flow chart of a blink detection process  200  performed using the blink monitoring system  100  of  FIG. 1 , according to an embodiment of the disclosure. As shown in  FIG. 2 , the process  200  comprises acquiring a signal indicative of eye muscle activity (block  202 ), processing the signal to identify one or more blink occurrences (block  204 ), analyzing the occurrences to detect the onset or occurrence of a patient condition (block  206 ), and outputting a result of the analysis (block  208 ). In an embodiment discussed in further detail below, the processing of the signal includes wavelet filtering and scaling, and a fuzzy model to identify signal segments that correspond to blink occurrences.  
         [0040]      FIG. 3  illustrates an exemplary block diagram of an application of a blink sensor  302  according an embodiment of the disclosure. As shown in  FIG. 3 , the blink sensor  303  comprising a tab electrode may be placed beneath a lower eyelash and around the eye socket area of the right or left eye. As shown in the exploded view, the tab sensor  302  may include two terminals of two electrodes  304 ,  306 , electrically communicating with a processor or controller device  308 . In an embodiment, the processor  308  conditions the biopotential signal detected across the electrodes  304 ,  306 . For example, the processor  308  may perform some or all of the following signal conditioning: amplification, noise filtering, and conversion to digital data. In an embodiment, the processor  308  wireless communicates the digitized signal to a pickup receiver.  
         [0041]     In an embodiment, the electrodes in each electrode pair are spaced using a predetermined distance, such as, for example about zero (0) to about two (2) or more cm. In a preferable embodiment, the electrode pair are spaced about one (1) cm from one another. Use of this predetermined distance advantageously provides a stable range of measured voltages across a patient population. Moreover, use of the range provides that when a voltage is measured outside this range, it may advantageously be determined that the electrode pair is disconnected from the driver or patient.  
         [0042]     Use of a comparable snap electrode has been shown to produce signal data roughly comparable in quality and amplitude as more typically electrode-monitored muscles. For example,  FIG. 7 ( a ) illustrates a graph of a typical ECG signal. As shown in  FIG. 7 ( a ), the ECG signal shows a strong heartbeat with an excursion range of about +/− five (5) mV. As shown, the heartbeat reaches a positive amplitude of approximately four (4) mV. As a favorable comparison illustrating the viability of a tab or snap electrode for monitoring eye muscles,  FIG. 7 ( b ) illustrates a graph of a blink EMG signal from an eye muscle according to an embodiment of the disclosure exhibiting a similar excursion range of about +/− five (5) mV. In particular, the blink EMG includes a positive amplitude of about three (3) mV.  
         [0043]      FIG. 4  illustrates an exemplary block diagram of the blink monitoring system  400 , including an embodiment of the blink sensor  102  of  FIG. 1 , according an embodiment of the disclosure. As shown in  FIG. 4 , the sensor  102  includes an electrode pair  404  communicating with an amplifier and/or filter  406 , an analog-to-digital converter  408 , and in a wireless embodiment, a transmitter  410 . The amplifier  406 , converter  408  and transmitter  410  may advantageously be powered by power source  412 , such as, for example, a battery.  
         [0044]     In an embodiment, the electrode pair  404 , located beneath the lower eyelash of the right or left eye (although an artisan will recognize from the disclosure herein other locations may be substituted with equivalent/acceptable success) comprises a typical snap- or tab-style electrode. The electrode pair  404  detect an EMG voltage resulting from local muscle activity present during blinking  402 . Using a predetermined distance between electrode terminals provides a stable range of measured voltages across a patient population. When a voltage is measured outside this range, it can be determined that the electrode pair is disconnected from the patient.  
         [0045]     The gain of the EMG is increased using an amplifier, which may include the anti-aliasing lowpass filter  406 . The amplified EMG is then digitized using the A/D converter (ADC)  408 . The digitized signal is transmitted by the transmitter  410  at a convenient frequency, such as one within the Industrial Scientific and Medical band. In one embodiment, the amplifier  406 , A/C  408 , battery  412 , and transmitter  410  are co-located on the tab electrode  404  using a flex circuit recognizable to an artisan from the disclosure herein. The receiver  414  senses the digitized signal within an EMG monitor or module  
         [0046]     An artisan will recognize from the disclosure herein that the analog signal may be transmitted to the receiver  414 , that some signal processing may occur using electronics on the monitor, the electrode, combinations of the same, or the like.  
         [0047]      FIG. 5  illustrates an exemplary block diagram of a blink monitoring system  500 , including an embodiment of the signal processor  104  of  FIG. 1 , according an embodiment of the disclosure. As shown in  FIG. 5 , the signal processor  500  may include the receiver  414  providing the signal to a wavelet filter  502  and a scaling filter  506 . The wavelet filter  502  forwards the filtered signal to a spike detector  504 . The spike detector  504  and the scaling filter  506  forward signals to a blink-blink detector  508  which forwards the resulting signal to a fuzzy model analysis module  510 . The fuzzy model analysis module  510  outputs results to a display and/or alarm device  512 .  
         [0048]     In an embodiment, one, both, the mean, or other combination of two EMGs resulting from blinking are analyzed using discrete wavelet transforms. A “discrete wavelet transform” as used herein is meant to have its broad ordinary meaning to an artisan, including being a time-scale representation of an input signal that is obtained by convolving the signal with a wavelet or scaling filter at a particular scale. Various wavelet and scaling filters are utilized (as discussed below) to emphasize certain features of interest associated with the EMG waveforms obtained from electrodes positioned under the lower eyelash. The wavelet filter  502  acts as a highpass filter to obtain wavelet transform detail coefficients. The wavelet scaling filter  506  acts as a lowpass filter to obtain wavelet transform approximation coefficients. Convolution of the input signal(s) with the wavelet filter  502  identifies each EMG spike cluster, which represents the onset of a blink. The input signal(s) are also convolved with the scaling filter  506 . The resulting approximation coefficients are separated into blink-blink intervals, using the detected blink onsets. Detection of characteristic low frequency morphologies by the blink-blink detector  508  leads to the output of drowsiness parameters. These drowsiness parameters are analyzed using the fuzzy model  510 . A “fuzzy model” as used herein is meant to have its broad ordinary meaning to an artisan and including reference to a nonlinear, time-invariant system operator that is sufficiently complex that it cannot be easily summarized with conventional mathematical equations. Fuzzy models are typically used for pattern recognition, modeling, and prediction. Based on the current values of drowsiness parameters, each blink-blink interval is judged by the fuzzy model as being as either aware or drowsy. An artisan will recognize that clinical data will advantageously assist in determining the best or most appropriate particular input membership functions, rule base inference, and output membership functions. The clinical data may advantageously include EMG patterns determined to correspond to particular patient conditions, including the onset or occurrence of drowsiness or ophthalmology disorders. For example,  FIG. 7 ( b ) illustrates a blink EMG obtained from an alert subject, while  FIG. 7 ( c ) illustrates a blink EMG from the same subject, but in a drowsy state  
         [0049]     In one exemplary embodiment, the wavelet filter  502  employs Haar detail coefficients to isolate blink onset, which is high frequency and shaped like one or more spikes. The filter  502  also employs Haar approximation coefficients to isolate drowsy blink-blink intervals. A linguistic fuzzy model then classifies the different low frequency content of alert versus drowsy blink-blink intervals. The low frequency distinctions between alert versus drowsy blink-blink intervals become apparent from analysis of substantial numbers (i.e., several hundred) of patient blink waveforms. Analysis of these blink waveforms will also determine the fuzzy model input membership functions, rule base inference, and output membership functions. For example, one set of input membership functions for the fuzzy model could be based on the global minimum of the Haar wavelet approximation coefficients, calculated between two blink onsets. In concert with a second set of input membership functions and a rule base inference table, a detection decision would be generated (on/off), based on one set of output membership functions. It will be recognized, however, that other types of wavelets (such as the family of biorthogonal or Daubechies wavelets) could be substituted for or used in conjunction with the Haar wavelets of the present embodiment, and other types of fuzzy models (such as the fuzzy relational model and Takagi-Sugeno model) could be substituted for or used in conjunction with the linguistic fuzzy model of the present embodiment. Furthermore, an absolute or other type of blink detection threshold could be specified if desired.  
         [0050]      FIG. 6  illustrates an exemplary block diagram of a blink monitor  600 , according an embodiment of the disclosure. As shown in  FIG. 6 , the blink monitor  600  includes the receiver  414 , a signal processor  604 , an interface  606  and a host monitor  609  including a display and/or alarm  610 . As discussed in the foregoing, the receiver  414  is capable of, in some embodiments, wirelessly receiving blink sensor signal(s) indicative of electrical impulses in muscles indicative of eye blinking. The receiver  414  sends the blink sensor signal(s) to processing circuitry  604 , which are capable of implementing the waveform processing software modules discussed with reference to  FIG. 5 . In an embodiment, a resulting drowsiness decision can be advantageously communicated via the interface  606  to the host monitor or monitoring device  608 . In an embodiment, the drowsiness decision output from the DSP  604  is communicated via a standard hospital communication protocol, customized manufacturer protocol, or the like, to a hospital patient monitor. In another embodiment, the drowsiness decision output from the DSP  604  is communicated via a standard cellular or other network protocol to a personal emergency system, such as OnStar, to an emergency facility, such as the local police or fire station, to a management facility, or the like.  
         [0051]     In yet another embodiment, the drowsiness decision output from the DSP  604  is communicated to an intelligent vehicle system. The drowsiness decision is important for monitoring driver vigilance/fatigue and driver distraction. For such commercial, military, or private vehicle systems, extreme drowsiness decision feedback will alert autonomous driving systems to avoid collisions and driver assistance systems to display alarms and other cues to increase driver awareness and/or inform management or emergency personnel. In another embodiment, the monitor  608  includes a plug-in module that communicates with a host device such as a personal computer, vehicle audio device, dedicated display, or the like.  
         [0052]     An artisan will recognize that the interface  606  and host monitor  608  in  FIG. 6  may be replaced by other components within a monitor, such as a standalone monitor. Rather than interfacing to other devices, the standalone monitor may comprise a receiver that sends received signals to a processor-based device adapted to process signals derived from one or more living subjects. In response to the drowsiness decision, the monitor outputs signals and alarms to its own display. In one exemplary embodiment, the standalone monitor mounts to a commercial, military, or private vehicle dashboard. When the monitor determines that the driver is becoming drowsy, is dangerously drowsy (i.e., an extreme drowsiness decision), or the like, the monitor loudly alarms, increases radio volume, activate vibration mechanisms in the vehicle seat, or the like in an effort to fully wake the driver.  
         [0053]     In yet another embodiment, the monitor  608  comprises an ophthalmology disorder monitor. In such embodiments, EMG signals from both eyes are compared using the discrete wavelet transforms discussed in the foregoing. Moreover, the signal processing can be chosen from discrete wavelet transforms to produce spike clusters representing the onset of a blink or focus response. Disorder parameters obtained from convolution of the left and right EMG with wavelet and scaling filters are analyzed using the fuzzy model module  510 . Based on current values of disorder parameters, time intervals are judged as being normal or representative of a least one disorder. For example, one set of input membership functions for the fuzzy model could be based on the similarity in the timing of the global minimum of the Haar wavelet approximation coefficients from both eyes, calculated between two blink onsets. In concert with a second set of input membership functions and a rule base inference table, a strabismus detection decision would be generated (on/off), based on one set of output membership functions. An artisan will recognize that clinical data will advantageously assist in determining the best or most appropriate particular values of the foregoing wavelets, input membership functions, rule base inference table, and output membership functions. The clinical data may advantageously include EMG patterns determined to correspond to particular patient conditions, including normals and the onset or occurrence of ophthalmology disorders.  
         [0054]     In various embodiments, the ophthalmology disorder monitor may advantageously communicate with an accounting system to provide fee based determination of disorders, to track patient data, to collect clinical data, or the like. In an embodiment, the disorder comprises strabismus.  
         [0055]     FIGS.  7 ( b )-( c ) illustrate graphs of a blink EMG signal obtained from a subject in an alert state ( FIG. 7 ( b )) and the same subject in a drowsy state ( FIG. 7 ( c )). As shown in  FIG. 7 ( c ), the blink EMGs include differing information that when evaluated, provide indicators of the alertness/drowsiness, ocular disorder or the like. For example, the duration of peaks, their frequency, their shape, the surrounding shapes, and the like all provide information usable to evaluate a condition of a person.  
         [0056]     Although the foregoing invention has been described in terms of certain preferred embodiments, other embodiments will be apparent to those of ordinary skill in the art from the disclosure herein. For example, it is noted that while the disclosure described an apparatus and method for determining drowsiness suitable for use under the lower eyelash of a human subject, the invention may also conceivably be embodied or adapted to monitor drowsiness or another parameter derived from a biopotential at other locations on the human body, as well as monitoring drowsiness or another parameter derived from a biopotential on other warm-blooded species. Moreover, the blink sensors may comprise other sensors capable of outputting electrical signals indicative of blink occurrences, such as, for example, piezoelectric sensors or the like.  
         [0057]     Additionally, other combinations, omissions, substitutions and modifications will be apparent to the skilled artisan in view of the disclosure herein. Accordingly, the present invention is not intended to be limited by the reaction of the preferred embodiments, but is to be defined by reference to the appended claims.  
         [0058]     Additionally, all publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.