Patent Publication Number: US-2022233080-A1

Title: Systems and methods for monitoring one or more physiological parameters using bio-impedance

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims benefit of U.S. provisional patent application Ser. No. 62/845,114 filed May 8, 2019, and entitled “Wirelessly Coupled Bio-Impedance Patches,” which is hereby incorporated herein by reference in its entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     Not applicable. 
     BACKGROUND 
     Prevention and treatment of many disorders may require sustainable and long-term health tracking. For instance, in the case of cardiovascular and respiratory diseases, the symptoms of many complications may be observed through anomalies in the hemodynamic (e.g., heart rate (HR), heart rate variability, blood pressure (BP), respiration rate (RR), etc.) and respiratory physiological parameters (e.g., respiration rate, pulmonary volumes) of a patient. Continuous and reliable monitoring of a patient&#39;s physiological parameters may allow for the early diagnosis, management, or even prevention of disease. 
     A variety of techniques may be employed for monitoring one or more physiological parameters of a patient. For example, nocturnal polysomnography (PSG), which monitors chest wall and upper abdominal wall movements through piezoelectric sensors, and capnography, which monitors CO 2  and O 2  gas exchanges using nasal sensors or face masks, are commonly used techniques for respiration monitoring. Additionally, wearable systems may be used for monitoring HR and other hemodynamic parameters through electrocardiogram (ECG) signals, which are obtained electrodes placed on the patient&#39;s skin that monitor electrical activity of the heart, or photoplethysmography (PPG) signals, which are obtained through measuring the intensity of reflected and/or transmitted light applied to the patient&#39;s skin. Bio-impedance is another technique which may be used to monitor a patient&#39;s hemodynamic and/or physiological parameters. Techniques employing bio-impedance (“Bio-Z”) may include stimulating a patient&#39;s epidermis with alternating electrical current (AC) and measuring a voltage difference induced by the AC stimulation across two points on the patient&#39;s body. 
     BRIEF SUMMARY OF THE DISCLOSURE 
     An embodiment of a system for monitoring one or more physiological parameters comprises a plurality of patches positionable at a plurality of locations on a surface of a body, wherein the plurality of patches are electrically isolated from each other, wherein a first patch of the plurality of patches comprises a transmitter configured to inject an electrical signal into the body at a first location on the surface of the body, and wherein a second patch of the plurality of patches comprises a sensor configured to detect a bio-impedance signal at a second location on the surface of the body that is spaced from the first location, and wherein the bio-impedance signal is induced within the body by the electrical signal injected from the first patch which is electrically isolated from the second patch. In some embodiments, the transmitter of the first patch is configured to inject an alternating current at a first frequency, the sensor of the second patch is configured to detect the bio-impedance signal from a voltage induced by the injected alternating current, and the second patch comprises a transmitter configured to inject an alternating current at a second frequency that is different from the first frequency. In some embodiments, the first patch comprises sensor configured to detect a bio-impedance signal from the body that is induced by the alternating currents injected from the first patch and the second patch. In certain embodiments, the system further comprises a controller coupled to the second patch and configured to estimate the one or more physiological parameters based on the bio-impedance signal. In certain embodiments, the controller is configured to adjust at least one of a frequency, an amplitude, and a shape of the electrical signal injected by the transmitter. In some embodiments, at least one of the one or more physiological parameters estimated by the controller corresponds to at least one of heart activity and respiratory activity of a patient. In some embodiments, the one or more physiological parameters comprises a plurality of physiological parameters, and wherein the controller is configured to apply a source separation algorithm on the bio-impedance signal to separate a first physiological parameter from the plurality of physiological parameters. In certain embodiments, the controller is configured to apply a demodulation algorithm to the bio-impedance signal prior to the application of the source separation algorithm to the bio-impedance signal. In certain embodiments, each of the plurality of patches are independently grounded to the body. In some embodiments, each of the plurality of patches comprises a ground electrode in electrical contact with the surface of the body. In some embodiments, at least one of the plurality of patches comprises a sensors configured to independently detect a bio-impedance signal from the body induced by the electrical signal injected from the second patch. In certain embodiments, the transmitter of the first patch is configured to inject an alternating voltage at a first frequency, the sensor of the second patch is configured to detect the bio-impedance signal from a current induced by the injected alternating voltage, the second patch comprises a transmitter configured to inject an alternating voltage at a second frequency that is different from the first frequency, and the first patch comprises sensor configured to detect a bio-impedance signal from the body that is induced by the alternating voltages injected from the first patch and the second patch. 
     An embodiment of a system for monitoring one or more physiological parameters comprises a plurality of patches configured to releasably couple with a surface of a body at a plurality of locations, wherein each of the plurality of patches are independently electrically grounded to the body, wherein a first patch of the plurality of patches comprises a transmitter configured to inject an electrical signal into the body at a first location on the surface of the body, and wherein a second patch of the plurality of patches comprises a sensor configured to detect a bio-impedance signal at a second location on the surface of the body that is spaced from the first location, and wherein the bio-impedance signal is induced within the body by the electrical signal injected from the first patch. In some embodiments, each of the plurality of patches comprises a ground electrode in electrical contact with the surface of the body. In some embodiments, the transmitter of the first patch is configured to inject an alternating current at a first frequency, the sensor of the second patch is configured to detect the bio-impedance signal from a voltage induced by the injected alternating current, the second patch comprises a transmitter configured to inject an alternating current at a second frequency that is different from the first frequency, and the first patch comprises sensor configured to detect a bio-impedance signal from the body that is induced by the alternating currents injected from the first patch and the second patch. In certain embodiments, the system further comprises a controller coupled to the second patch and configured to estimate the one or more physiological parameters based on the bio-impedance signal, wherein the one or more physiological parameters comprises a plurality of physiological parameters, and wherein the controller is configured to apply a source separation algorithm on the bio-impedance signal to separate a first physiological parameter from the plurality of physiological parameters. In certain embodiments, the controller is configured to adjust at least one of a frequency, an amplitude, and a shape of the electrical signal injected by the transmitter. 
     An embodiment of a method for monitoring one or more physiological parameters comprises (a) injecting an electrical signal into a body with a first patch positioned at a first location on a surface of the body, and (b) detecting a bio-impedance signal induced within the body by the injected electrical signal with a second patch positioned at a second location on the surface of the body and that is electrically isolated from the first patch, wherein the second location is spaced from the first location. In some embodiments, the method further comprises (c) independently electrically grounding the first patch and the second patch to the surface of the body. In some embodiments, the method further comprises (c) estimating the one or more physiological parameters based on the detected bio-impedance signal. 
     Embodiments described herein comprise a combination of features and characteristics intended to address various shortcomings associated with certain prior devices, systems, and methods. The foregoing has outlined rather broadly the features and technical characteristics of the disclosed embodiments in order that the detailed description that follows may be better understood. The various characteristics and features described above, as well as others, will be readily apparent to those skilled in the art upon reading the following detailed description, and by referring to the accompanying drawings. It should be appreciated that the conception and the specific embodiments disclosed may be readily utilized as a basis for modifying or designing other structures for carrying out the same purposes as the disclosed embodiments. It should also be realized that such equivalent constructions do not depart from the spirit and scope of the principles disclosed herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For a detailed description of exemplary embodiments of the disclosure, reference will now be made to the accompanying drawings in which: 
         FIG. 1  is a schematic representation of a resistor-capacitor circuit tissue model, 
         FIG. 2  is a schematic representation of an impedance mapping model; 
         FIGS. 3-5  are graphs illustrating data obtained from a simulation conducted using the impedance mapping model of  FIG. 2 ; 
         FIG. 6  is a schematic representation of an embodiment of a system for monitoring physiological parameters in accordance with principles disclosed herein; 
         FIG. 7  is a schematic representation of an embodiment of a Bio-Z detecting patch of the system of  FIG. 6  in accordance with principles disclosed herein; 
         FIG. 8  is a schematic representation of an embodiment of a signal injector unit of the patch of  FIG. 7  according to some embodiments; 
         FIG. 9  is a schematic representation of an embodiment of a parameter extraction unit of the patch of  FIG. 7  according to some embodiments; 
         FIG. 10  is a block diagram of an embodiment of a digital signal processing algorithm performable by the parameter extraction unit of  FIG. 9  according to some embodiments; and 
         FIGS. 11-25  are graphs pertaining to data obtained from experiments of Bio-Z detecting patches. 
     
    
    
     DETAILED DESCRIPTION 
     The following discussion is directed to various exemplary embodiments. However, one of ordinary skill in the art will understand that the examples disclosed herein have broad application, and that the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to suggest that the scope of the disclosure, including the claims, is limited to that embodiment. 
     The drawing figures are not necessarily to scale. Certain features and components herein may be shown exaggerated in scale or in somewhat schematic form and some details of conventional elements may not be shown in the interest of clarity and conciseness. In addition, unless the context dictates the contrary, all ranges set forth herein should be interpreted as being inclusive of their endpoints, and open-ended ranges should be interpreted to include only commercially practical values. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary. 
     In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . .” Also, the term “couple” or “couples” is intended to mean either an indirect or direct connection. Thus, if a first device couples to a second device, that connection may be through a direct connection of the two devices, or through an indirect connection that is established via other devices, components, nodes, and connections. In addition, as used herein, the terms “axial” and “axially” generally mean along or parallel to a given axis (e.g., central axis of a body or a port), while the terms “radial” and “radially” generally mean perpendicular to the given axis. For instance, an axial distance refers to a distance measured along or parallel to the axis, and a radial distance means a distance measured perpendicular to the axis. Further, when used herein (including in the claims), the words “about,” “generally,” “substantially,” “approximately,” and the like mean within a range of plus or minus 10%. The following discussion is directed to various exemplary embodiments. However, one skilled in the art will understand that the examples disclosed herein have broad application, and that the discussion of any embodiment is meant only to be exemplary of that embodiment, and not intended to suggest that the scope of the disclosure, including the claims, is limited to that embodiment. 
     As described above, various techniques may be employed to monitor physiological parameters of a patient. Some techniques for monitoring physiological parameters are obtrusive to the patient. For instance, techniques that rely on PSG signals may require the usage of a belt that is securely attached to the patient, which may result in significant discomfort to the patient. As another example, capnography may require the patient to wear multiple nasal sensors or a face mask that also result in significant discomfort to the patient. 
     Additionally, wearable systems intended to provide an unobtrusive experience for the patient may have limited reliability in monitoring varying types of physiological parameters. For instance, PPG signals may have limited reliability for monitoring RR given that the mixing effect between the PPG signal and respiratory-induced variations of the vessels (relied on for monitoring RR using PPG signals) may be weak and dependent on skin thickness, temperature, sensor location, and other parameters. ECG-derived respiratory monitoring may also suffer from limited reliability. 
     Accordingly, embodiments disclosed herein include systems and methods for monitoring one or more physiological parameters using Bio-Z. Particularly, embodiments disclosed herein include systems for monitoring one or more physiological parameters that include a plurality of patches positionable at a plurality of locations on a surface of a body, wherein the plurality of patches are electrically isolated from each other. Additionally, each patch of the plurality of patches may be independently electrically grounded to the body. A first patch of the plurality of patches may comprise a transmitter configured to inject an electrical signal into the body at a first location on the surface of the body, and a second patch of the plurality of patches may be configured to detect a bio-impedance signal at a second location on the surface of the body, wherein the bio-impedance signal is induced by the electrical signal injected into the body from the first patch. 
     Further, embodiments disclosed herein include a method for monitoring one or more physiological parameters which includes injecting an electrical signal into a body with a first patch positioned at a first location on a surface of the body, and detecting a bio-impedance signal induced within the body by the injected electrical signal with a second patch positioned at a second location on the surface of the body and that is electrically isolated from the first patch, wherein the second location is spaced from the first location. The method may additionally include independently electrically grounding the first patch and the second patch to the surface of the body, and/or estimating the one or more physiological parameters based on the detected bio-impedance signal. 
     Through establishing electrical contact with the skin via electrodes, it is possible to stimulate a patient&#39;s epidermis with an electrical signal, such as an AC signal, which seeks the last impeding path through the tissues of the patient. At high frequencies, the AC signal may pass through a combination of extracellular fluid, cell membrane, and intracellular fluid, thereby capturing a mixture of information about the physiological status of the patient. Referring to  FIG. 1 , the interaction between the AC signal and the tissue of the patient can be modeled as a resistor-capacitor (RC) circuit  10  as shown in  FIG. 1 . Particularly, by representing the intracellular and extracellular fluids with resistors,  12  and  14  respectively, and the cell membrane as a capacitor  16  positioned in parallel to the extracellular resistor  14  an in series with the intracellular resistor  12 . 
     In this configuration, the frequency of the injected AC signal directly affects the impedance path. Particularly, at low frequencies, the AC signal may not be able to penetrate through the membrane capacitor  16  and thus may generally travel only through the extracellular fluid, whereas at very high frequencies the tissue RC circuit  10  will act as a low-pass filter (LPF) allowing high-frequency AC signals to penetrate the cell membrane and travel through the intracellular fluid contained therein. In the calculation of a transfer function from a cellular region, the ratio of RC parameters with respect to each other may be more significant than the values themselves. 
     Several advantages may be obtained from injecting a high-frequency electrical signal, such as an AC signal, into the epidermis of a patient. First, increasing frequency significantly decreases the electrode-to-skin impedance caused by the unideal current transfer between ions and electrons. Therefore, the voltage drop across the electrodes at the location of injection becomes minimal allowing higher amplitudes of current stimulation. Second, the allowance of injection amplitude before damaging the tissue increases may increase for frequencies approximately between 1 kilohertz (kHz) and 100 kHz. Third, changes in the impedance of the tissues and underlying cells due to variations in physiological parameters (blood flow, lung and heart movements, hydration, and muscle movements, etc.) may be more reliably carried by higher frequency AC signals. Thus, flicker noise which may dominate the low-frequency AC spectrum will have less of an impact on information carried by a high-frequency AC signal. 
     Variations in a Bio-Z signal based on the physiological autonomic motive forces (e.g., heart, lungs, pulsatile movement, etc.) as well as controlled muscle forces (respiration based chest motion, stretching, etc.) may be detected by injecting or passing an electrical signal through the upper chest of the patient. Particularly, as the injected electrical signal travels through the upper chest, the electrical signal may be modulated by physiological autonomic and controlled muscle forces of the patient. One or more sensors positioned on the upper chest of the patient may measure or detect the modulated electrical signal as the electrical signal is simultaneously injected into the patient. Thus, placing multiple sensors at various positions on the patient&#39;s body may capture these activities from multiple locations with various contributions of each activity. By capturing the autonomic motive forces and controlled muscle forces of the patient at a plurality of locations on the patient&#39;s body, the application of source separation techniques may be employed to separate these individual activities into individual estimated physiological parameters. 
     Referring to  FIGS. 2-5 , as an exemplary illustration of this methodology, an impedance mapping model  20  of an upper chest of a patient is shown in  FIG. 2 . Chest impedance model  20  simulates variations caused by the injection of an AC signal (positive injection point identified by arrow  22  in  FIG. 2  and the negative injection point identified by arrow  24 ), which may be matched with experimental data. In simulations employing model  20 , the time constant of the tissue impedance is generally smaller than body impedance changes due to heart and lung activities (i.e., τ≈μs&lt;&lt;τ heart /lungs≈s). The difference in the time constants between tissue impedance and body impedance changes may be due to the values of membrane capacitor  16 , intracellular resistor  12 , and extracellular resistor  14  of the tissue RC circuit  10  being on the order of nano-farads and tens of ohms, respectively. Therefore, the complexity of chest impedance model  20  may be minimized by replacing the tissue RC circuit  10  with a resistive element. With this simplification, the fastest frequency component of chest impedance model  20  may be due to time variant resistance (i.e. 1.3 Hz) instead of frequency of the injected AC signal (10 kHz in this example). Hence, the Nyquist sampling criterion requirement may be relaxed by approximately five orders of magnitude, allowing a larger time step selection for the simulation and a substantial reduction in the number of samples per second. 
     Chest impedance model  20  generally comprises a resistive 10-by-13 matrix (130 resistive elements in total) to simulate an entire upper chest of a patient. Particularly, the 130 resistive elements comprise a plurality of body-body tissue resistive elements  26 , a plurality of lung-body tissue resistive elements  28 , a plurality of heart-lung tissue resistive elements  30 , and a plurality of heart-body tissue resistive elements  32 , where tissue resistive elements  26 - 32  are arranged in the 10-by-13 matrix to model the location of the heart, lungs, and body tissues of an upper chest of a patient. 
     Each tissue resistive element  26 - 32  of chest impedance model  20  corresponds to two parallel resistive tissue blocks each having an assigned resistance value to include the possibility of representing regions with multiple organs (i.e. heart and lungs) associated with a single element. For example, each heart-lung resistive element  30  may comprise a modeled heart resistor positioned in parallel with a modeled lung resistor while each lung-body tissue resistive element  28  may comprise the modeled lung resistor positioned in parallel with a modeled body tissue resistor. Each resistive tissue block of each tissue resistive element  26 - 32  is assigned to have a non-stationary part that is approximately five orders of magnitude larger than the time-dependent part to match experimental results, as will be discussed further herein. Chest impedance model  20  also includes a plurality of differential voltage sensors  34 ,  36 , and  38  positioned at different locations within the 10-by-13 matrix and configured to detect a Bio-Z signal from a voltage induced by the injection of an AC signal into the 10-by-13 matrix at the injection point indicated by arrows  22 ,  24 . 
     An exemplary simulation was performed using the chest impedance model  20  where the body tissue block of each body-body resistive element  26 , long-body resistive element  28 , and heart-body resistive element  32  comprised approximately 10 4  micro-ohms (mΩ), the heart tissue block of each heart-lung resistive element  30  and each heart-body resistive element  32  comprised approximately (10 4 +25 sin(2π*1.3(t)) mΩ where (t) comprises simulation time, and the lung tissue block of each lung-body resistive element  28  and heart-lung resistive element  30  comprised approximately (10 4 +50 sin(2π*0.2( t )) mΩ.  FIGS. 3, 4  comprise graphs  40 ,  48  which illustrate Bio-Z normalized sensor readings (in arbitrary units (a.u.))  42 ,  44 , and  46  (corresponding to sensors  34 ,  36 , and  38 , respectively) in the time and frequency domains, respectively, produced from the simulation. Graphs  40 ,  48  indicate that each sensor  34 ,  36 , and  38  was able to capture respiratory activity (occurring generally at 0.15 Hz), no matter the location of each sensor  34 ,  36 , and  38 . However, sensor  38  showed a relatively weak response to the variations caused by heart activity (occurring generally at 1.3 Hz) both due to the location of sensor  38  with respect to the heart and the injection point indicated by arrows  22 ,  24 .  FIG. 5  comprises a graph  50  that illustrates the normalized amplitude of the simulated pulse  52  (occurring generally at 1.3 Hz) and respiration (occurring generally at 0.2 Hz) produced from the simulation across the columns of the 10-by-13 matrix of chest impedance model  20 . For this simulation, only the columns of the sensors were changed, whereas the rows of the differential points of the voltage sensors  34 ,  36 , and  38  remained unchanged at rows 4 and 9 as shown in  FIGS. 3, 4 . The results of the simulation indicate that the peaks for simulated heart activity and lung activity do not necessarily need to happen at the same sensing point, justifying placing multiple patches across different locations. Hence, chest impedance model  20  indicates that a holistic set of observations of the patient&#39;s physiological parameters may be directly from a plurality of spaced-apart differential voltage sensors. This sensor arrangement also allows for the creation of a diverse mixing matrix, where although the exact locations of the sources of the physiological parameters are unknown, by leveraging a source separation algorithm, it is possible to estimate these physiological parameters. Moreover, chest impedance model  20  indicates that although the exact values of the placement of sensors  34 ,  36 , and  38 , as well as the current flowing through each resistive element  26 - 32 , may also remain unknown, the impedance variations still appear at the voltage sensors  34 ,  36 , and  38 . Thus, chest impedance model  20  demonstrates the sources (physiological parameters) which may appear in the sensed Bio-Z at different locations of the patient&#39;s upper chest. 
     Referring to  FIGS. 6, 7 , an embodiment of a system  100  for monitoring one or more physiological parameters of a patient  90  using Bio-Z is shown in  FIGS. 6, 7 . System  100  generally includes a plurality of wirelessly communicable, Bio-Z sensing patches  102  positionable on or configured to releasably couple to the surface or skin of the patient  90 . Each patch  102  is generally configured for injecting a programmable electrical signal, such as an AC and/or alternating voltage signal, into the body  92  of patient  90 , and sense or detect a Bio-Z signal from the body  92  that is induced by the injected AC signal. 
     In the embodiment shown in  FIGS. 6, 7 , each patch  102  is positioned on the upper chest  94  of the patient&#39;s body  92 . Particularly, a pair of the patches  102  may be positioned underneath each armpit of the patient  90  while a third patch  102  may be positioned across the patient&#39;s heart (left the sternum). In this configuration, patches  102  may provide full or global coverage of the patient&#39;s entire upper chest  94  to thereby permit the monitoring of global variations in physiological parameters (e.g., variations directly correlated with heart and respiratory activity of the patient  94 ) of the patient  90  rather than only localized variations (e.g., localized to the wrist, ankles, arms, etc. of the patient  90 ) observed via a single sensor which may only be partially correlated to respiratory-induced blood flow of the patient  90 , making such localized observations sensitive to motion artifacts and the placement of the sensor on the patient&#39;s body  92 . However, the placement of patches  102  on the patient  90  may vary depending on the application. Additionally, while in this embodiment system  100  includes three patches  102  positioned on the patient  90 , in other embodiments, the number of patches  102  of system  100  may vary. For example, system  100  may comprise a single patch  102  or more than three patches  102  positioned at locations other than the upper chest  94  of the patient  90 . 
     To ensure that patches  102  remain comfortable to wear by the patient  90 , patches  102  are separated and electrically isolated from one another, thereby giving up the phase calibration between the patches  102 . Given that patches  102  do not include a common potential, only the frequency information is known by each patch  102 . In some embodiments, this information is sufficient to carry out a phase insensitive lock-in based amplitude demodulation to fully capture variations in the Bio-Z signal sensed by patches  102 . The variations in the received Bio-Z signal may correspond to the physiological signals or parameters initiated by the periodic heart and lung movements of patient  90 . 
     System  100  may also include an input/output (I/O) unit  300  in electronic wireless communication with one or more of the patches  102  of system  100 . I/O unit  300  may include a screen or display  302  for viewing signals received from one or more of the patches  102 . For instance, the display  302  may display one or more continuously updated physiological parameters of the patient  90  estimated by the patches  102  of system  100 . In some embodiments, the display  302  of I/O unit  300  may display physiological parameters of the patient  90  estimated by patches  102  in real-time or near real-time. For instance, in some embodiments, the delay between the sensing of the patient&#39;s Bio-Z by patches  102  and the display of one or more estimated physiological parameters derived from the sensed Bio-Z may be less than one second, such as approximately 100 milliseconds; however, in other embodiments the delay between the sensing of the patient&#39;s Bio-Z and the display of one or more estimated physiological parameters may vary. 
     As shown particularly in  FIG. 7 , each patch  102  may generally include a body or adhesive pad  104  and a plurality of electrical components coupled to and positioned on the adhesive pad  104 , including a plurality of sensors  106 , a transmitter  120 , a ground (GND) electrode  126 , a battery  140 , and a control unit or controller  150 . In some embodiments, the adhesive pad  104  of each patch  102  may have a maximum size or length  105  of approximately three inches (in); however, the size of pad  104  may vary. For instance, the maximum size or length  105  of each pad  104  may vary approximately between 1 in and 5 in. 
     In some embodiments, each patch  102  of system  100  may be configured similarly with similar components. However, in other embodiments, some patches  102  may vary in configuration from others. For example, in some embodiments, one or more patches  102  may comprise a transmitter  120  but not a sensor  106  while one or more other patches  102  comprise one or more sensors  106  but do not include a transmitter  120 . In other words, in some embodiments, system  100  may comprise at least one patch configured to inject an AC signal into the patient  90  but not to sense Bio-Z from the patient  90  and one or more patches configured to sense Bio-Z but not to inject the AC signal into the patient  90 . Additionally, some patches  102  may comprise a single senor  106  while other patches  102  may comprise a plurality of sensors  106 . Further, the controllers  150  of each patch  102  may also vary in configuration. 
     Transmitter  120  may comprise a current transmitter configured to inject an AC signal at a programmable frequency, amplitude, and/or shape and sensors  106  may comprise voltage sensors generally configured to detect a Bio-Z signal from the patient  90  based on a voltage induced by the current injected into the patient  90  by the transmitter  120  of one of the patches  102 . However, in other embodiments, transmitter  120  may be configured to inject an alternating voltage signal into the body  92  of the patient  90  and each sensor  106  may comprise a current sensor configured to detect a Bio-Z signal from the patient  90  based on a current induced by the injected voltage. 
     In this embodiment, at least one patch  102  of system  100  comprises a plurality of sensors  106 . The plurality of sensors  106  are located close proximity and may provide redundant information to mitigate the effect of noise due to motion of the patient  90  and improve signal quality and SNR by combining the output of each sensor  106 . Particularly, noise due to the motion of the patient  90  may be mitigated given that the noise introduced in the measurement of each sensor  106  will be unique given that each sensor  106  is positioned at a different location on the patient&#39;s body  92 . 
     At least one patch  102  of system  100  comprises a transmitter  120 . In some embodiments, a plurality of patches  102  comprise transmitters  120  for independently injecting a plurality of electrical signals (e.g. AC current signals) simultaneously into the patient&#39;s body  92  as a plurality of sensors  106  of system  100  simultaneously detect Bio-Z signals from the patient&#39;s body  92  induced by the injection of the plurality of electrical signals. Each transmitter  120  of system  100  may inject an electrical signal at a unique, programmable frequency into the patient&#39;s body  92  to allow for the separation of Bio-Z signals detected by the sensors  106  of system  100 . 
     While in this embodiment each patch  102  comprises three separate sensors  106 , in other embodiments, the number of sensors  106  may vary. For instance, each patch  102  may comprise a single sensor  106  or more than three sensors  106 . Each sensor  106  may include a positive electrode and a negative electrode  110  while the transmitter  120  comprises a positive electrode  122  and a negative electrode  124 . The electrodes  108 ,  110 ,  122 ,  124 , and  126  may be coupled or placed in signal communication with controller  150  via electrical tracing  130  extending between each electrode  108 ,  110 ,  122 ,  124 ,  126  and the controller  150 . At least some of electrodes  108 ,  110 ,  122 ,  124 ,  126  may comprise pre-gelled silver chloride (Ag/AgCl) electrodes having a diameter of approximately between 20 millimeters (mm) and 25 mm, however, the configuration of each electrode  108 ,  110 ,  122 ,  124 ,  126  may vary. Separating the sensing electrodes  108 ,  110  and transmission electrodes  122 ,  124  may leverage the four-point sensing mechanism to mitigate the effect of the electrode-skin interface. This is because the sensing electrodes may be followed by a buffer or instrumentational amplifier (IA) with a very high input gain rejecting a current flow to its input. Therefore, the voltage drop across the sensing patches caused by a portion of the injected current that is passing through the underlying Bio-Z may be buffered to the output as a detected Bio-Z signal with negligible loss and interference. 
     Without needing to commonly ground patches  102  (e.g., via a common hardwired connection between each patch  102 ), a reference point to the differential voltage inputs (e.g., the inputs provided by each sensor  106 ) of each patch  102  may be provided by individually and separately grounding each patch  102  to the body  92  of patient via the GND electrode  126  of each patch  102 . By separately grounding each patch  102  to the patient&#39;s body  92 , the common-mode rejection ratio may be improved by preventing the amplification of the floating common-mode signals and increasing the received signal to noise ratio (SNR) at the sensors  106 . Patches  102  of system  100  are electrically isolated from each other. As used herein, the term “electrically isolated” means that no direct electrical communication (wireless or wired) takes place between each patch  102 . Instead, the only communication which occurs between each patch  102  is the communication mediated by the body  92  of the patient  90 . Particularly, the only communication which takes place between the patches  102  of system  100  is the detection of a Bio-Z signal (via one or more of the sensors  106  of one of the patches  102 ) from the patient&#39;s body  92  induced by the electrical signal (e.g., an AC current signal and/or an alternating voltage signal, etc.) injected into the patient&#39;s body  92  (via the transmitter of one of the patches  102 ). 
     The battery  140  of each patch  102  may be generally configured to electrically power the transmitter  120  and controller  150  of the patch  102 . Battery  140  may be configured to support several hours of continuous operation of the patch  102 . In some embodiments, battery  140  may comprise a lithium polymer (LiPo) battery; however, the configuration of battery  140  may vary. 
     Controller  150  is generally configured to control the operation of the sensors  106  and transmitter  120  of the patch  102 . Controller  150  may comprise a singular controller or control board or may comprise a plurality of controllers or control boards that are coupled to one another. Controller  150  may comprise one or more flexible printed circuit boards (PCB) and/or one or more rigid PCBs with flexible or rigid connections therebetween. For convenience, and to simplify the drawings, controller  150  is depicted schematically in  FIG. 7  as a single controller unit that is coupled to various components of patch  102  and positioned on adhesive pad  104 ; however, in some embodiments, at least some components of controller  150  may not be positioned on adhesive pad  104  and instead may be positioned distal adhesive pad and in signal communication (e.g., wired or wireless communication) with components of controller  150  positioned on adhesive pad  104 . Controller  150  may comprise at least one processor and associated memory. The one or more processors (e.g., microprocessor, central processing unit (CPU), or collection of such processor devices, etc.) of controller  150  may execute machine-readable instructions provided on the memory (e.g., non-transitory machine-readable medium) to provide controller  150  with all the functionality described herein. The memory of controller  150  may comprise volatile storage (e.g., random access memory (RAM)), non-volatile storage (e.g., flash storage, read-only memory (ROM), etc.), or combinations of both volatile and non-volatile storage. Data consumed or produced by the machine-readable instructions of controller  150  can also be stored on the memory thereof. As noted above, in some embodiments, controller  150  may comprise a collection of controllers and/or control boards that are coupled to one another. As a result, in some embodiments, controller  150  may comprise a plurality of the processors, memories, etc. 
     In this embodiment, controller  150  generally includes a signal injector unit  152 , a parameter extraction unit  180 , and a wireless transmitter  240 . In this embodiment, each unit  152 ,  180 , and the wireless transmitter  240  of controller  150  may be located directly on the adhesive pad  104  of patch  102 . However, in other embodiments, certain components of controller  150  may be spaced from the adhesive pad  104 . For instance, all or some of the components of injector unit  152  and/or parameter extraction unit  180  may be located distal patch  102 , such as within I/O unit  300  or another electronic device in signal communication with I/O unit  300 . In embodiments were all or at least some components of units  152 ,  180  are positioned distal patch  102 , signals may be transmitted between patch  102  and the distal components via a hardwired or wireless (e.g., via wireless transmitter  240 ) signal link. 
     The signal injector unit  152  of controller  150  is generally configured to generate a programmable AC signal for injection into the body  92  of patient  90  via the transmitter  120  of patch  102 . Particularly, signal injector unit  152  of controller  150  may be configured to adjust the frequency, amplitude, and/or shape (e.g., sinusoidal, square, saw tooth, triangle wave, etc.) of the AC signal. For example, the signal injector unit  152  of a controller  150  of one of the patches  102  may adjust a shape of the injected AC signal from a sinusoidal shape to a square or triangle shape, etc. As the AC signal passes through the upper chest  94  of the patient  90 , the AC signal induces a voltage measured by the sensors  106  of the patches  102  positioned at different locations on the patient&#39;s body  92 . 
     Referring to  FIG. 8 , a schematic representation of an exemplary signal injector unit  152  of the controller  150  of  FIG. 7  is shown in  FIG. 8 .  FIG. 8  may only illustrate some of the components of signal injector unit  152 , and some embodiments of signal injector unit  152  may be configured differently from the unit  152  shown in  FIG. 8 . Signal injector unit  152  may generally include an injector microcontroller  154 , a digital-to-analog converter (DAC)  156 , an injector capacitor  158 , and a voltage-to-current converter (V-to-I)  160 . Injector microcontroller  154  is generally configured for operating the DAC  156  to generate an AC signal with a programmable frequency, amplitude, and/or shape. In some embodiments, injector microcontroller may comprise a 32-bit ARM® Cortex®-M4F microcontroller available from Texas Instruments. 
     DAC  156  of signal injector unit  152  may be 16-bit and may generate an AC signal having an output amplitude of approximately between 5.0 Volts (V) to 5.5 V providing a resolution of approximately 60 micro-Volts (pV) to 90 pV. Injector capacitor  158  may be positioned at the outlet of DAC  156  to prevent the injection of direct current (DC) into the skin of patient  90 . V-to-I is generally configured to convert the alternating voltage signal outputted by DAC  156  into an AC signal and may include a resistor  162  fed to the negative input of a low power precision amplifier  164  with the positive input being grounded. The amplifier  164  may support low power applications with a typical supply current of approximately between 3.0 milliamps (mA) and 4.0 mA. Electrodes  122 ,  124  of transmitter  120  may be connected to the feedback loop of amplifier  164 , allowing a root means square (RMS) current of approximately between 0.5 mA to 1.0 mA. 
     The parameter extraction unit  180  of controller  150  is generally configured to sense Bio-Z signals induced by the injection of an AC signal into the patient&#39;s body  92  via signal injector unit  152  and extract one or more estimated physiological parameters of the patient  90  derived from the sensed Bio-Z signals. The amplitude of the variations in the Bio-Z signals due to blood flow is very small (e.g., approximately between 10 mΩ and 200 mΩ) with several inches of separation between electrodes  108 ,  110  of the sensor  106 . Thus, parameter extraction unit  180  may comprise low-noise Bio-Z sensing hardware using discrete components. 
     Particularly, referring to  FIG. 9 , a schematic representation of an exemplary parameter extraction unit  180  of the controller  150  of  FIG. 7  is shown in  FIG. 9 .  FIG. 9  may only illustrate some of the components of current extraction unit  180 , and some embodiments of parameter extraction unit  180  may be configured differently from the unit  180  shown in  FIG. 9 . Parameter extraction unit  180  may generally include a low power instrumentation amplifier  182 , a LPF  186 , an analog-to-digital converter (ADC)  190 , and an extraction microcontroller  196 . 
     Amplifier  182  of parameter extraction unit  180  may provide a gain of approximately 20 decibels (dB) with approximately a 110 dB common-mode rejection ratio. The LPF  186  of parameter extraction unit  180  may comprise an anti-aliasing filter having a cut-off frequency (e.g., approximately 30 kHz) based on the sampling frequency of the ADC  190 . ADC  190  may sample three independent Bio-Z channels (e.g., the Bio-Z signal detected by each sensor  106  of patch  102 ) simultaneously with 24-bit resolution. A single multi-channel ADC  190  may be used to carry out all analog to digital conversion to isolate the processing of each individual analog channel. The multi-channel digital output of the ADC  190  may be provided to the extraction microcontroller  196  of parameter extraction unit  180  for digital signal processing (DSP). In some embodiments, the parameter extraction unit  180  may be configured for acquiring Bio-Z measurements (provided by sensors  106 ) with a RMS error of less than 1 mΩ, which is significantly lower than Bio-Z variations due to variations in blood flow (approximately 50 mΩ). 
     Extraction microcontroller  196  of parameter extraction unit  180  may perform DSP on the digital output of ADC  190  prior to estimating one or more physiological parameters of the patient  90  based on the output of ADC  190 . For example, referring to  FIG. 10 , a block diagram of an embodiment of a DSP algorithm  200  is shown in  FIG. 10 . DSP algorithm  200  may generally include a filtering or demodulation algorithm  202  followed by a parameter separation algorithm  220 . In some embodiments, both the demodulation algorithm  202  and parameter separation algorithm may be stored on a memory and performed by a processor located directly on path  202 ; however, in other embodiments, at least some of the steps comprising demodulation algorithm  202  and/or parameter separation algorithm  220  may be stored on a memory and performed by a processor of controller  150  that is not positioned on the adhesive pad  104  of any of the patches  102  of system  100  (e.g., these steps may be performed by a processor of controller  150  that is spaced from each patch  102  and in communication with one or more patches  102  via a wireless or hardwired connection). 
     In some embodiments, demodulation algorithm  202  may receive a plurality of digital raw input signals  203  each corresponding to an output from one of the channels of the ADC  190  of parameter extraction unit  180 . In other words, each raw input signal  203  corresponds to a digitized Bio-Z signal detected by one of the sensors  106  of a patch  102 . Demodulation algorithm  202  is generally configured to filter or demodulate the received raw input signals  203  to produce a plurality of corresponding demodulated signals  215  to minimize image noise in each demodulated signal  215  prior to feeding the demodulated signals  215  the parameter separation algorithm  220 . 
     In some embodiments, demodulation algorithm  202  may receive information pertaining to the unique frequency of each electrical signal injected into the patient&#39;s body  92  by each transmitter  120  of system  100 , and demodulation algorithm  202  may utilize this frequency information when producing demodulated signals  215 . For example, if a first transmitter  120  of a first patch  102  of system  100  injects a first electrical signal at a first frequency into the patient&#39;s body  92  while simultaneously a second transmitter  120  of a second patch  102  of system  100  injects a second electrical signal at a second frequency into the patient&#39;s body  92  demodulation algorithm  202  may receive as an input information pertaining to the first and second frequencies of the first and second electrical signals injected into the patient&#39;s body  92 . The demodulation algorithm  202  may include different bandpass filters centered around the first and second frequencies to separate the signal injected by the first transmitter  120  from the signal injected by the second transmitter  120  to produce a first demodulated signal  215  corresponding to the first frequency and a second demodulated signal  215  corresponding to the second frequency. 
     In some embodiments, demodulation algorithm  202  may comprise a quadrature demodulation technique or algorithm. For example, after being multiplied by a channel constant  205  (having units of ON), each input signal  203  may be demodulated via a lock-in based demodulator  204  to produce a pair of signals which may pass through a LPF  206  (e.g., a Butterworth LPF) to reject image noise as well as high high-frequency fluctuations while permitting heart rate signals up to 180 beats per minute (bpm). In some embodiments, each LPF may have a cut-off of approximately 4.4 Hz. After passing through LPFs  206 , the pair of signals may be down-sampled at block  208  to produce an in phase (I) signal  210  and a quadrature (Q) signal  212  which may be combined at block  214  to produce one of the demodulated signals  215  (e.g., the square root of the sum of the squared in phase signal  210  and the squared quadrature signal  212  may be taken to produce the demodulated signal  215 ) that is inputted to the parameter separation algorithm  220 . In some embodiments, prior to combination at block  214 , the pair of signals may each be down-stepped at block  208  from a frequency of approximately between 90 kHz to 100 kHz to a frequency approximately between 350 Hz and 400 Hz. 
     In other embodiments, demodulation algorithms as well as other filtering algorithms may be used in place of the quadrature algorithm described above for producing demodulated signals  215 . For example, in some embodiments, an envelope detection algorithm may be employed to condition raw input signals  203  prior to feeding the signals to the parameter separation algorithm  220 . Additionally, in some embodiments, raw input signals  203  may be fed directly to parameter separation algorithm  220 . 
     In some embodiments, an ECG signal may be obtained from one of the patches  102  of system  100 . Particularly, a second-order LPF (e.g., a Butterworth LPF) may be applied to one of the raw input signals  203 . In the LPF may have a cut-off frequency of approximately 30 Hz. 
     The demodulated signals  215  outputted from demodulation algorithm  202  may each comprise a plurality of physiological parameters of the patient  90  (e.g., HR, RR, BP, etc.) which may be separated via the parameter separation algorithm  220  to provide a plurality of signals corresponding to estimated physiological parameter signals  225  of the patient  90 . In some embodiments, parameter separation algorithm  220  may comprise a blind source separation (BSS) technique or algorithm configured to separate the signals sourced independently by heart and lungs from each other using the multiple simultaneous observations (e.g., the plurality of demodulated signals  215  inputted to the parameter separation algorithm  220 ). 
     Particularly, in some embodiments, parameter separation algorithm  220  may comprise a second-order blind identification (SOBI) algorithm which leverages information on the second-order statistics of the Bio-Z observations and prior knowledge of human physiology to separate and extract parameters corresponding to, for example, heart and respiration rates. A SOBI algorithm may be particularly adapted for using the time coherence of the physiological parameter signals appearing in the observations (e.g., demodulated signals  215 ) with different time delays. Additionally, a SOBI algorithm may be robust to the time delay variations of the physiological parameters for each observation. The unknown delays may be introduced due to blood flow being present at various parts of the upper chest  94  but with different phases and the capacitive components of the Bio-Z sensing. 
     In other embodiments, source separation algorithms other than SOBI may be utilized, such as, for example, Independent Component Analysis (ICA). However, Bio-Z measurements over a large chest area may demonstrate high variances in terms of phase delays given blood arrives at different time instances to each sensing location (e.g., the location of each patch  102  on the upper chest  94  of the patient  90 ), which may limit the utility of ICA and other purely statistical methods. Conversely, Second Order Separation (SOS) techniques or algorithms, such as SOBI, contrast with this feature of statistical methods, where the separation takes place due to the temporal characteristics in the ongoing activity of the underlying physiological parameters. In order to enhance the robustness of the SOS algorithms and combat the ambiguity in the time delays introduced by the patient&#39;s body  92 , the SOBI algorithm utilized in this embodiment may be run iteratively multiple times with a set of preselected time lags introduced to the demodulated signals  215  prior to each run. Given that the SOBI algorithm is based on joint diagonalization of multiple covariance matrices having different time delays, instead of a single unique matrix utilized in other SOS techniques, the SOBI algorithm may improve the robustness in separating the heart and lung sources at low processing cost. 
     To briefly outline an exemplary SOBI algorithm which may comprise the source separation algorithm  220 , input data can be represented as a vector of N recorded signals (e.g., demodulated signals  215 ), v=[v 1 , v 2 , . . . , v N ] T , as the observations of M unknown independent physiological parameters or sources represented as, s=[s 1 , s 2 , s M ] T . Not intending to be bound by any particular theory, the instantaneous linear mixture can be modeled as shown in the equation below, where A comprises an n×m mixing matrix, n(t) comprises additive noise, v(t) comprises vector of recorded signals (e.g., demodulated signals  215 ) referenced above, and s(t) comprises the vector of independent physiological parameters or sources referenced above: 
         v ( t )= A×s ( t )+ n ( t )  (1)
 
     In Equation (1), n(t) represents the additive noise modeled under two assumptions: source signals s(t) and noise n(t) are statistically independent, and n(t) is white, stationary and with zero mean. SOS-based source separation techniques may leverage the temporal correlations between the observations. Hence, in some applications, SOS-based source separation techniques may provide an advantage for scenarios where the observed signals retain low SNR given that the noise may not be common between the observations and may not significantly impact the correlation estimations. In addition, noise elements that are not Gaussian can be added to the source estimation problem. 
     The SOBI algorithm may only use the array of v(t) observations, without any prior knowledge of the model, to find the mixing matrix A and obtain the estimated and uncorrelated physiological parameters or sources represented as, y=ŝ. As described further below, and not intending to be bound by any particular theory, in order to obtain the mixing matrix A, an exemplary SOBI algorithm may begin with process of whitening the observations to reduce the determination of the n×m mixing matrix A to a unitary m×m matrix, U, without any loss of generality, using the whitening matrix, W, in accordance with the equation presented below: 
         A=W   H   ×Û   (2)
 
     Not intending to be bound by any particular theory, within a preset time delay window (τ), the exemplary SOBI algorithm may first calculate a set of covariance or correlation matrices in accordance with Equations (3) and (4) presented below, where z(t) comprises the whitened raw Bio-Z signals (e.g., whitened input signals  203 ), and τ comprises the time lag: 
         z ( t )= W×v ( t )  (3)
 
       for  j= 1 to τ  do: R   z [ j ]= E{z (;, j:N )× z (:,1: N−j ) H }  (4)
 
     In some embodiments, τ may comprise 100, corresponding to a ±13 millisecond (ms) time window, with a sampling frequency of 375 Hz following the down-stepping performed at the demodulation algorithm  202 ; however, in other embodiments, τ may vary. 
     Not intending to be bound by any particular theory, following the calculation of the covariance matrix (e.g., in accordance with Equations (3), (4) presented above), a joint diagonalization analysis may be performed to find the orthonormal change of basis. At the output, the matrix with the highest sum-squared cross-correlation value may be selected as the first estimated component, and the iteration may continue until all m components are determined. For example, a joint diagonalization Û of {R z [k j ]|j=1, . . . , τ} with the minimum sum-squared off diagonals may be determined. Following the determination of joint diagonalization U, mixing matrix A may be determined in accordance with Equation (2) presented above. 
     Not intending to be bound by any particular theory, estimated physiological sources or parameters (e.g., sources or parameters corresponding to physiological parameter signals  225 ) may be calculated in accordance with Equation (5) presented below, where y(t) comprises an estimated physiological source or parameter: 
         y ( t )= A×v ( t )  (5)
 
     Bio-Z signals provided by patches  102  positioned across the upper chest  94  of the patient  90  may provide a strong reflection of the respiration of the patient  90 . This reflection may be captured from each sensor  106  of each patch  102  simultaneously. In addition, each sensor  106  may capture a set of other internal and external sources such as heart movements, blood flow, motion artifacts and other physiological parameters of the patient  90  which do not necessarily appear with the same temporal structure in the Bio-Z signals detected by the sensors  106 . Due to the strong appearance of the respiration cycle in the temporal characteristics of the detected Bio-Z signals, the first estimated physiological parameter signal  225  at the output of SOBI algorithm that has the highest eigenvector may comprise a respiratory signal of the patient  90  (e.g., BioZR(t)=y 1 (t)) from which various physiological parameters pertaining to the respiration of the patient  90  may be derived. 
     In some embodiments, for the physiological parameter signal  225  corresponding to the respiration signal of the patient  90 , an additional LPF (e.g., a second-order Butterworth low-pass filter having approximately a 1 Hz cut-off) may be applied to remove high-frequency oscillations, followed by a peak detection algorithm to calculate inter-breath intervals (IBrI) of the patient  90 . Optionally, in order to provide an average estimate of the RR of the patient  90  for a preset interval, a 60-second averaging window may be applied to the calculated IBrIs with approximately 55 seconds of overlap. 
     In addition to the estimated physiological parameter signals  225 , the exemplary SOBI algorithm may estimate the mixing matrix A (e.g., via Equation (2) presented above), which allows for the isolation of each physiological parameter of the patient  90  which may be estimated from the Bio-Z signals detected by the sensors  106  of patches  102 . The reconstruction of the remaining physiological parameter signals  225  (excluding the physiological parameter signal  225  corresponding to the respiratory signal of the patient  90 ) by removing the contribution of the RR extracted by the SOBI algorithm for each demodulated signal  215  using a demixing matrix which is the inverse of the determined mixing matrix A. The resulting, demixed demodulated signals  215  include temporal information of the heart activity (e.g., HR, etc.) of the patient  90  without any disturbance of the phase characteristics (i.e. delayed arrival of heart pressure pulse wave at different locations of the thorax due to finite pulse wave velocity). For example, and not intending to be bound by any particular theory, a demixing matrix D may calculated in accordance with Equations (6) and (7) presented below where I corresponds to an identify matrix of the D and A matrices: 
         I=D×A   (6)
 
         v ( t )= D×y ( t )  (7)
 
     The SOBI algorithm may employ the demixed demodulation signals  215  to reconstruct or estimate one or more physiological parameter signals  225  corresponding to the heart activity of the patient  90 , from which a HR of the patient may be derived. For example, and not intending to be bound by any particular theory, a physiological parameter signal  225  corresponding to the heart activity (BioZH(t)) of patient  90  may be extracted in accordance with Equation (8) below where the contribution of the respiratory activity (BioZR(t)) is subtracted: 
       Bio ZH ( t )= v   1 ( t )− D   nx1   ×y ( t )  (8)
 
     A spectrogram of each physiological parameter signal  225  corresponding to the heart activity of the patient  90  (e.g., BioZH(t)) may be extracted using a fast Fourier transform (FFT) and a dominant frequency region may thereby be detected. A LPF (e.g., a second-order Butterworth LPF) may then be applied with a cut-off frequency that is approximately 1 Hz higher than the detected dominant frequency to reject high-frequency oscillations. To calculate the HR, zero-crossing, foot and peak points of the first and second derivatives of the signal may be used to detect the important characteristic points, such as local peaks, and feet and maximum slope points (MSPs). These points may be used to calculate the corresponding interbeat intervals (IBI) of the patient  90 , where IBI may comprise the period or the duration of one heartbeat of the patient  90 . In order to reduce the effect of motion artifacts, a moving average filter may be applied to the inverse of the calculated IBIs (1/IBI, beats per second) with an approximately 30-second averaging window and approximately 28 seconds of overlap to the physiological parameter signals  225 . The output of each window may be multiplied by approximately 60 to obtain the HR of the patient  90  in bpm for each 30-second window. 
     In order to evaluate the performance of the system (e.g., system  100  shown in  FIG. 6 ), wirelessly communicable, Bio-Z detecting patches (e.g., patches sharing features in common with the patches  102  shown in  FIGS. 7, 8 ) were used to extract continuous HR and RR values from ten healthy human subjects. Prior to the main experimental study, the performance improvement with the body GND electrode placement was first evaluated and two pilot studies were conducted to decide on the optimum patch size and to show the importance of multiple patch approach rather than single patch measurements. As discussed further below, a smaller electrode separation on a single patch may lead to a direct decrease in the size of the patch supporting the wearable applications built on top of this technology with a trade-off in the SNR. In addition, the multiple patch approach proposed herein may provide high-fidelity separation of heart and lung activities. 
     As described above, a novel sensing technique was implemented with Bio-Z detecting patches to provide global observations of the chest physiology. We achieved through two general steps: First, a patch (“TX patch”) comprising a current transmitter was placed underneath the left armpit. Second, with the assumption that all parts of the upper body see a fraction of this current, three patches (the “RX patches”) including voltage sensors were placed at different locations of the upper chest. A first patch (“Bio-Z1”) comprising voltage sensors was placed across the heart (left of the sternum), the second patch (“Bio-Z2”) comprising voltage sensors was placed at the right of the sternum, and a third patch (“Bio-Z3”) comprising voltage sensors was placed underneath the right armpit, giving full coverage across the upper chest. In all of the Bio-Z measurements conducted during these experiments, a sinusoidal AC signal at 10 kHz with 0.64 mA RMS amplitude was injected, complying with the safety standards. 
     To evaluate the effect of GND electrode placement of the RX patches on the body, two one-minute long experiments were run with the only difference between the experiments being the inclusion of the GND electrode. For both experiments, the TX patch was used for the injection and Bio-Z1 for sensing. Since the voltage pick-up is differential, the location of the GND electrode may not be significant in at least some applications. On the other hand, the GND electrode was placed next to the RX patch to prevent the patch size increase. Under the same injection amplitudes, the SNR of the carrier signal, picked-up by the TX patch with the body GND electrode, was observed to be 7 dB higher than the case without the body GND electrode given that the GND electrode may improve the common-mode rejection of the IA. Referring to  FIG. 11 , graphs  310 ,  312  illustrating a flipped ΔBio-Z signal after demodulation are shown for both cases. The signal fidelity may be better with the body GND electrode (with body GND for graph  310  and without body GND for graph  312 ), in agreement to the SNR measurements. Consequently, moving forward the body GND electrodes placed next to the RX patches were used with no constraint on the precise placement. 
     For the pilot study regarding the size of the Bio-Z detecting patches, the effect of the separation amount between differential electrodes (nodes) of each patch was tested from 1-inch to 5-inches under a total of 25 minutes of data collection from a single subject. Moving forward, 3-inches separation was used between the two differential nodes of each TX and RX patches, due to its superior performance (in some applications) compared to the smaller sizes while providing similar performance compared to the larger sizes. In order to complete an extensive evaluation of the Bio-Z detecting patches, four sets of five-minute continuous data were collected from 10 healthy subjects. 
     To assess the performance of Bio-Z detecting patches for estimating the HR, an ECG signal captured simultaneously by the Bio-Z1 sensor placed at the heart was used to detect the true heartbeats. Moreover, capnography (e.g., RespSense II, Nonin, USA) was used for measuring a reference of the respiration waves recorded during the data collection with Bio-Z detecting patches to assess the estimation of RR. The capnography tracked the CO 2  concentration through a nasal cannula connected to the device to determine the RR. During the data collection process, subjects remained seated with the capnography device connected through a nasal cannula. Prior to the start of the actual data collection with the Bio-Z detecting patches, we asked subjects were asked to hold their breath for 10 seconds, where we used this signature time interval to synchronize capnography data to the Bio-Z detecting patches data. 
     Referring to  FIG. 12 , graphs  320 ,  322 ,  324 , and  326  are shown which illustrate exemplary physiological parameters or signals measured with the experimental setup and the capnography device are shown. Particularly, graphs  320 ,  322  indicate time plots of measured physiological signals with the Bio-Z detecting patches, graph  324  indicates a reference capnography measurement, and graph  326  indicates all signals scaled and plotted within the same time legend. Periodic lung movements due to respiration were observed on the raw Bio-Z signals captured by the Bio-Z detecting patches. Based on these observations, it is expected that iterative SOBI would identify this temporal correlation between the sensors with high fidelity. Referring to  FIG. 13 , graphs  330 ,  332 , and  334  are shown which illustrate an exemplary frequency spectrogram correlating the raw Bio-Z inputs which are inputted to iterative SOBI and the respiration and heart activity signals obtained through iterative SOBI. Particularly, graph  330  indicates raw Bio-Z signals modulated by the respiration cycle with additional frequency components appearing at higher frequencies; graph  332  indicates a respiration signal obtained after the application of source separation which matches a reference signal acquired through capnography; and graph  334  indicates a hear activity signal estimated after signal reconstruction which exhibits an improved SNR matching with a reference ECG signal. Corresponding references were also plotted for both sources in  FIG. 13 . After the application of the iterative SOBI algorithm with the signal reconstruction, the system estimated heart and lung activities with high accuracy. The separated heart (Bio-ZH) and respiration (Bio-ZR) source signals (y(t)) were used in HR and RR estimations, respectively. 
     Referring to  FIG. 14 , graphs  340 ,  342  are shown which illustrate an example of the extracted characteristics points on the Bio-ZH and Bio-ZR signals and the corresponding reference signals are shown. Particularly, graph  340  indicates Bio-ZH with reference to ECG while graph  342  indicates Bio-ZR with reference to capnography signals. IBIs were calculated from a combination of maximum slope point (MSP), peak and footpoints in Bio-ZH and R-peaks in the ECG signal. To estimate IBrIs from the Bio-ZR and capnography signals, MSPs were used. In order to mitigate the effect of motion artifacts and high-frequency oscillations that alter the peak points, a moving average algorithm, similar to the moving average algorithms described above, were applied. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE I 
               
             
            
               
                   
                   
               
               
                   
                   
                   
                 IBrI 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Patch 
                 RR 
                 Upper 
                 Lower 
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 Size 
                   
                 RMSE 
                 95% 
                 95% 
                   
               
               
                   
                 (inches) 
                 r 
                 (BPM) 
                 limit (s) 
                 limit (s) 
                 r 
               
               
                   
                   
               
               
                   
                 5 
                 0.961 
                 0.167 
                 0.444 
                 −0.389 
                 0.961 
               
               
                   
                 4 
                 0.914 
                 0.112 
                 0.439 
                 −6.464 
                 0.914 
               
               
                   
                 3 
                 0.912 
                 0.067 
                 0.274 
                 −0.283 
                 0.912 
               
               
                   
                 2 
                 0.904 
                 0.299 
                 0.785 
                 −0.605 
                 0.904 
               
               
                   
                 1 
                 6.962 
                 0.183 
                 0.549 
                 −0.482 
                 0.962 
               
               
                   
                   
               
            
           
         
       
     
     The Pearson&#39;s correlation coefficient (r) and average root mean square error (RMSE) in breaths-per-minute (BPM) in RR estimation for five different patch sizes determined by the separation between the differential electrodes of each patch starting from 5-inches up to 1-inch are shown above in Table I. Referring to  FIGS. 15, 16 , graphs  350 - 359  are shown which illustrate the Bland-Altman and Pearson&#39;s correlation analyses for this experiment. Particularly, graphs  350 - 354  illustrate the Bland-Altman correlation analysis plots while graphs  355 - 359  illustrate the Pearson&#39;s correlation analysis plots. Graphs  350 ,  355  pertain to a patch size of approximately 5 in; graphs  351 ,  356  pertain to a patch size of approximately 4 in; graphs  352 ,  357  pertain to a patch size of approximately 3 in; graphs  353 ,  358  pertain to a patch size of approximately 2 in; and graphs  354 ,  359  pertain to a patch size of approximately 1 in. For each plot, 51 IBrIs calculated from the averaged peak-to-peak values were used. It was observed from the experiment that the Bio-Z detecting patches provide strong confidence in RR estimation with all patch sizes with a minimum of 0.9 correlation and a maximum of 0.3 BPM RMSE. 
     
       
         
           
               
               
               
               
             
               
                   
                 TABLE II 
               
             
            
               
                   
                   
               
               
                   
                   
                   
                 IBI 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                   
                   
                 Upper 
                 Lower 
                   
               
               
                   
                 Patch 
                 HR 
                 95% 
                 95% 
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 Size 
                   
                 RMSE 
                 limit  
                 limit  
                   
               
               
                   
                 (inches) 
                 r 
                 (bpm) 
                 (ms) 
                 (ms) 
                 r 
               
               
                   
                   
               
            
           
           
               
               
               
               
               
               
               
            
               
                   
                 5 
                 0.976 
                 0.267 
                 6.16 
                 −6.95 
                 0.976 
               
               
                   
                 4 
                 0.980 
                 0.325 
                 9.15 
                 −4.87 
                 0.984 
               
               
                   
                 3 
                 0.983 
                 0.655 
                 17.1 
                 −18.9 
                 0.983 
               
               
                   
                 2 
                 0.751 
                 3.597 
                 124.3 
                 −25.6 
                 0.765 
               
               
                   
                 1 
                 −0.122 
                 5.217 
                 189.2 
                 −67.2 
                 −0.090 
               
               
                   
                   
               
            
           
         
       
     
     The r and RMSE in bpm in HR estimation for all patch sizes are shown above in Table II. Results show that the device performance with higher than 3-inches patch separation remained comparable with an RMSE of less than one bpm. However, in contrast to RR estimation performance, further decreasing the size from 3-inches to 2-inches degraded the signal quality and increases the error in HR estimation. Referring to  FIG. 17, 18 , graphs  360 - 369  are shown which illustrate the corresponding Bland-Altman and Pearson&#39;s correlation plots for different patch sizes using 140 IBIs for each patch size. Particularly, graphs  360 - 364  illustrate the Bland-Altman correlation analysis plots while graphs  365 - 369  illustrate the Pearson&#39;s correlation analysis plots. Graphs  360 ,  365  pertain to a patch size of approximately 5 in; graphs  361 ,  366  pertain to a patch size of approximately 4 in; graphs  362 ,  367  pertain to a patch size of approximately 3 in; graphs  363 ,  368  pertain to a patch size of approximately 2 in; and graphs  364 ,  369  pertain to a patch size of approximately 1 in. The 95% limits of agreement are also shown above in Table II. A 3-inch patch size was used moving forward with the extensive analysis due to the high accuracy in both HR and RR estimations (in some applications) using this configuration. 
     1377 IBrIs were used in the analysis, where data for subjects 7 and 9 in the analysis were excluded due to peak detection problems and very high noise in the dataset for these subjects, respectively. 
     
       
         
           
               
               
               
             
               
                 TABLE III 
               
               
                   
               
               
                   
                   
                 RMSE  
               
               
                 Subject 
                 r 
                 (BPM) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 1 
                 0.978 
                 0.097 
               
               
                 2 
                 0.893 
                 0.294 
               
               
                 3 
                 0.919 
                 0.292 
               
               
                 4 
                 0.892 
                 0.364 
               
               
                 5 
                 0.935 
                 0.128 
               
               
                 6 
                 0.962 
                 0.559 
               
               
                 8 
                 0.848 
                 0.365 
               
               
                 10 
                 0.944  
                 0.181 
               
               
                 Average 
                 0.921 ± 0.042 
                 0.285 ± 0.151 
               
               
                   
               
            
           
         
       
     
     r, RMSE in BPM and average BPM are presented above for each subject in Table III. Referring to  FIGS. 19, 20 , graphs  370 ,  372  are shown which illustrate plots for Bland-Altman correlation analysis (graph  370  of  FIG. 20 ) and Pearson&#39;s correlation analysis (graph  372  of  FIG. 21 ) over 1377 IBrIs. Graphs  370 ,  372  indicate agreement between the novel method described herein and the reference method, where the negative and positive 95% limits of agreement values appeared less than 1.3 BPM, with the mean of the error (IBI estimated −IBI true ) appearing as 0.07 BPM. In addition, Pearson&#39;s correlation analysis resulted in 0.983 for the correlation coefficient, r. 
     In the performance evaluations, 3798 IBIs calculated using more than 2 hours of data collection in total from seven healthy subjects were used. For this evaluation, data corresponding to Subjects 1 and 9 were excluded due to high divergence from the rest of the subjects. In addition, an anomaly in ECG of Subject 5 was observed, also detected with the Bio-Z detecting patches. For this reason, Subject 5&#39;s data was separated from the dataset. To indicate the agreement between the ECG and Bio-ZH acquired with the Bio-Z detecting patches, the averaged IBIs for both signals were compared. 
     
       
         
           
               
               
               
             
               
                 TABLE IV 
               
               
                   
               
               
                   
                   
                 RMSE  
               
               
                 Subject 
                 r 
                 (BPM) 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                 1 
                 0.978 
                 0.097 
               
               
                 2 
                 0.893 
                 0.294  
               
               
                 3 
                 0.919  
                 0.292 
               
               
                 4 
                 0.892 
                 0.364  
               
               
                 5 
                 0.935 
                 0.128 
               
               
                 6 
                 0.962 
                 0.559 
               
               
                 8 
                 0.848 
                 0.365  
               
               
                 10 
                 0.944 
                 0.181 
               
               
                 Average 
                 0.921 ± 0.042  
                 0.285 ± 0.151 
               
               
                   
               
            
           
         
       
     
     Table IV shown above provides the average RMSE in bpm and r for each subject in HR estimation. The results demonstrated a strong correlation of estimated HR with the reference HR for each subject, with average r appearing at 0.948 and RMSE at 0.579 bpm. In order to conduct a comprehensive analysis of the IBI estimation performance, the Bland-Altman and Pearson&#39;s correlation analyses were carried over 3798 IBIs. Referring to  FIGS. 21, 22 , graphs  380 ,  382  are shown which illustrate the Bland-Altman correlation plot (graph  380 ) and the Pearson&#39;s correlation plot (graph  382 ). Graphs  380 ,  382  indicate a strong agreement between the novel method described herein and the gold standard. The 95% limits of the agreement appear at 22.1 ms and −22.4 ms in graph  380 . The Pearson&#39;s correlation plot shown in graph  382  includes a Pearson&#39;s correlation coefficient of 0.998. 
     In order to evaluate the performance improvement with the novel method disclosed herein with respect to the traditional methods that depend on a single patch measurement, a pilot study was conducted on a single subject over 20 minutes of data collection. With the subjects at rest, Bio-Z detecting patches were placed in a configuration similar to the experiment described above and a lock-in based demodulation was carried out separately for each voltage sensor to extract the raw Bio-Z signals. Three different methods were then defined to extract the heart activity. The first method comprised running a 2nd order Butterworth filtering with a cut-off at 0.5 Hz on a single patch Bio-Z, whereas second and third methods depended on the application of iterative SOBI on two patches and three patches respectively. To evaluate the performance in separating the heart activity, precision and recall analysis were performed on estimated peak locations from the output signal and the true peak locations extracted from the ECG, as well as the Bland-Altman and Pearson&#39;s correlation analyses on the estimated IBIs and the true IBIs. For the filtering case with a single patch, the patch placed on the heart was chosen to get the strongest heart activity, and for two patches based iterative SOBI, we tried all three input combinations and took the average. 
     1988 beats were used in the analysis, where the peak detection algorithm described herein was applied to the output signal for each method and the reference ECG signal. Based on the time location of each of the estimated peak with respect to the reference peak, the peaks were classified as true positive (TP), false positive (FP) or false negative (FN). The TP, FP, and FN refer to the cases where the estimation falls in between 20% of the current beat in reference to ECG peak, falls outside of this 20% threshold and no peak is found inside this threshold, respectively. The accuracy and precision were then calculated according to Equations (9) and (10) presented below: 
     
       
         
           
             
               
                 
                   Accuracy 
                   = 
                   
                     TP 
                     
                       TP 
                       + 
                       FP 
                     
                   
                 
               
               
                 
                   ( 
                   9 
                   ) 
                 
               
             
             
               
                 
                   Precision 
                   = 
                   
                     TP 
                     
                       TP 
                       + 
                       FN 
                     
                   
                 
               
               
                 
                   ( 
                   10 
                   ) 
                 
               
             
           
         
       
     
     After this classification, the RMSE based on the TPs only was also calculated, where the error is defined as the difference between the IBIs calculated through reference versus estimated peaks. Referring to  FIG. 23 , charts  390 ,  392 , and  394  are shown which illustrate the accuracy (shown in chart  390 ), precision (shown in chart  392 ), and RMSE (shown in chart  394 ) obtained for each method are shown for a single patch configuration, a two patch configuration, and a three patch configuration (the number of patches arranged on the X-axis), where SOBI performed with three patches performed the best in all analysis metrics. In addition, SOBI even with two patches showed a significant improvement compared to the traditional filtering approach. 
     In this analysis, instead of classifying estimated peaks with respect to the reference peak, for each reference peak, the closest estimated peak was chosen, followed by beat-by-beat IBI calculation for both reference and estimated signals. 1295 IBIs were used in the analysis. 
     
       
         
           
               
               
             
               
                 TABLE V 
               
             
            
               
                   
               
               
                   
                 IBI 
               
            
           
           
               
               
               
               
               
               
            
               
                   
                 Lower 
                   
                 Upper 
                   
                   
               
               
                   
                 95% 
                 Mean 
                 95% 
                   
                   
               
               
                   
                 limit 
                 Error 
                 limit 
                   
                 RMSE 
               
               
                 Method 
                 (ms) 
                 (ms) 
                 (ms) 
                 r 
                 (%) 
               
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 1 Patch 
                 −107.12 
                 3.95 
                 115.03 
                 0.8658 
                 6.1% 
               
               
                 with 2 nd   
                   
                   
                   
                   
                   
               
               
                 order 
                   
                   
                   
                   
                   
               
               
                 Butterworth 
                   
                   
                   
                   
                   
               
               
                 HPF 
                   
                   
                   
                   
                   
               
               
                 2 Patches 
                 −108.85 
                 1.14 
                 111.13 
                 0.8780 
                 6.0% 
               
               
                 with  
                   
                   
                   
                   
                   
               
               
                 iterative 
                   
                   
                   
                   
                   
               
               
                 SOBI 
                   
                   
                   
                   
                   
               
               
                 2 Patches 
                 −81.87 
                 1.68 
                 85.24 
                 0.9184 
                 4.5% 
               
               
                 with  
                   
                   
                   
                   
                   
               
               
                 iterative 
                   
                   
                   
                   
                   
               
               
                 SOBI 
               
               
                   
               
            
           
         
       
     
     r, RMSE and Bland-Altman coefficients are shown above in Table V. Referring to  FIGS. 24 .  25 , graphs  400 - 406  are shown which illustrate the plots for the Bland-Altman correlation analysis (graphs  400 - 402 ) and the Pearson&#39;s correlation analysis (graphs  403 - 405 ) over all IBIs are shown. Graphs  400 ,  403  pertain to a single patch system; graphs  401 ,  404  pertain to a system including two patches; and graphs  402 ,  405  pertain to a system including three patches. The results show a significant improvement in the agreement between the novel method described herein and the reference method compared to filtering. 
     Overall the results of the above described experimentation demonstrate that Bio-Z detecting patches perform HR and RR estimations high accurately with an average RMSE of 0.288 BPM and 0.589 bpm for HR and RR, respectively. The above results also demonstrate the performance of the system under various electrode separation configurations and patch sizes. The above described pilot study indicated that the 3-inches patch separation is sufficient for high fidelity signal acquisition. Moreover, it is still possible to use 1-inch patches to extract the respiration rate. 
     While exemplary embodiments have been shown and described, modifications thereof can be made by one skilled in the art without departing from the scope or teachings herein. The embodiments described herein are exemplary only and are not limiting. Many variations and modifications of the systems, apparatus, and processes described herein are possible and are within the scope of the disclosure. For example, the relative dimensions of various parts, the materials from which the various parts are made, and other parameters can be varied. Accordingly, the scope of protection is not limited to the embodiments described herein, but is only limited by the claims that follow, the scope of which shall include all equivalents of the subject matter of the claims. Unless expressly stated otherwise, the steps in a method claim may be performed in any order. The recitation of identifiers such as (a), (b), (c) or (1), (2), (3) before steps in a method claim are not intended to and do not specify a particular order to the steps, but rather are used to simplify subsequent reference to such steps.