Patent Description:
Health monitoring may traditionally involve obtaining physiological signals such as electrocardiogram (ECG) readings from an individual in a clinical setting such as a hospital, doctor's office or other medical center. In such a case, a medical professional may connect various sensors to the individual/patient and interpret the readings in order to make health-related decisions. If the medical professional determines that the ECG readings are not reliable or of adequate quality, the medical professional may make adjustments to the sensing configuration and/or environment prior to making health-related decisions based on those readings. In home use settings, however, patients may often lack the requisite medical and/or technical knowledge to identify unreliable or inferior quality readings and make the appropriate adjustments to the sensing configuration/environment. As a result, suboptimal health care (e.g., improper diagnosis, increased cost and/or increased patient risk) may be experienced. XP027670999 discloses ECG baseline wander and powerline interference reduction using nonlinear filter bank. <CIT> discloses a system for monitoring various physiological and environmental factors. <CIT> discloses an automated medical diagnostic system. <CIT> discloses ambulatory and centralized processing of physiological signals.

<FIG> shows a plurality of signals that may be associated with the monitoring of an individual/patient in a home health setting. In the illustrated example, a physiological signal <NUM> such as, for example, an electrocardiogram (ECG) signal may be deemed reliable due to a lack of noise in the physiological signal <NUM>. Although the illustrated physiological signal <NUM> contains ECG information, in other examples, the physiological signal <NUM> may contain blood pressure information, pulse oximeter information, Electroencephalograph (EEG) information, Photoplethysmograph (PPG) information, and so forth.

Depending upon the sensing configuration and/or environment, a plurality of noise sources <NUM> (12a-12e) may be superimposed on physiological signals such as the signal <NUM> and therefore reduce the quality and/or reliability of those signals. For example, a power main (e.g., <NUM>/<NUM>) interference source 12a might originate from nearby low frequency electrical equipment, building power lines, etc. Additionally, a muscle noise source 12b may originate from involuntary muscle contractions of the patient due to anxiety, and a motion artifact noise source 12c may originate from patient movement. Moreover, an electromagnetic interference (EMI) source 12d may originate from nearby high frequency devices such as mobile phones and other electronic devices, and a baseline wander noise source 12e may originate from chemical reactions and other contributors to changes in skin-electrode impedance. Each of the noise sources <NUM> may therefore have a negative impact on the quality of the measured physiological signal to the extent that the respective type of noise is present in the physiological signal. Indeed, the noise sources <NUM> may present unique challenges in home health settings due to a relative lack of medical and/or technical knowledge of typical patients.

As will be discussed in greater detail, both a qualitative analysis and a quantitative analysis may be conducted in home health settings for each of the noise sources <NUM>, wherein those analyses may be used to determine whether and when to report the physiological signals to a remote location such as a clinical health setting (e.g., hospital, doctor's office or other medical center). In addition, the analyses may be used to guide patients in modifying the sensor configuration and/or environment in order to increase the reliability of reported physiological signals.

Turning now to <FIG> a home health monitoring environment is shown in which a patient <NUM> uses a mobile device <NUM> to take readings such as, for example, ECG readings, blood pressure readings, pulse oximeter readings, EEG readings, PPG readings, and so forth. In the illustrated example, the mobile device <NUM> includes one or more sensors (e.g., electrodes, contacts) <NUM> that may be pressed against a body part (e.g., chest, arm, head) of the patient <NUM> in order to measure the physiological condition of the patient <NUM>. The mobile device <NUM> may generate one or more physiological signals in conjunction with the readings, wherein the physiological signals may be transmitted to a healthcare network <NUM>. As will be discussed in greater detail, the mobile device <NUM> may be configured to make automated quality assessments of the physiological signals prior to transmitting them to the healthcare network <NUM> as well as guide the patient <NUM> in taking additional readings if the assessments indicate that earlier readings lack reliability.

The healthcare network <NUM> may in turn provide the reported physiological signals to healthcare professionals such as physicians, nurses, clinicians, and so forth. Additionally, the healthcare professionals may deliver advice to the patient <NUM> via the healthcare network <NUM> and/or mobile device <NUM>. In addition to having the integrated sensors <NUM>, the mobile device <NUM> may be a computing platform such as a wireless smart phone, smart tablet, personal digital assistant (PDA), mobile Internet device (MID), notebook computer, convertible tablet, etc., having other functionality such as messaging (e.g., text messaging, instant messaging/IM, email), computing, media playing, and so forth.

<FIG> shows a home health monitoring environment in which the patient <NUM> uses a measurement accessory <NUM> and a mobile device <NUM> to take readings such as, for example, ECG readings, blood pressure readings, pulse oximeter readings, EEG readings, PPG readings, and so forth. In the illustrated example, the measurement accessory <NUM> includes one or more sensors (e.g., electrodes, contacts) <NUM> that may be pressed against a body part of the patient <NUM> in order to measure the physiological condition of the patient <NUM>. The illustrated measurement accessory <NUM> generates one or more physiological signals in conjunction with the readings, wherein the physiological signals may be transmitted to the mobile device <NUM>. As in the case of the mobile device <NUM> (<FIG>), the measurement accessory <NUM> or mobile device <NUM> may be configured to make automated quality assessments of the physiological signals prior to transmitting them to the healthcare network <NUM> as well as guide the patient <NUM> in taking additional readings if the assessments indicate that earlier readings lack reliability.

As already discussed, the healthcare network <NUM> may provide the reported physiological signals to healthcare professionals, who may deliver advice to the patient <NUM> via the healthcare network <NUM> and/or mobile device <NUM>. The mobile device <NUM> may be a computing platform such as a wireless smart phone, smart tablet, PDA, MID, notebook computer, convertible tablet, etc., with messaging, computing, media playing and/or other functionality.

<FIG> shows a method <NUM> of evaluating physiological signals in a home health setting. The method <NUM> may be implemented in executable software as a set of logic instructions stored in a machine- or computer-readable medium of a memory such as random access memory (RAM), read only memory (ROM), programmable ROM (PROM), firmware, flash memory, etc., in configurable logic such as, for example, programmable logic arrays (PLAs), field programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), in fixed-functionality logic hardware using circuit technology such as, for example, application specific integrated circuit (ASIC), complementary metal oxide semiconductor (CMOS) or transistor- transistor logic (TTL) technology, or any combination thereof. For example, computer program code to carry out operations shown in method <NUM> may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages.

Illustrated processing block <NUM> provides for receiving a physiological signal from a sensor configuration associated with a mobile device. As already noted, the physiological signal may be associated with an ECG reading, blood pressure reading, pulse oximeter reading, EEG reading, PPG reading, and so forth. The signal strength of the physiological signal may be estimated at block <NUM>. In one example, estimation of the signal strength involves de-noising the physiological signal using multi-band filters. The filters used in the de-noising procedure may take into consideration the frequency profile of various types of noise sources (e.g., power main interference, muscle noise, motion artifact noise, EMI, baseline wander noise). Additionally, block <NUM> may involve signal processing to identify one or more fiducial points in the filtered signal. For example, a fudicial point in an ECG signal may correspond to an R-wave (i.e., upward deflection in a QRS complex) of the ECG signal. Thus, the fiducial points may be used to calculate the signal strength of the physiological signal.

Block <NUM> may extract a first noise source from the physiological signal. For example, for the aforementioned baseline wander noise 12e (<FIG>), block <NUM> may apply a digital low pass filter (LPF) having a cutoff frequency of lHz, a cubic spline, etc., to the physiological signal.

Alternatively, the de-noised physiological signal from block <NUM> may be subtracted from the signal in block <NUM> in order to isolate the baseline wander noise 12e from the physiological signal. In this example, the output of block <NUM> may be only the baseline wander, extracted from the physiological signal. Illustrated block <NUM> provides for performing noise estimation for the first noise source. For example, for the baseline wander noise 12e (<FIG>), the noise estimation may involve rejecting outlier data in the isolated baseline wander noise and determining/calculating the area under the resulting curve. The area under the noise curve may be particularly effective for low frequency noise such as baseline wander noise. Block <NUM> may also provide for normalizing the estimated noise with respect to the physiological signal strength estimated in block <NUM>.

A qualitative analysis may be conducted for the first noise source at block <NUM>. More particularly, an individual qualitative rating - QR<NUM> (e.g., "Good", "Fair", "Poor") may be assigned to the first noise source by comparing the estimated noise for the first noise source to appropriate thresholds. In this regard, since medical professionals typically make visual assessments of physiological signals to decide whether they are of acceptable quality, the qualitative thresholds may be chosen to match manual visual acuity/assessments. For example, the rating criteria might be implemented as given in Table I below.

Illustrated block <NUM> conducts a quantitative analysis for the first noise source. More particularly, a signal to noise ratio (SNR<NUM>) may be computed for the first noise source based on the estimated physiological signal strength from block <NUM> and the estimated and normalized noise from block <NUM>. As will be discussed in greater detail, the SNR<NUM> for the first noise source may be subsequently combined with the SNRs of the other noise sources to obtain an overall quality level for the physiological signal.

Similarly, block <NUM> may extract a second noise source from the physiological signal. For example, for the aforementioned power main interference source 12a (<FIG>), block <NUM> may apply a digital elliptic band pass filter (BPF) having a center frequency of <NUM> or <NUM>, a wavelet transform, etc., to the physiological signal. Alternatively, the de-noised physiological signal from block <NUM> may be subtracted from the filtered signal in block <NUM> in order to isolate the power main interference source from the physiological signal. In this example, the output of block <NUM> may be only the mains <NUM> or <NUM> noise, extracted from the physiological signal. Illustrated block <NUM> provides for performing noise estimation for the second noise source. For example, for the power main interference source 12a (<FIG>), the noise estimation may involve calculating the peak-to-peak average for the noise curve for the isolated power main interference. The peak-to-peak average may be particularly effective for high frequency noise such as power main interference. Block <NUM> may also provide for normalizing the estimated noise with respect to the physiological signal strength estimated in block <NUM>.

As in the case of the first noise source, a qualitative analysis may be conducted for the second noise source at block <NUM>. Thus, an individual qualitative rating - QR<NUM> (e.g., "Good", "Fair", "Poor") may be assigned to the second noise source by comparing the estimated noise for the second source to appropriate thresholds, as already discussed with regard to Table I. Illustrated block <NUM> conducts a quantitative analysis for the second noise source. More particularly, an SNR<NUM> may be computed for the second noise source based on the estimated physiological signal strength from block <NUM> and the estimated and normalized noise from block <NUM>.

The illustrated noise extraction and estimation procedure may be conducted for each of a plurality of noise sources in the physiological signal. For example, for the muscle noise source 12b (<FIG>), the noise extraction may involve applying a digital BPF having a center frequency of <NUM> or <NUM> to the physiological signal. For the motion artifact noise source 12c (<FIG>), the noise extraction might involve applying a digital notch filter with a center frequency of <NUM> or <NUM> to remove power main interference and applying a digital LPF having a cutoff frequency of <NUM>, a cubic spline, etc. Noise extraction techniques may be similarly tailored to the EMI source 12d (<FIG>) and other types of noise in the physiological signal. In each case, the de-noised physiological signal from block <NUM> may be subtracted from the filtered noise signal in order to isolate the particular type of noise from the physiological signal.

With regard noise estimation, relatively high frequency noise such as the muscle noise source 12b and/or the EMI source 12d may be estimated by calculating the peak-to-peak average for the noise curve. Relatively low frequency noise, on the other hand, such as the motion artifact noise source 12c might be estimated by rejecting outlier data in the isolated noise and determining the area under the resulting curve.

The illustrated qualitative and quantitative analyses may also be conducted for each of the plurality of noise sources. Thus, a plurality of qualitative ratings may be obtained, wherein the plurality of qualitative ratings correspond to the plurality of noise sources. Additionally, a plurality of SNRs may be obtained for the plurality of noise sources. Moreover, after assigning individual qualitative ratings to separate noise types, the individual qualitative ratings (QR<NUM>, QR<NUM>,. ) may be combined using a scoring function to arrive at an overall qualitative rating (OQR) in terms of Good, Fair or Poor. Thus, illustrated block <NUM> provides for determining an OQR for the physiological signal.

Block <NUM> may also determine an overall quality level (OQL) for the physiological signal, wherein the OQL may be based on both the individual qualitative analyses (QR<NUM>, QR<NUM>,. ) and the individual quantitative analyses (SNR<NUM>, SNR<NUM>,. More particularly, a dynamic weighting function may combine the individual SNRs into a single value (e.g., ranging from zero to ten). The weights in the weighting function may change dynamically if a certain noise type is present in excessive quantity to tilt the physiological signal quality to unacceptable levels. For example, if an ECG signal is contaminated with only baseline wander noise but in amounts to render the ECG signal unreliable, the weighting function may adjust the weight of this particular noise type relative to other noise types so that due consideration is given to a single excessive noise type over mild-to-moderate amounts of multiple noise types that may be acceptable. The dynamic weighting function may be implemented so that OQL is a function of, <MAT>.

Where n is the number of noise types, Wk is a dynamic weight assigned to a particular noise type and SNRk is the signal to noise ratio of that particular noise type. Thus, in the above expression, the weight Wk of a noise type may dynamically change based upon its corresponding individual qualitative rating (QRk). Block <NUM> may also provide for storing the de-noised physiological signal as well as the qualitative and quantitative information associated with the physiological signal (e.g., individual QRs, OQR, OQL, etc.) for later use.

<FIG> shows a weighting curve <NUM> that may be used to assign weights to individual noise types. In general, if the qualitative rating of a particular noise type is Poor, its associated weight may sharply fall toward zero so as to significantly reduce the SNR contribution of that noise type to the OQL. Such an approach may effectively amplify the presence of a noise type in excessive amounts, by significantly reducing the OQL.

Returning now to <FIG>, a determination may be made at block <NUM> as to whether a quality condition has been satisfied. The quality condition may specify, for example, that no noise type has an individual QR of "Poor", the OQR is either "Good" or "Fair", the OQL is above a certain threshold (e.g., <NUM> out of <NUM>), etc., or any combination thereof. If the quality condition is not satisfied, illustrated block <NUM> determines whether a maximum number of readings (e.g., three) has been reached. If not, a user prompt may be generated at block <NUM>. The user prompt may request one or more additional readings (e.g., "Please take another ECG reading"). The user prompt may also include a recommendation that is tailored to one or more of the plurality of noise sources. For example, in the case of baseline wander noise, the patient might be asked to hold the device lightly and with uniform pressure. In the case of excessive muscle tremor noise, the patient may be asked to relax and support his or her hands. In the case of motion artifact noise, the patient might be asked to remain still or avoid too much chest movement during breathing. In the case of excessive power main interference or EMI, the patient may be asked to change locations and/or power off nearby devices. Other noise type-specific recommendations may also be made. Once the patient has been prompted, the illustrated method <NUM> may be repeated to obtain a plurality of physiological signals associated with a corresponding plurality of readings, and conduct the qualitative and quantitative analyses for each of the plurality of physiological signals. The resulting physiological signals and associated qualitative and quantitative data may be stored for later use, as already noted.

If either the quality condition is satisfied or the maximum number of readings is reached, block <NUM> may select the best physiological signal based on the qualitative and quantitative analysis results, wherein the selected best physiological signal (and associated qualitative and quantitative data) is reported to a remote location at illustrated block <NUM>.

<FIG> shows a logic architecture <NUM> (51a-<NUM>) to evaluate physiological signals in a home health setting. In the illustrated example, a sensor interface 51a receives a physiological signal from a sensor configuration associated with a mobile device and a qualitative module 51b conducts a qualitative analysis for each of a plurality of noise sources in the physiological signal to obtain a corresponding plurality of qualitative ratings. A selection module 51c may use at least the plurality of qualitative ratings to determine whether to report the physiological signal to a remote location.

In one example, the architecture <NUM> also includes a quantitative module <NUM> Id to conduct a quantitative analysis for each of the plurality of noise sources to obtain an overall quality level, wherein the overall quality level may also be used to determine whether to report the physiological signal to the remote location. More particularly, the quantitative module <NUM> Id may assign weights to signal to noise ratios associated with the plurality of noise sources based on the plurality of qualitative ratings.

Additionally, the qualitative module 51b may combine the plurality of qualitative ratings into an overall qualitative rating, wherein the overall qualitative rating is to be used to determine whether to report the physiological signal to the remote location. The illustrated architecture <NUM> also includes a noise extraction module 51e to filter, for each of the plurality of noise sources, the physiological signal, and a noise estimation module <NUM> If to conduct a noise estimation for the filtered physiological signal. The architecture <NUM> may also include a user interface (UI) to generate a user prompt if a quality condition is not satisfied. As already noted, the user prompt may request one or more additional readings and/or include a recommendation that is tailored to one or more of the plurality of noise sources.

Turning now to <FIG>, a computing platform <NUM> is shown. The platform <NUM> may be part of a mobile device having computing functionality (e.g., PDA, laptop, smart tablet), communications functionality (e.g., wireless smart phone), imaging functionality, media playing functionality (e.g., smart television/TV), or any combination thereof (e.g., mobile Internet device/MID). In the illustrated example, the platform <NUM> includes a processor <NUM>, an integrated memory controller (IMC) <NUM>, an input output (<NUM>) module <NUM>, system memory <NUM>, a network controller <NUM>, a sensor configuration <NUM>, mass storage <NUM> (e.g., optical disk, hard disk drive/HDD, flash memory), one or more user interface (UI) devices <NUM> and a battery <NUM> to supply power to the platform <NUM>. The processor <NUM> may include a core region with one or several processor cores <NUM>.

The illustrated IO module <NUM>, sometimes referred to as a Southbridge or South Complex of a chipset, functions as a host controller and communicates with the network controller <NUM>, which could provide off -platform communication functionality for a wide variety of purposes such as, for example, cellular telephone (e.g., Wideband Code Division Multiple Access/W-CDMA (Universal Mobile Telecommunications System/UMTS), CDMA2000 (IS-<NUM>/IS-<NUM>), etc.), WiFi (Wireless Fidelity, e.g., Institute of Electrical and Electronics Engineers/IEEE <NUM>- <NUM>, Wireless Local Area Network/LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications), <NUM> LTE (Fourth Generation Long Term Evolution), Bluetooth (e.g., IEEE <NUM>. <NUM>-<NUM>, Wireless Personal Area Networks), WiMax (e.g., IEEE <NUM>-<NUM>, LAN/MAN Broadband Wireless LANS), Global Positioning System (GPS), spread spectrum (e.g., <NUM>), and other radio frequency (RF) telephony purposes. The IO module <NUM> may also include one or more wireless hardware circuit blocks to support such functionality. Although the processor <NUM> and IO module <NUM> are illustrated as separate blocks, the processor <NUM> and IO module <NUM> may be implemented as a system on chip (SoC) on the same semiconductor die.

The system memory <NUM> may include, for example, double data rate (DDR) synchronous dynamic random access memory (SDRAM, e.g., DDR3 SDRAM JEDEC Standard JESD79-3C, April <NUM>) modules. The modules of the system memory <NUM> may be incorporated into a single inline memory module (SIMM), dual inline memory module (DIMM), small outline DIMM (SODIMM), and so forth.

The illustrated cores <NUM> execute logic <NUM> to evaluate physiological signals in home health settings as already described with respect to <FIG>. Thus, the logic <NUM> may receive physiological signals from the sensor configuration <NUM>, conduct a qualitative analysis for each of a plurality of noise sources in the physiological signals to obtain a corresponding plurality of qualitative ratings, and use at least the plurality of qualitative ratings to determine whether to report the physiological signals to a remote location. The logic <NUM> may also conduct a quantitative analysis for each of the plurality of noise sources to obtain an overall quality level, wherein the overall quality level is also used to determine whether to report the physiological signals to the remote location. User prompts for additional readings may be presented to the patient via the UI devices <NUM>, which may include a display, speaker, and so forth.

Thus, techniques described herein may therefore automatically assess physiological signal quality by measuring contributions due to multiple types of noise. Additionally, rather than relying on a single noise type, techniques may synthesize a holistic signal quality assessment.

Moreover, since noise types may be separately extracted and quantified, it is also possible to point out the exact cause of the noise to the end user/patient. Such an approach may enable the patient to precisely correct the cause of the noise in successive measurements. In addition, a dynamic weighting approach may bias analysis results so that excessive contamination from a single noise source (which is typically unacceptable), over mild/moderate contamination from multiple noise sources (which may be acceptable). Techniques may also enhance performance by selecting the best quality metrics from a set of re-measurement results.

Accordingly, the likelihood of generating clinically acceptable physiological signals may be improved because only those physiological signals with clinically acceptable quality may be sent to the healthcare network for interpretation by a medical professional. Turnaround time for desired medical advice may also be significantly reduced under the techniques described herein. The techniques may also enable individuals with little or no medical or technical training to self- measure their own physiological condition in remote/home settings. Indeed, various risk- mitigation requirements associated with medical standards may be satisfied using the techniques described herein.

Embodiments of the present invention are applicable for use with all types of semiconductor integrated circuit ("IC") chips. Examples of these IC chips include but are not limited to processors, controllers, chipset components, programmable logic arrays (PLAs), memory chips, network chips, systems on chip (SoCs), SSD/NAND controller ASICs, and the like. In addition, in some of the drawings, signal conductor lines are represented with lines. Some may be different, to indicate more constituent signal paths, have a number label, to indicate a number of constituent signal paths, and/or have arrows at one or more ends, to indicate primary information flow direction. This, however, should not be construed in a limiting manner. Rather, such added detail may be used in connection with one or more exemplary embodiments to facilitate easier understanding of a circuit. Any represented signal lines, whether or not having additional information, may actually comprise one or more signals that may travel in multiple directions and may be implemented with any suitable type of signal scheme, e.g., digital or analog lines implemented with differential pairs, optical fiber lines, and/or single-ended lines.

Example sizes/models/values/ranges may have been given, although embodiments of the present invention are not limited to the same. As manufacturing techniques (e.g., photolithography) mature over time, it is expected that devices of smaller size could be manufactured. In addition, well known power/ground connections to IC chips and other components may or may not be shown within the figures, for simplicity of illustration and discussion, and so as not to obscure certain aspects of the embodiments of the invention. Further, arrangements may be shown in block diagram form in order to avoid obscuring embodiments of the invention, and also in view of the fact that specifics with respect to implementation of such block diagram arrangements are highly dependent upon the platform within which the embodiment is to be implemented, i.e., such specifics should be well within purview of one skilled in the art. Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the invention, it should be apparent to one skilled in the art that embodiments of the invention can be practiced without, or with variation of, these specific details. The description is thus to be regarded as illustrative instead of limiting.

The term "coupled" may be used herein to refer to any type of relationship, direct or indirect, between the components in question, and may apply to electrical, mechanical, fluid, optical, electromagnetic, electromechanical or other connections. In addition, the terms "first", "second", etc. are used herein only to facilitate discussion, and carry no particular temporal or chronological significance unless otherwise indicated.

Claim 1:
At least one storage device or storage disk comprising instructions that, when executed, cause at least one processor of a mobile electronic device to at least:
determine an amount of noise associated with a first noise source in physiological signal data collected from a user via one or more human body sensors, the noise associated with the first noise source indicative of interference from a power line in an environment in which the user is located during collection of the physiological signal data;
determine an amount of noise associated with a second noise source in the physiological signal data;
determine a respective signal-to-noise ratio for the amount of noise of each noise source;
identify a noise source that produces excessive noise compared with the remaining noise sources, based on the signal-to-noise ratios;
determine an overall quality level based on a dynamic weighting function that combines the signal-to-noise ratios into a single value;
prompt a user to repeat measuring physiological signal data if the overall quality level is not above a threshold and a maximum number of readings has not been reached,
wherein the user is prompted to adjust the measuring based on a predefined recommendation associated with the identified noise source;
subsequent to prompting the user, repeat determining an amount of noise associated with the first noise source, determining an amount of noise associated with the second noise source, determining a respective signal-to-noise ratio, determining an overall quality level, and conditionally prompting the user.