Patent Description:
For a variety of reasons, the importance of remote health monitoring systems, such as in-home monitoring systems, is increasing. Remote health parameter monitoring of ambulatory patients enables doctors to detect or diagnose health problems, such as heart arrhythmias, that may produce only transient symptoms and therefore may not be evident when the patients visit the doctors' offices. Remote health parameter monitoring is a significant tool available to healthcare providers for reducing hospital readmission rates and to track disease progression. The use of monitoring systems can permit a smooth transition from hospital to home care. Steadily increasing healthcare costs and outpatient populations have created a need to maximize time intervals between office visits.

The relentless pressure to reduce costs in the healthcare industry has required the more efficient use of a healthcare professional's services. As a result, many physicians now regularly prescribe home monitoring of such health parameters as blood pressure, heart rate, blood glucose level, and EKG (electrocardiogram) signals. In addition, health insurance providers are increasingly viewing remote health parameter monitoring as a means to reduce in-patient expenses and overall healthcare costs. An example of remote health monitoring system is known from document <CIT>.

This document provides devices and methods (not covered by the invention) for monitoring patient health parameters using a wearable monitoring device. For example, this document provides device, methods, and algorithms for the personalization of a remote patient monitoring system.

In general, one aspect of this document features a method (not covered by the invention) for monitoring a patient using a computer algorithm operating in a health parameter monitoring system. The method comprises: establishing one or more personalized health parameter templates for the patient, wherein the personalized templates comprise data characterizing a physiological parameter of the patient; storing the personalized templates in memory of the health parameter monitoring system; monitoring the patient's health parameters using the health parameter monitoring system; comparing, by the algorithm, the patient's monitored health parameters to the personalized templates; based on determining that the patient's monitored health parameters are outside of an acceptable range in comparison to the personalized templates, classifying, by the algorithm, the patient's monitored health parameters as abnormal; determining, by the algorithm, whether the patient's monitored health parameters are classified as abnormal because of artifact signal noise of the health parameter monitoring system that effected the patient's monitored health parameters; based on determining that the patient's monitored health parameters were not classified as abnormal because of artifact signal noise, determining, by the algorithm, that the patient has experienced a health event; and based on determining that the patient has experienced a health event, initiating, by the algorithm, countermeasures to be carried out by the health parameter monitoring system.

In various implementations, the health parameter monitoring system may comprise: a sensor patch in contact with a skin surface of the patient, wherein the sensor patch comprises a plurality of sensors for measuring physiologic or pathologic parameters of the patient; a control unit, wherein the control unit is releasably receivable in a cradle of the sensor patch, and wherein the control unit is in electrical communication with the plurality of sensors when the control unit is in the cradle; and a cap, wherein the cap is configured to releasably couple with the sensor patch to detain the control unit in the cradle, and wherein the cap includes a user interface that is configured to provide indications related to the functioning of the health parameter monitoring system. The personalized templates may be related to times of a day. The personalized templates may be related to an activity level of the patient. The personalized templates may be generated by actions of a clinician. The personalized templates may be automatically generated by the health parameter monitoring system. The countermeasures may include alerting the patient. The countermeasures may include alerting a remote monitoring service in communication with the health parameter monitoring system.

In another general aspect, this document features a health parameter monitoring system comprising: a sensor patch in contact with a human skin surface, wherein the sensor patch comprises a plurality of sensors for measuring physiologic or pathologic parameters; a control unit, wherein the control unit is releasably receivable in a cradle of the sensor patch, and wherein the control unit is in electrical communication with the plurality of sensors when the control unit is in the cradle; a cap, wherein the cap is configured to releasably couple with the sensor patch to detain the control unit in the cradle, and wherein the cap includes a user interface that is configured to provide indications related to the functioning of the health parameter monitoring system; and a biometric interface, wherein the biometric interface is configured to detect a biometric characteristic of a user of the health parameter monitoring system.

In various implementations, in response to detecting a particular biometric characteristic of a user the health parameter monitoring system may operate, and in response to not detecting the particular biometric characteristic of a user the health parameter monitoring system may not operate. The particular biometric characteristic may be a fingerprint. The particular biometric characteristic may be a QRS morphology. The particular biometric characteristic may be a bioimpedance.

In another general aspect, this document features a method of operating a health parameter monitoring system. The method comprises: providing the health parameter monitoring system; detecting a particular biometric characteristic of a user; determining whether the detected particular biometric characteristic is sufficiently similar to a template of the particular biometric characteristic; and allowing the health parameter monitoring system to operate in response to determining that the detected particular biometric characteristic is sufficiently similar to the template of the particular biometric characteristic, or not allowing the health parameter monitoring system to operate in response to determining that the detected particular biometric characteristic is not sufficiently similar to the template of the particular biometric characteristic. The health parameter monitoring system comprises: a sensor patch in contact with a human skin surface, wherein the sensor patch comprises a plurality of sensors for measuring physiologic or pathologic parameters; a control unit, wherein the control unit is releasably receivable in a cradle of the sensor patch, and wherein the control unit is in electrical communication with the plurality of sensors when the control unit is in the cradle; a cap, wherein the cap is configured to releasably couple with the sensor patch to detain the control unit in the cradle, and wherein the cap includes a user interface that is configured to provide indications related to the functioning of the health parameter monitoring system; and a biometric interface, wherein the biometric interface is configured to detect a biometric characteristic of a user of the health parameter monitoring system.

Particular embodiments of the subject matter described in this document can be implemented to realize one or more of the following advantages. In some embodiments, an integrated health monitoring system based on acquisition of physiologic and pathologic parameters from sensors can facilitate ambulatory care to promote patient independence and permit a smooth transition from hospital to home care. In some embodiments, the algorithms provided herein can enable enhanced detection of health issues by using personalized templates and algorithms that can identify deviations from normal in the context of an individual patient and in a particular patient context. In some embodiments, the algorithms provided herein can be used to increase the accuracy of the data collected by a health monitoring system by detecting and rejecting some signals as artifact noise. In result, the accuracy and effectiveness of health parameter monitoring systems can be improved, overall healthcare costs can be reduced, and patient health and longevity can be enhanced using the devices and methods provided herein.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described herein. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The details of one or more embodiments are set forth in the accompanying drawings and the description herein.

Like reference numbers represent corresponding parts throughout.

This document provides devices and methods (not covered by the invention) for the remote monitoring of patient health parameters. For example, this document provides devices, methods, and algorithms for the personalization of a remote patient monitoring system. In some embodiments, the remote health parameter monitoring system includes a wearable component and a separate computing device that can communicate with each other as well as with a remote monitoring service. In some embodiments, a controller unit in the wearable component performs the personalized algorithms for artifact rejection and for detection of health monitoring events. In other embodiments, the separate computing device performs the personalized algorithms for artifact rejection and for detection of health monitoring events. In some embodiments, both systems can perform such algorithms. While the methods and algorithms provided herein may be described in the context of particular health parameter monitoring systems, it should be understood that the methods, algorithms, and techniques for personalization of health monitoring systems as described in this document can be applied to other monitoring systems or to the data of other monitoring systems.

In one example embodiment, biometric identifiers and associated algorithms are included in remote patient monitoring systems. Such biometric identifiers can personalize a monitor to a particular patient as prescribed by a clinician. Biometric identifiers such as finger print recognition, facial recognition, voice recognition, and other techniques can be used to personalize a health parameter monitor to a particular patient. Such techniques can be used to prevent unintended individuals from using a monitor that is prescribed for use by a particular patient.

In another example embodiment, during set-up and initialization, a monitoring device can acquire biometric data from the individual using the monitor, and the data can be compared to stored data that was acquired from the intended patient. Algorithms can be applied to determine whether the individual using the monitor is likely the intended patient or not. Biometric data including but not limited to ECG, QRS morphology, QRS amplitude, QRS duration, PR interval, impedance, and the like can be used for such comparisons.

In yet another embodiment, personal templates for a patient can be created by a clinician or automatically by the monitoring system. The personal templates can be physiological biometric data or a combination of physiological biometric data such as ECG, QRS morphology, QRS amplitude, QRS duration, PR interval, impedance, and the like. The personal templates can be stored in the memory of the monitoring system. Real-time-measured health parameters can be compared with the personal templates in attempt to determine if differences between the real-time parameters and the personal templates exist. If differences exist, the real-time parameter data can be analyzed to determine if the differences are attributable to artifact signal noise. If artifact is not determined to be the cause of the differences, the monitoring system can initiate corrective actions related to the real-time health parameter data such as triggering an alarm, collecting additional data, recording an event, notifying a monitoring service, and so on.

A number of algorithms for artifact detection and management are provided herein. The algorithms are based on a variety of different techniques. For example, in one multi-modal algorithm embodiment, the algorithm for artifact elimination uses a sensor such as an accelerometer to identify a heartbeat movement and the actual QRS. Then any signal that is found to be synchronous with the QRS can be used to help direct the search for the next QRS which may be embedded in signal noise, and those signals not synchronous with the QRS can be filtered.

The health parameter data collected from sensors on a patient can be communicated from a wearable monitor device to data collection and analysis systems in a variety of modes. In some implementations, the monitor device can wirelessly transmit data to a cellular telephone that is coupled via a short-range wireless link to a transceiver (e.g., Bluetooth, RF, infrared, etc.), and the transceiver can communicate over a network such as the internet to a remote monitoring server. In some implementations, a control module from the wearable monitor device can be decoupled from the monitor device and coupled to a base station, computing device, docking device coupled to a computing device, and the like. The health parameter data can then be downloaded from the control module to the base station, and the base station can communicate the data to a remote monitoring server over a network (including, for example, internet, Ethernet, telephone landline or cellular phone networks). In some embodiments, a combination of such techniques and other techniques well known in the art can be used to communicate the health parameter data collected by the monitor device to a remote location for data monitoring and analysis.

The sensors used in the monitoring devices provided herein can include a variety of types and configurations. Some sensors are non-invasive. That is, some sensors make contact with the skin surface of the patient. Other sensors penetrate the dermal layers of the patient. Such penetrating sensors may also be referred to herein as "microneedles" or "microsensors. " Microneedles can advantageously eliminate signal interference from the patient's skin in some circumstances. Therefore, microneedles may provide enhanced signal reception for parameters including but not limited to electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), and others.

The monitoring devices provided herein may be used to collect other data types including but not limited to blood pressure, weight, hip waist ratio, oximetry, thoracic, bioimpedance, physical activity, temperature, drug levels, microfluidics (including serum and urine analytes and protein-based assays), respiration rate, heart sounds, voice recordings, heart rate (heart rate), posture, analyte values such as blood glucose, just to provide a few more examples. Movement or activity may be sensed with appropriate accelerometers or multi-axis gyroscopes, such as micro electromechanical system (MEMS) devices. Such collected data may in turn be synthesized using various algorithms to calculate other health status indicators such as QRS complex values, RR intervals, PVC values, arrhythmia, P wave, and others.

<FIG> provide an example wearable modular external monitoring device <NUM> shown in a top view (<FIG>), an exploded top view (<FIG>), and a bottom view (<FIG>). The modular external monitoring device <NUM> depicted includes a sensor patch <NUM>, control unit <NUM>, and a snap-on monitor <NUM>. Sensor patch <NUM> includes a central cradle <NUM> that is a receptacle for releasably receiving control unit <NUM>. With control unit <NUM> installed in cradle <NUM>, snap-on cap <NUM> can be installed onto sensor patch <NUM> over control unit <NUM> to detain control unit <NUM> in sensor patch <NUM> as shown in <FIG>. Snap-on cap <NUM> can engage with complementary physical features on the sensor patch <NUM> so as to snap in place using a mechanical fit, for example. In some embodiments, snap-on cap <NUM> engages with sensor patch <NUM> to create a water-resistant seal therebetween.

When control unit <NUM> is installed in sensor patch <NUM>, electrical connections are made such that control unit <NUM> is in electrical communication with the sensors that are visible on the bottom of sensor patch <NUM>. Sensor patch <NUM> includes, in this example embodiment, an ECG electrode <NUM> and a bioimpedance sensor <NUM>. However, a wide variety of types, configurations, and numbers of sensors can be included in sensor patch <NUM> as described further herein, and as known in the art.

In addition, in some embodiments a GPS system can be included in control unit <NUM>. The inclusion of GPS in monitoring device <NUM> can be advantageous in multiple ways. First, as will be described further herein, the patient's geographical location and movements as determined by the GPS system can be used as a factor for establishing an expected baseline heart activity while the patient is at certain locations and/or undergoing certain movements. Further, GPS can be used to help locate the patient should the patient become indisposed for whatever reason, including a cardiac event or an unrelated event such as a motor vehicle accident. In addition, in combination with accelerometers, the GPS will help define the total distance moved physically by the patient (e.g., when walking, running, using stairs, etc.) as opposed to other means of moving, such as in a vehicle or elevator, for example. Still further, by using GPS to define certain patient movements as from a vehicle or elevator, for example, extraordinary monitoring signals can be potentially attributed to artifact from the interference caused by the vehicle or elevator-related patient movements.

Some portions of modular external monitoring device's <NUM> sensory and monitoring systems are located in sensor patch <NUM>, and other portions are located in control unit <NUM> and snap-on cap <NUM>. For example, in this embodiment sensor patch <NUM> includes the sensor devices, such as ECG electrode <NUM> and bioimpedance sensor <NUM>. A power source such as a battery (not shown), and electrical contacts that mate with complementary contacts on control unit <NUM> can also be included in the sensor patch <NUM>. Control unit <NUM> can include microelectronics including but not limited to a CPU, data storage memory, wireless transceiver, power management circuitry, sensor interface circuitry, alarm devices, and complementary contacts that mate with sensor patch <NUM> and snap-on cap <NUM>. Snap-on cap <NUM>, in addition to contacts that mate with control unit <NUM>, can include user interface devices such as LEDs, a numeric display, a text display, an icon display, audio alarm devices, visual alarm devices, and a combination of such user interface devices.

Sensor patch <NUM> can be made from compliant polymeric materials and can have an adhesive on a bottom surface <NUM>. In some embodiments, sensor patch <NUM> can comprise a material that is well-suited for the convenient placement on the patient's skin, consistent retention thereon, and non-irritating skin contact. For example, sensor patch <NUM> can comprise a soft elastomer such as a thermoplastic elastomer, silicone, or the like.

Snap-on cap <NUM> can include indicator LEDs <NUM> (or another type of user interface). LEDs <NUM> can signal to the patient various messages such as errors, the proper functioning of monitoring device <NUM>, if the monitoring device <NUM> is transmitting data, and the like. Snap-on cap <NUM> can be a polymeric material. In some cases, snap-on cap <NUM> is a more rigid material than sensor patch <NUM>. For example, snap-on cap <NUM> can be made from any suitable material including but not limited to polypropylene, polystyrene, acrylonitrile butadiene styrene (ABS), polycarbonate, PVC, silicone, or the like.

As best seen in <FIG>, control unit <NUM> that includes the memory, CPU, communications, etc. can be reversibly attached/detached to sensor patch <NUM>. Because of this arrangement, a particular control unit <NUM> can be used with multiple properly configured sensor patches <NUM>, and conversely, multiple properly configured control units <NUM> can be used with a particular sensor patch <NUM>. In one example scenario of operating monitoring device <NUM>, a patient can be given two control units <NUM> that are programmed and personalized identically. The control units <NUM> are rotated daily. That is, each day the patient removes a control unit <NUM> and installs the other control unit <NUM>. The following day, the patient repeats the process-again swapping control units <NUM>. In this manner, a particular control unit <NUM> gets used every other day. This usage of control units <NUM> can be independent of the patient's frequency of replacing sensor patches <NUM>.

The control unit <NUM> that is removed from sensor patch <NUM> and is not in use on a particular day is installed into communication with a base station computer system. The base station can be located in the patient's home, at a treatment site, or a combination of such locations. The base station has network access (wired or wirelessly) and a standard AC power supply. In some cases, a cellular phone or other portable computing device can be used instead of the base station. The base station then downloads the health parameter data from control unit <NUM> and either stores the data to the data storage system of the base station or transmits the data to a monitoring service via the network. Further analysis of the data can be performed by the base station, monitoring service, and by health practitioners using the systems. Data can be presented graphically. Trends can be compiled and displayed for analysis. Various types of algorithms can be applied to provide artifact management, arrhythmia detection and other types of data analysis and diagnostic tools.

While control unit <NUM> typically downloads the health parameter data to a base station or equivalent device, in some cases control unit <NUM> while installed in the sensor patch <NUM> can send wireless transmissions to the base station or over cellular networks based on triggering events. Such triggering events can be determined for a particular patient and programmed into control units <NUM> for the patient. For example, a triggering event may be a particular variability in RR over a short time period, or an ECG QRS morphology, or the like.

Referring to <FIG>, a patient <NUM> is illustrated wearing modular external monitoring device <NUM>. Monitoring device <NUM> is adhered to the skin of patient <NUM>. In this example, monitoring device <NUM> is on the chest of patient <NUM> in a position over the sternum to measure heart and respiratory health parameters. This position is less prone to motion artifact than some other locations, because the skeletal and muscle motion above the sternum is generally minimal. Still, in other implementations monitoring device <NUM> is worn on other areas of patient <NUM>. For example, monitoring device <NUM> may be worn on the head, abdomen, back, side, extremities, and other suitable locations on patient <NUM>. The location of monitoring device <NUM> on patient <NUM> will depend on the type of health parameter data to be collected.

Referring to <FIG>, a cross-sectional side view of another example modular external monitoring device <NUM> is depicted on the skin <NUM> of a patient. Monitoring device <NUM> includes microneedles <NUM> that can be employed as sensors, injection devices, sampling devices, and for other like purposes. Microneedles <NUM> can be barbed or otherwise include structures which facilitate adherence to skin <NUM>.

Microneedles <NUM> penetrate the skin <NUM> and the distal tips of the mocroneedles <NUM> reside subdermally. Therefore, microneedles <NUM>, when used as sensors, have enhanced signal reception (e.g., for ECG, EEG, EMG, etc.). The enhanced reception can be due to the elimination of "shielding" by dermal layers to outside interference as well as because of closer proximity to organ to be monitored. In another implementation, microneedles <NUM> have access to interstitial fluid for sensing electrolytes, glucose, oxygen, pH, temperature, and so on. The portions of microneedles <NUM> near sensor patch <NUM> can be insulated portions <NUM> such that the only electrical recording would come from the exposed electrodes at the distal end of microneedles <NUM> that are positioned deeper into the tissue. In some cases, this arrangement can reduce signal artifact caused by patient motion or from intermittent contact between skin <NUM> and a surface electrode (e.g., electrodes <NUM> and <NUM> of <FIG>). Further, there are known electrical potentials that arise from the surface of skin <NUM> which can be a source of electrical noise. The avoidance of recording from the surface of skin <NUM> can decrease or eliminate this source of electrical noise.

In some embodiments, microneedles <NUM> can alternatively be used for drug delivery by injecting medication from a reservoir <NUM> located within or coupled to sensor patch <NUM>. For example, a drug such as a steroid, lidocain, and others can be beneficially administered to the patient to prevent discomfort and inflammation which could otherwise result from the chronic use of sensor patch <NUM> and microneedles <NUM>. In another example, an agent can be delivered from reservoir <NUM> through microneedles <NUM> to treat a patient's particular detected disorder. Drugs such as quinidine, beta-blocker, amiodarone, insulin, and so on can be used in such applications.

Microneedles <NUM> may also include accelerometers at distal tips to help with the control of signal noise from the sensors. For example, movement sensed at microneedle <NUM> tip by an accelerometer can indicate motion and typical signal noise associated with such motion can be anticipated and managed. In some cases, electrical circuitry or software can be used for cancelation, correction, and filtering of the resulting signal to thereby reduce motion artifact. Previous attempts to record signal noise using accelerometers at locations removed from the recording electrode - even by a small amount - have been ineffective due to the lack of correlation between the forces at the accelerometer and at the electrode. An accelerometer in microneedle <NUM>, or at the base of the microneedle <NUM>, can resolve that problem.

In some embodiments, monitor device <NUM> can also include one or more piezoelectric sensors <NUM>. Piezoelectric sensors <NUM> can be used to measure bioimpedance which can in turn provide a useful signal for artifact elimination, arrhythmia detection, determination of respiration rate, and other purposes.

In reference to <FIG>, a modular external monitoring device <NUM> is illustrated including one or more upper accelerometers <NUM> and one or more lower accelerometers <NUM>. In some embodiments, one or more multi-axis gyroscopes can be used in addition to or as a substitute for accelerometers <NUM> and <NUM>.

Including accelerometers <NUM> and <NUM> in monitoring device <NUM> can provide many advantages. In some embodiments, monitoring device <NUM> only captures or analyzes data when motion levels as determined by accelerometers <NUM> and <NUM> are below certain thresholds levels (so as to avoid motion induced artifact). Accelerometers <NUM> and <NUM> can be oriented in multiple arrangements to facilitate several functions (e.g. physiologic monitoring and device context determination). For example accelerometers <NUM> and <NUM> can be incorporated into the monitoring device <NUM> platform as independent sensors, or into the electrodes themselves (as described herein). Integration of motion data at the electrode interface may be beneficial when correlated with motion at a distance - away from the electrodes - this would allow for noise subtraction due to motion of monitoring device <NUM>.

Further uses for accelerometers <NUM> and <NUM> can include physiologic monitoring. For example, physical activity or inactivity - including "learned" activities, can be measured, and correlations of these learned activities with expected changes in other monitored/sensed data inputs can be used to enhance the value of the data collected my monitoring device <NUM>. Signals from accelerometers <NUM> and <NUM> can be used to indicate patient falls, long-term inactivity, and levels of activity. Heart sounds and motion permitting event timing and ECG can be detected by accelerometers <NUM> and <NUM> in some embodiments. Respiration can be determined based on motion of monitor device <NUM>, bioimpedance changes, or both. Accelerometers <NUM> and <NUM> can also be used to determine erect or supine posture. All of these measurements can be combined and cross-checked to determine the presence of artifact and/or increase the sensitivity and specificity of event recording.

Signal noise (artifact) can be very difficult to distinguish from potentially dangerous and rapid heart rhythms. Artifact may be caused by a number of conditions, with the two primary ones being (<NUM>) mechanical motion with subsequent myopotentials and (<NUM>) poor electrode contact. With poor electrode contact, bioimpedance data may be useful particularly when supplemented with data from accelerometers <NUM> and <NUM>. With regard to physical motion, accelerometers <NUM> and <NUM> can be useful for determining that artifact is present secondary to motion data from accelerometers <NUM> and <NUM>. The physical motion that results in ECG artifact can have a characteristic "signature" unique to a specific activity such as walking and other routine activities, such as tremor, or local skeletal muscle contraction. Thus, rather than performing artifact rejection, the monitoring device <NUM> can be programmed to detect artifact and classify it as such using the "noise signature" that results from characteristic ECG signals. These unique activity signatures can be taught to the control unit during registration and thereby enhance signal quality and event detection, by artifact detection and rejection.

The location of the accelerometers <NUM> and <NUM> can be advantageously selected to enable artifact signal noise detection. Integration of accelerometers onto electrodes as described herein provides one beneficial location. For example, in some embodiments a first accelerometer is embedded in a microneedle or other type of sensor, and a second accelerometer is located in the sensor patch (such as on a circuit board of the control unit). Analysis of the relative motion of these two accelerometers would be useful in developing these characteristic "signatures" of particular types of motion. For example, an individual's stride during walking would likely result in similar motion detected by accelerometers in contact with the skin and by accelerometers on the circuit board, whereas as the type of motion associated with myopotentials (e.g., pectoralis motion) or vibratory motion from riding in a car on a bumpy road may result in different signals from the two accelerometers. If these "noise" motion signals occur in the setting of "high heart rates" it can indicate that artifact is likely present. For example, radial motion such as may be expected with a swinging of the arms or movement of the pectoralis would result in a greater translational motion of the upper accelerometer <NUM> than a lower accelerometer <NUM>. This difference can be taken advantage of to define a characteristic signature to each type of motion and thus identify specific activities. For example, if a person is traveling in a car and not otherwise moving, then accelerometer <NUM> would equal accelerometer <NUM> as both accelerometers <NUM> and <NUM> are being equally translated by the car's activity. In contrast, if a person is actively rowing a boat, then accelerometer <NUM> would be greater than accelerometer <NUM>, thus providing for the detection of this type of activity.

Accelerometer <NUM> and <NUM> data in combination with other data such as ECG and impedance data can also be used to indicate poor contact of monitoring device <NUM> with the patient. In turn, monitoring device <NUM> can alert the patient or other personnel, reject certain data signals, and so on. The user of multiple accelerometers <NUM> and <NUM> can be used to enhance this function. For example, if signals from accelerometers <NUM> shows motion analogous to accelerometers <NUM> then skin contact may be lost. Accelerometer <NUM> motion should be less than that of accelerometer <NUM> when monitor device <NUM> is correctly placed and in correct skin contact.

In reference to <FIG>, a modular external monitoring device <NUM> is illustrated including one or more first end accelerometers <NUM> and one or more second end accelerometers <NUM>. In some embodiments, one or more multi-axis gyroscopes can be used in addition to or as a substitute for accelerometers <NUM> and <NUM>.

Similar to the functionality of accelerometers <NUM> and <NUM> as described herein, the relative motion between accelerometers <NUM> and <NUM> can indicate monitor device <NUM> that is being twisted or otherwise configured. Such a motion could indicate a poor contact with the patient's skin such as a detachment of a "wing" or end portion of monitoring device <NUM>.

In some embodiments, a combination of vertically differentiated accelerometers (<NUM> and <NUM>) and horizontally differentiated accelerometers (<NUM> and <NUM>) can be used on a single monitoring device. In addition, other sensors such as a temperature sensor embedded near an electrode can be included to enhance detection of improper placement or detachment of monitoring device <NUM> from the patient's skin. For example, a temperature sensor may indicate a sudden change in temperature or sudden drop in temperature that indicates poor electrode contact with the patient's skin.

Referring to <FIG>, personal templates can be created and used to identify health monitoring events and artifact in accordance with an example personal template algorithm <NUM> as shown. These templates can be used to "personalize" the device to the user-patient. Specific signal patterns associated with the patient and the patient's interactions with the monitoring device can be input into templates that are stored in the monitoring system's memory. For example, personal characteristics such as the patient's anatomy (e.g., fat, tissue motion, muscle interference, gait, etc.), the patient's environment, and the patient's common day-to-day activities can be incorporated into the personal templates.

In general, this personal template algorithm <NUM> is used to establish a change from normal for a particular patient. Algorithm <NUM> can be performed by a health parameter monitoring system that is programmed with the logic described herein. The algorithm may be run on the CPU of the wearable portion of the monitoring system, or on a portable computing device (e.g., a cell phone, tablet computer, PDA, etc.), or on a base station, or on a computing device at a remote monitoring service, and the like, or using a combination of such computing devices.

The personal template algorithm <NUM> operates in general as follows. Health parameter data or combinations of health parameter data can be compiled for a patient using a remote health parameter monitoring system. Various types of physiologic biometric data such as ECG, QRS morphology, QRS amplitude, QRS duration, PR interval, impedance, and the like can be used. Extra-physiologic inputs (e.g., pollen, temperature, smog, gerographic location, season, humidity) can also be combined with the health parameter data to add context. Personal templates can be compiled from the data. The personal templates can be created by a clinician or by the monitoring system automatically. The personal templates can be stored in the memory of the monitoring system. Real-time-measured health parameters can be compared with the personal templates to determine if differences between the real-time parameters and the personal templates exist. If noteworthy differences exist, the real-time parameter data can be analyzed to determine if the differences are attributable to artifact signal noise. If artifact is not determined to be the cause the differences, the monitoring system can classify the situation as a health event.

At operation <NUM>, personal templates of health parameter data for the patient are created by a clinician. In some cases, the data for the personal template can be acquired from the patient's monitoring system, but in some cases other devices are used for collecting the data for the personal template. For example, in some instances a standard in-office <NUM>-lead ECG system, and other such clinical devices, can be used to collect the data to create the personal template. The templates can include parameters and combinations of parameters including but not limited to QRS, heart rate, heart sounds, pulmonary data such as impedance, bold oxygenation, activity, respiration frequency, blood pressure, analyte values (e.g., blood glucose), weight, and so on. The clinician can input the template data into the monitoring system for storage and comparison purposes (as will be explained further herein). These data could be optionally sub-stratified based on time of day, activity level, and other factors that add context to the templates.

At operation <NUM>, personal auto-templates of health parameter data for the patient are automatically created by the monitoring system. Any of a variety of events may trigger the monitoring system to create the personal auto-templates. For example, when a new sensor patch is installed the monitoring system may create a personal auto-template. Or, when a sensor patch is repositioned the monitoring system may create a personal auto-template. The creation of the auto-templates can also take place on a periodic time basis, such as every <NUM> hours, daily, every other day, and so on. In another example, an auto-template can be created whenever the monitoring system is powered on, e.g., during system start-up. In some cases, a remote monitoring service can initiate the creation of an auto-template using communications via a network from the service to the patient's monitor.

In some embodiments, various personal templates can be combined and correlated to each other. For example, personal auto-templates can be combined with personal templates created by a clinician, or a clinician can modify an auto-template. In addition, the dynamic interaction between two or more templates can provide additional information for analysis of disease. For example, heart rate is related to respiratory rate and activity. Therefore, those three templates can be related together and cross-referenced. For example, it is expected that the respiratory rate is high when the heart rate and/or activity level is high, but if the respiratory rate and/or heart rate is high when the activity level is low, a possible disease condition may exist (e.g., asthma, etc.). Higher order integrated templates of multiple physiologic parameters can be created through this technique to detect alterations in homeostasis for a given patient, and for that patient over time (during disease development, etc.).

The process of compiling the personal auto-templates by the monitor can include signal averaging. In one example, the monitor's control unit can verify no motion is occurring (e.g., a supine position indication from an accelerometer or minimal motion indication from the accelerometer). The monitor can then capture multiple signals (e.g., <NUM> QRS complexes). The signals can be averaged and stored. This process can be used to establish a personal template with reduced noise and enhanced accuracy.

The process of compiling the personal auto-templates by the monitor can also include a context of the patient. Therefore, a particular health parameter can have multiple templates that are distinguish from each other based on the context of the patient. For example, contexts can include walking, running, movement causing sensor patch flexure, sensor detachment, external auditory noise, car travel, signal strength decreases, and the like.

At operation, <NUM> the personal templates are stored in the monitoring system. The storage location can be, for example, in the memory located in the wearable component, on a computing system at the patient's home (such as a base station), a portable computing device of the patient (a cell phone, laptop computer, tablet computer, PDA, etc.), or at a remote computing system (e.g., at a monitoring service, cloud-based servers, hospital system, and so on).

At operation <NUM>, the monitoring system operates to acquire the patient's health parameters as described herein. The monitor, in addition to capturing health parameters, can use sensory data to determine a context of the patient. For example, the monitor can determine if the patient has a high activity level (e.g., running) based on heart rate data or respiration rate data. In some embodiments, the determined context can be used as a factor for determining what personal template to compare the real-time acquired health parameter data to.

At operation <NUM>, the real-time acquired health parameter data is compared to the personal templates. In some cases, the data is combined and cross-referenced as a part of the comparison process. That is, multiple sensed data can be correlated and multidimensional plots can be created of these parameters over time.

At operation <NUM>, the monitoring system determines whether the real-time acquired health parameter data is normal, or within an expected range, as compared to the personal templates. In some embodiments, a range of values around the templates can be established as an expected range. For example, if the patient's average heart rate is <NUM> beats per minute, an expected range of about <NUM> to <NUM> beats per minute can be established. In other examples, factor of about +/- <NUM>%, <NUM>%, <NUM>%, <NUM>%, and so on can be used in relation to the baseline template values to establish an expected range.

If the monitoring system determines the real-time acquired health parameter data is normal as compared to the personal templates, the personal template algorithm <NUM> proceeds to monitoring health parameters in operation <NUM>. However, if the monitoring system determines the real-time acquired health parameter data is not normal as compared to the personal templates, the personal template algorithm <NUM> proceeds to operation <NUM>.

At operation <NUM>, the algorithm determines whether the abnormalities of the real-time acquired health parameter data as compared to the personal templates is a result of artifact signal noise. Examples of algorithms to identify the presence of artifact are provided elsewhere in this document. If operation <NUM> determines that the abnormalities of the real-time acquired health parameter data as compared to the personal templates is a result of artifact signal noise, the personal template algorithm <NUM> proceeds to operation <NUM>. At operation <NUM>, the algorithm takes corrective actions to eliminate, reduce, or otherwise manage the artifact. Example techniques to eliminate, reduce, or otherwise manage artifact are provided elsewhere in this document. However, if operation <NUM> determines that the abnormalities of the real-time acquired health parameter data as compared to the personal templates are not a result of artifact signal noise, the personal template algorithm <NUM> proceeds to operation <NUM>.

At operation <NUM>, in response to finding that the abnormalities of the real-time acquired health parameter data as compared to the personal templates are not a result of artifact signal noise, the algorithm <NUM> can trigger the monitoring system to take countermeasures related to a determination by the algorithm that the patient is experiencing a health event. The countermeasures can be a variety of actions, e.g., providing an alarm message to the user, providing an alarm message to a remote monitoring system, providing a message to the user to confirm proper functioning of the wearable monitoring component, recordation of additional types of data, recordation of data more frequently, additional data analysis, and so on. Acute or chronic changes to the personal templates may be used to prompt patient contact and inquiries from the monitoring service or physician.

In regard to multidimensional plots of parameters, if over a period of time the configuration the multidimensional plot changes, an alert can be generated -including identifying a specific perturbation such as higher blood pressure, or lower heart rates, or higher weights versus a non-specific more subtle perturbation only apparent after cross correlation. This technique can be predictive of a more specific perturbation before it becomes overtly manifest, such as subtle changes of heart rate and weight (not immediately outside predefined parameters) but when taken together suggesting early heart failure decompensation for example.

Another example technique for personalization of monitoring systems includes a biometric identifier algorithm. This biometric identifier algorithm can be initiated and activated at various times including during set-up, start-up, or restarting of the monitoring system. In one example, the device acquires and stores certain biometric data of the patient to whom the monitoring system is prescribed. Such biometric data can include but is not limited to ECG QRS morphology, QRS amplitude, QRS duration, PR interval, impedance, finger prints, skin markings such as freckles (e.g., using photo recognition), retinal scan, facial recognition, voice recognition, and so on. These parameters provide unique patterns to the individual patient. These biometrics patterns can be used for the purposes of routinely querying the user and verifying that the device is attached to the correct individual and to ensure personalization.

In another example algorithm, a multi-modal algorithm for artifact detection and management is provided. This algorithm utilizes multiple types of physiologic inputs (e.g., heart rate, ECG, respiration, oximetry, activity, posture, movement, etc.) from multiple sensors (accelerometers, gyroscopes, electrodes, bioimpedance sensors, microneedles, temperature sensors, audio sensors, etc.) to verify and eliminate noise, corroborate signals, reject artifacts, detect events, trigger recordings, and so on. The multi-modal approach can be used to increase the rate of artifact detection (and elimination) thereby improving the overall performance of the monitor system.

Using the multi-modal algorithm, the CPU in the wearable monitor (or another computer system in the overall monitoring system) would be able to select between sensors and algorithms to determine which signal is providing the best data stream. Alternatively, the control unit can use multiple sensors and algorithms to verify or corroborate signals to ensure fidelity and prevent false positives or false negatives in event detection. This approach would allow for cross-checking for artifact noise and signal quality. For example, if the ECG has noise in the heart rate signal, that could be compared with the impedance generated heart rate signal in an attempt to create a true signal for analysis (or rejection). The comparison may also result in confirming that the suspected artifact noise is actually occurring throughout the system and that it is not artifact. In that situation, the signals would be recorded for review by the monitoring service or physician.

This multi-modal algorithm approach can also be used to capture relevant data from one sensor when another is suffering from excess noise. For example, ECG sensors could be used to gather respiration data if accelerometers have high noise levels in their detection of respiration.

In some embodiments, various physiological signals can be integrated with each other using factors to weight these signals based on the quality of the acquisition. For example, signals such as QRS and ECG, bio-impedance, accelerometer, and sound can be combined, while weighting the highest quality signals the most, to define an accurate representation of the heartbeat. The eventual decision as to what and where the heart beat occurs, and hence definition of heart rate, will therefore be based on multiple inputs, with the greatest credibility given to the clearest (or least disrupted) signal. For each sensor input, a signal quality determination can be made and those sensor inputs with a high quality signal can be sent to the control unit for processing (e.g., entered into an arrhythmia detection algorithm), whereas those with a poor quality signal can be labeled as "noise" and sent to the control unit for elimination or other types of signal management (e.g., entered into a personal template algorithm or artifact algorithm).

In some embodiments of the multi-modal algorithm, the variety of sensor inputs (e.g., ECG, impedance, accelerometer, oximeter, etc.) can be weighted in terms of most valuable depending on the physiologic parameter to be measured (e.g., ECG is high for heart rate and rhythm, whereas impedance for heart rate and rhythm would be weighted lower).

While this specification contains many specific implementation details, these should not be construed as limitations on the scope of the claims but rather as descriptions of features that may be specific to particular embodiments.

Claim 1:
A health parameter monitoring system for monitoring a patient (<NUM>), the health parameter monitoring system configured to run a computer algorithm (<NUM>), wherein the health parameter monitoring system is configured to:
store the one or more personalized health parameter templates in a memory of the health parameter monitoring system, wherein the personalized templates comprise data characterizing a physiological parameter of the patient (<NUM>);
monitor the patient's health parameters;
compare, by the algorithm (<NUM>), the patient's monitored health parameters to the personalized templates;
based on determining that the patient's monitored health parameters are outside of an acceptable range in comparison to the personalized templates, classify, by the algorithm (<NUM>), the patient's monitored health parameters as abnormal;
determine, by the algorithm (<NUM>), whether the patient's monitored health parameters are classified as abnormal because of artifact signal noise of the health parameter monitoring system that effected the patient's monitored health parameters;
based on determining that the patient's monitored health parameters were not classified as abnormal because of artifact signal noise, determine, by the algorithm (<NUM>), that the patient (<NUM>) has experienced a health event; and
based on determining that the patient (<NUM>) has experienced a health event, initiate, by the algorithm (<NUM>), countermeasures to be carried out by the health parameter monitoring system, and
wherein the health parameter monitoring system comprises:
a sensor patch (<NUM>, <NUM>) adapted to be in contact with a skin surface of the patient (<NUM>), wherein the sensor patch (<NUM>, <NUM>) comprises a plurality of sensors (<NUM>, <NUM>) for measuring physiologic or pathologic parameters of the patient (<NUM>);
a control unit (<NUM>), wherein the control unit (<NUM>) is releasably receivable in a cradle (<NUM>) of the sensor patch (<NUM>, <NUM>), and wherein the control unit (<NUM>) is in electrical communication with the plurality of sensors (<NUM>, <NUM>) when the control unit (<NUM>) is in the cradle (<NUM>); and a cap (<NUM>), wherein the cap (<NUM>) is configured to releasably couple with the sensor patch (<NUM>, <NUM>) to detain the control unit (<NUM>) in the cradle (<NUM>), and wherein the cap (<NUM>) includes a user interface with a display that is configured to provide indications related to the functioning of the health parameter monitoring system.