The present invention concerns a system for determining a blood pressure of one or a plurality of users.
According to the World Health Organization, one in three adults suffer from hypertension worldwide. Hypertension can lead to severe complications, such as stroke and heart failure. Each year, this illness results in 7.5 million premature deaths worldwide. The paradox of hypertension is that most people suffering from this condition are unaware of it.
Furthermore, the current «gold standard» for blood pressure (BP) measurement is performed with a cuff placed around the arm. This 110 years old technology is cumbersome and leads to low compliance for patients prescribed to self-monitor. As a consequence, healthcare professionals lack access to complete and high-quality data for their diagnosis and the treatment of this disease.
The present disclosure concerns a non-invasive system for monitoring BP of one or a plurality of users in a continuous and accurate fashion.
More particularly, the present disclosure concerns a system for determining a blood pressure (BP) of one or a plurality of users, the system comprising:
for each user, a signal module configured to cooperate with a wearable device destined to be worn on a wrist of the user and comprising a pulsatility sensing unit; the signal module comprising a controlling module configured to control the pulsatility sensing unit such that the pulsatility sensing unit measures a plurality of pulsatility signals at the user's wrist; a processing module configured for processing the pulsatility signals to obtain pulsatility signal data; and a communication module for remotely transmitting said pulsatility signal data;
the system further comprising an external service module including a database storage system for storing in a database the transmitted pulsatility signal data for each of said one or a plurality of users; and a calculating module configured for calculating a BP value for each of said one or a plurality of users, based on the pulsatility signal data stored in the database.
In an embodiment, the pulsatility sensing unit can comprise an optical measuring sensor.
In another embodiment, the controlling module can be configured such that each of said plurality of pulsatility signals is measured during a predetermined measurement time period. The controlling module can comprise a triggering module configured to initiate and end said measurement time period.
In yet another embodiment, the pulsatility signal data corresponds to the measured pulsatility signal or the processing module can be configured to perform a pre-processing step on the measured pulsatility signals such as to obtain said pulsatility signal data.
In yet another embodiment, the signal module can cooperate with the wearable device via a first short range communication link.
In yet another embodiment, the communication module can be comprised in a portable gateway device.
In yet another embodiment, the database storage system can be configured for storing in the database the pulsatility signal data for each user.
In yet another embodiment, the system can be configured for inputting a user-specific information for each of said one or a plurality of users.
In yet another embodiment, the calculating module is configured for calculating the BP value of a user, based on the pulsatility signal data stored in the database.
In yet another embodiment, the system can comprise a display interface configured for displaying the calculated BP value.
The invention will be better understood with the aid of the description of an embodiment given by way of example and illustrated by the figures, in which:
Wearable Device
A possible configuration of the wearable device 1 is illustrated in the cross section view of
In one embodiment, the pulsatility sensing unit 11 may comprise a photoplethysmograph (PPG) sensor array that may measure arterial pulsation, arterial diameter, blood flow and/or blood content. In this embodiment, the pulsatility sensing unit 11 may be arranged on the wristband 15 so that the optical sensor array 11 straddles or otherwise addresses an artery, such as the ulnar artery 111, in the vicinity of the ulna bone 113, or radial artery 112, in the vicinity of the radius bone 114 (as shown in
A detail of a possible configuration of the PPG sensor 11 is shown in
The pulsatility signal 4 measured by the PPG sensor 11 can be defined as a signal containing information on the periodic variation of blood flow and arterial diameter of a given segment of the arterial tree. The periodic variations are typically generated by the arrival of a pressure pulse at the given segment of the arterial tree. In the configuration of
In other arrangements, the wearable device 1 may comprise a strip of material that is to be worn on another body part of the user. Examples of the wearable device 1 may include, but are not limited to an armband, a headband, an ankle bracelet, a choker, and a ring, a helmet, an ear plug, a hearing aid, a headphone, glasses, a shirt, a bra, a garment, a fingertip sensor, a glove, underpants, a socket, a shoe, a wearable sensor, a patch adhering to the skin of the user, a bed sensor, a chair sensor, a toilet sensor, a table sensor, a car sensor, a computer mouse sensor, or any other arrangement intended to measure a pulsatility signal.
For example, the wearable device 1 may include any existing device including an armband device, a smartwatch or any or device worn at a user's wrist and comprising the pulsatility sensing unit 11 adapted to measure pulsatility signals at the user's wrist.
In other arrangements, the wearable device 1 may include, in addition to, or instead of the pulsatility sensing unit 11, other type of sensors, such as a galvanic skin response (GSR) sensor array, a bioimpedance (BioZ) sensor array, an electrocardiography sensor (ECG), a sensor based on radio frequency (RF) detection, a radar sensor, a mechanical sensor, a pressure sensor, an invasive sensor, an intra-arterial sensor, a minimal invasive sensor, a subcutaneous sensor, a tonometer, a strain sensor, a plethysmographic sensor, a microphone, an ultrasound sensor, a capacitive sensor, an electromagnetic sensor, a Raman sensor, or any sensor capable of measuring a pulsatility signal either from the capillary bed of the skin or from any other section of the arterial tree.
Turning back to
A schematic representation of the external service module 5 is shown in
The external service module 5 is typically remote from the signal modules 2. In an embodiment, a communication module 3 is used for transmitting the pulsatility signal data from the signal module 2 to the external service module 5. The external service module 5 can comprise one or a plurality of remote servers (or computers). The one or a plurality of remote servers can be in a single location or the plurality of remote servers can be geographically distributed (such as in a computer network or cloud computing). The database storage system 51, the calculating module 52 and/or the database 53 can be distributed across the plurality of remote servers.
As shown in
Measuring-Triggering
In an embodiment, the controlling module 20 can be configured such that each of said plurality of pulsatility signals is measured during a predetermined measurement time period.
In an embodiment, the controlling module 20 comprises a triggering module 23 (see
The triggering module 23 can control the pulsatility sensing unit 11 according to a trigger parameter. The trigger parameter can be specific to a user.
Examples of trigger parameter can include a trigger signal such as a motion signal representative a user's movement. Such motion signal can be measured by using a motion sensor 12 placed on the user, for example on the wearable device 1. The motion signal can be used for calculating an activity level of the user and the activity level compared to a threshold value. The triggering module 23 can control the pulsatility sensing unit 11 such that a pulsatility signal measurement is initiated or stopped when the activity level is above or below the threshold value.
The motion signal and/or the pulsatility signal 4 can be used for detecting that the wearable device is worn by the user.
The motion sensor 12 can include any one of an inertial measurement unit (IMU), an accelerometer, a gyroscope, magnetometer, or a combination of these devices.
In another embodiment, the trigger parameter includes a trigger signal such as a geolocation information of the user. The geolocation information can be provided by a geolocation sensor 13 (such as a GPS device) worn by the user, for example, a geolocation sensor 13 comprised on the wearable device 1. The geolocation information can be used, for example, to allow measuring with the pulsatility sensing unit 11 when the user is in a given area such as at home, in the working place, and prevent measurements otherwise. The geolocation information can also be provided by any indoor positioning system that is interfaced with the geolocation sensor 13 (such as nodes from a WiFi/LiFi access points, Bluetooth beacons, or any other optical, radio or acoustic localization technology) also worn by the user. The geolocation information can also be provided by any other positioning system integrated in the geolocation sensor 13 (such as magnetic or inertial positioning systems) also worn by the user. The geolocation information can be used, for example, to allow measuring with the pulsatility sensing unit 11 when the user is in a given area such as at home, in the working place, at a certain living environments, and promote or prevent measurements accordingly.
In yet another embodiment, the trigger parameter comprises a trigger signal such as a behavioral information on the user. Behavioral information can comprise: known awake/asleep patterns of the user, predetermined measurement schedule, or specific measurement schedule. Known awake/asleep patterns or active/sedentary patterns can be manually introduced in the system (in the database 53) or learnt by the system. Predetermined measurement schedule can be introduced by a clinician, for instance depending on a drug therapy schedule or depending on the needs of a particular investigation to be performed on the user. The schedule can be fixed for a specific user, or dynamically modified for the investigation needs. Specific measurement schedule can comprise a measurement schedule manually introduced depending on working, home, leisure plans of the user, geo-localization of the user or new events occurring in the user life.
Behavioral information can further comprise a feature connected with the user agenda that automatically adapts the measuring frequency according to the scheduled activities.
In yet another embodiment, the trigger parameter can comprise an activity level, determined from the motion signal, and/or an exercise detection. Here, the trigger parameter can be used in combination with a predetermined time period during which the user is at rest after the activity and/or exercise.
Pulsatility signal measurements performed during “intense” exercise, during activity and/or during any type of mental or cardiovascular stresses are biased and might lead to wrong clinical interpretations. The trigger parameters can thus be used for detecting such stresses, and further used for tracking elapsed time after the end of each of them. The system can then trigger the initiation of a measurement when the elapsed time are above predetermined thresholds. For example, according to guidelines for measuring/monitoring blood pressure (such as “Home Blood Pressure Monitoring Explained” by the British Hypertension Society), the predetermined resting period after activity can be about 5 min and the predetermined resting period after “intense” exercise can be about 30 min.
Indeed, blood pressure measurements should be performed after the user has been resting for at least 5 min, and not having exercised for at least 30 min. Measurements performed during exercise and/or during any type of mental or cardiovascular stresses are biased and may lead to wrong interpretations. The trigger parameters can thus be used for detecting such stresses, and further used for tracking one or several elapsed time periods after the end of each of any type of activity or stress.
In yet another embodiment, the trigger parameter can comprise the activity level and/or an exercise detection in combination with at least another trigger parameter, such as behavioral information, geolocation information, etc. In fact, the trigger parameter can correspond to any criteria define in the guidelines. The triggering module 23 can use the different trigger parameters in a hierarchical fashion, or in a decision tree manner, in order to initiate the pulsatility signal measurement.
In an embodiment, the trigger parameter can be transmitted to the external service module 5 via the communication module 3. The storage device 51 can then be configured to store the transmitted trigger parameter in the database 53.
In yet another embodiment, the triggering module 23 control the pulsatility sensing unit 11 according to a manual input. The communication module 3 can be configured for transmitting the triggering input to the external service module 5.
Pre-Processing
In an embodiment, the processing module 21 can be configured such that the measured pulsatility signals 4 are transmitted to the external service module 5 and stored in the database 53. In other words, no processing is performed on the “raw” pulsatility signals 4.
In an embodiment, the processing module 21 can be configured to perform a pre-processing step on the measured pulsatility signals 4, such that the pulsatility signal data 22 correspond to measured pulsatility signals 4 having been submitted to the pre-process step.
In a variant, the processing module 21 is configured to perform a pre-processing step on the measured pulsatility signals 4 such as to obtain said pulsatility signal data 22.
The pre-processing step comprises a lossless compression of the measured pulsatility signal 4. Examples of such lossless compression is described in the reference: J. Uthayakumar et. al., “A survey on data compression techniques: From the perspective of data quality, coding schemes, data type and applications”, available online 17 May 2018, https://doi.orq/10.1016/j.jksuci.2018.05.006.
Alternatively, the pre-processing step comprises a lossy compression of the measured pulsatility signal 4. An example of such lossless compression is described in the same reference.
Alternatively, the pre-processing step comprises executing an ensemble averaging algorithm on the measured pulsatility signal 4.
A possible ensemble averaging algorithm can comprise the steps of:
identifying individual arterial pulses in a sequence of optical signals (by instance by detecting a local maxima or a local minima of the signals);
selecting a window of data around each identified pulse (by instance, a 1 sec window length);
weighting each individual pulse according to a reliability criteria (by instance, setting to “0” too noisy pulses;
overlapping the identified pulses, weighted by the respective weighing factor; and
estimating the most likely ensemble average representing all the pulses in the sequence of optical signals (by instance, by performing the arithmetic average of the weighted pulses).
Buffering
In an embodiment, the signal module 2 is remote from the wearable device 1. The signal module 2 can communicate with the wearable device 1 via a first short range communication link 25 (see
The first short range communication link 25 can include an optical or radio wave communication device, notably using RFID and near-field communication, the Bluetooth® transmission protocol, Bluetooth Low Energy (BLE), a near field communication protocol (NFC), a proximity card or a WiFi direct connection, Zigbee, power line communication, infrared transmission (IR), ultrasound communication, a Z-wave protocol or any other home automation communication protocol.
The short range communication link 25 can comprise a first short range data buffer 24 (see
The communication module 3 can be configured for remotely transmitting the pulsatility signal data 22 from the signal module 2 to the external service module 5 via a long range communication link 7 (see
The signal module 2 can comprise a long range data buffer 26 adapted to store the pulsatility signal data 22 when they are not transmitted by communication module 3 to the external service module 5. Again, the long range data buffer 26 can store the pulsatility signal data 22 when the signal module 2 and/or the external service module 5 are out of range or any one of them is disabled, or the long range communication link 7 is not available or not working.
The first short range data buffer 24 and long range data buffer 26 can comprise a region of a physical memory storage provided in the signal module 2 used to temporarily store, respectively, the measured pulsatility signals 4 and the pulsatility signal data 22 while not transmitted by the short range communication link 25 and the communication module 3, respectively. The first short range and long range data buffer 24, 26 can be implemented in a fixed memory location in hardware or by using a virtual data buffer in software, pointing at a location in the physical memory.
Other Arrangements of the Wearable Device and the Signal Module
In an embodiment illustrated in
In another embodiment illustrated in
The controlling module 20 can comprise a controlling firmware portion that can be included in the wearable device 1 and configured to cooperate with the wearable device 1.
The controlling module 20 can further comprise a controlling hardware portion that can also be included in the wearable device 1.
In a further possible embodiment, the processing module 21 can comprise a processing firmware portion that can be included in the wearable device 1.
In a further possible embodiment, the triggering module 23 can comprise a processing firmware portion that can be included in the wearable device 1.
It should be understood that the firmware portion may also comprise a middleware, a software portion or any executable code.
The first short range communication link 25 can be placed between the part of the signal module 2 being comprised in the wearable device 1 and the part being remote form the wearable device 1. The first short range data buffer 24 can then be between the part of the signal module 2 being comprised in the wearable device 1 and the first short range communication link 25. In the example of
Portable Gateway Device
In an embodiment, the signal module 2 can be comprised at least in part in a portable gateway device 30. The portable gateway device 30 can comprise an electronic mobile device such as a smartphone, a tablet, a laptop, a desk computer or any other suitable device capable of communicating with the signal module 2 and the external service module 5.
In the embodiment of
The portable gateway device 30 can be configured for remotely transmitting said pulsatility signal data 22 via the long range communication link 7.
In another embodiment shown in
In another embodiment shown in
In a further variant shown in
The portable gateway device 30 can be advantageously used as an inputting means.
For example, the triggering input can be entered via the portable gateway device 30.
In a possible configuration, the motion sensor 12 can be comprised in the portable gateway device 30. For example, the accelerometer or geolocation means in a smartphone can be used for this purpose. Alternatively, the motion sensor 12 can be comprised in any other device such as a commercial fitness tracker.
Database
The database storage system 51 is configured for storing the pulsatility signal data 22 in a database 53.
Turning back to
In the particular example of
The database 53 can further store trigger parameters (indicated by the symbol “tpi” with i=1 to n) corresponding to each user and to each set of pulsatility signal data 22.
The database storage system 51 can be further configured for storing the triggering input in the database 53 (not shown) for each user and set of pulsatility signal data 22.
The database storage system 51 can be configured such that a set of pulsatility signal data 22 corresponding to a user is stored in the database 53 separately from another set of pulsatility signal data 22 corresponding to another user.
User-Specific Information
In an embodiment, the system is configured for inputting a user-specific information for each of said one or a plurality of users.
The user-specific information can comprise any one of: type of wearable device worn by the user, age, weight, ethnics information, gender, skin color, cardiovascular condition, information related to the user's, mood, intake of food and drinks, type of activity performed by the user, drug intakes and dosages, weather, physical condition, alcohol consumption, smoking history, known health issues, health records or a combination thereof.
The user-specific information can further comprise one or a plurality of reference BP measurements. The reference BP measurements can be performed independently from to the measurement made with the pulsatility sensing unit 11.
Independent BP measurement can comprise measurements performed using any appropriate non-invasive measurement technique, including manual measurement by a health care professional, automatic measurement by an automated brachial cuff, automatic measurement by an automated wrist cuff or any appropriate invasive measurement technique, including an invasive arterial line.
In an embodiment illustrated in
In another embodiment illustrated in
In yet another embodiment, the communication module 3 is configured for transmitting said user-specific information to the external service module 5. The database storage system 51 can then be configured such that user-specific information corresponding to each set of pulsatility signal data 22 is stored in the database 53. In particular, user-specific information corresponding to each pulsatility signal data 22 in a set of pulsatility signal data 22 (indicated by the symbol “usi” with i=1 to n) can be stored in the database 53.
Calculating
The external service module 5 further comprises a calculating module 52 configured for calculating a BP value of a user, based the pulsatility signal data 22 stored in the database 53.
The external calculating module 52 can be further configured for calculating a quality index value of the pulsatility signal of the user, based on the pulsatility signal data 22 stored in the database 53. The quality index value of a pulsatility signal can be calculated from the pulsatility signal 4 and can be used for quantifying the quality of the measured signal 4. The quality index value can be calculated from: the signal to noise ratio of the pulsatility signal, the likelihood of the pulsatility signal 4 able to be analyzed by the calculating module 52, the presence of physiological features within the pulsatility signal 4 (for instance, the physiological features described in European patent application EP3226758), or any combination thereof. The quality signal value can also be stored in the database 53 as a user-specific information.
In an embodiment, the calculating module 52 can be configured for calculating the BP value of a user based on the set comprising a plurality of pulsatility signal data 22 obtained for the user.
In a variant, the calculating module 52 can be configured for calculating the BP value of a user, based on a subset of the set comprising a plurality of pulsatility signal data 22. In the example of
In a further embodiment, the calculating module 52 can be configured for the BP value of a user using a subset comprising only one pulsatility signal data 22 (see
In another embodiment shown in
The subset of users can further be clustered by using the clustering procedure disclosed in patent application US20130041268, or any other clustering or classification technique. As clustering space, any combination of the following features can be used: the signal to noise ratio of the pulsatility signal data, the likelihood of the pulsatility signal data 22 able to be analyzed by the calculating module 52, the presence of physiological features within the pulsatility signal (for instance, the physiological features described in European patent application EP3226758), or any other feature that can be calculated from the pulsatility signal data 22. Pulsatility signal data 22 can be automatically clustered from different users.
The clustered pulsatility signal data 22 can then be used altogether to better train the algorithms for those users. The clustered pulsatility signal data 22 are independent of from specific user or of the time pulsatility signal 4 have been measured.
In yet another embodiment, the calculating module 52 can be configured for calculating the BP value of a user by further using the user-specific information. This step is also known as a calibration step, an initialization step or a re-initialization step.
In yet another embodiment, the calculating module 52 can be further configured for calculating the BP value of a user by further using the triggering input.
The calculating module 52 can be configured for calculating the BP value by using on a pulse wave analysis technique, for example such as the pulse wave analysis technique described in European patent application EP3226758.
The calculating module 52 can be further be configured for calculating the BP value by using on a machine learning technique, for example such as described in Fen Miao et. al., “A Novel Continuous Blood Pressure Estimation Approach Based on Data Mining Techniques”, IEEE Journal of Biomedical and Health Informatics, Volume: 21, Issue: 6, pp. 1730-1740, 2017.
In a further embodiment, the calculating module 52 can be configured for calculating BP related physiological parameters from the pulsatility signals including any one of: systolic BP, diastolic BP or mean arterial pressure.
The calculating module 52 can be further configured for calculating additional physiological parameters including any one of: pulse pressure, central pulse wave velocity, peripheral pulse wave velocity, arterial stiffness, aortic pulse transit time, augmentation index, stroke volume, stroke volume variations, pulse pressure variations, cardiac output, systemic vascular resistance, venous pressure, systemic hemodynamic parameters, pulmonary hemodynamic parameters, cerebral hemodynamic parameters, heart rate, heart rate variability, inter-beat intervals, arrhythmias detection, ejection duration, SpO2, SpHb, SpMet, SpCO, respiratory rate, tidal volume, apnea detection, sleep quality, sleep scoring, sleep analysis, bed time, sleep duration, rem sleep time, light sleep time, deep sleep time, time to get up, time to sleep, sleep efficiency, minutes awake after sleep onset, snoring duration, stress indexes, and general cardiovascular and health indexes.
In another embodiment, the database storage system 51 can be configured to store the calculated BP values (represented by the symbol “BPi” with i=1 to n) and/or the additional physiological parameters in the database 53.
Display
In an embodiment, the system can comprise a display interface 8 for displaying the calculated BP values and/or additional physiological parameters stored in the database 53.
The display interface 8 can be comprised in the external service module 5 (see
The display interface 8 can be comprised in the wearable device 1. The external service module 5 can then be configured for transmitting the calculated BP value and/or additional physiological parameters to the wearable device 1 such that the latter can be displayed on the display interface 8.
The display interface 8 can be comprised in the portable gateway device 3. The external service module 5 can then be configured for transmitting the calculated BP value and/or additional physiological parameters to the portable gateway device 3 such that the latter can be displayed on the display interface 8.
The external service module 5 can be further configured for transmitting the calculated BP values to the portable gateway device 3. The transmitted calculated BP values can then be displayed on the portable gateway device 3.
Filing Document | Filing Date | Country | Kind |
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PCT/IB2018/056736 | 9/4/2018 | WO | 00 |