SYSTEM AND METHOD FOR ESTIMATING A FERTILITY STATUS OF A WOMAN

Abstract

The invention relates to a system (4) for estimating a fertility status of a woman, particularly for determining a conception probability of a woman, the system (4) comprising:—A wearable device (2A, 2B), comprising at least one sensor (201, 203, 204) configured to record at least one physiological signal from a woman wearing the wearable device (2A, 2B) and to generate sensor data from the at least one physiological signal, wherein the wearable device (2A, 2B) is configured and arranged to provide the sensor data to—An evaluation system (1) configured and arranged to receive and process the sensor data from the wearable device (2A, 2B), wherein the evaluation system (1) is further configured and arranged to classify the sensor data into at least a first group and a second group, wherein the first group is associated to sensor data indicative of a woman having a high fertility status and wherein the second group is associated to sensor data indicative of a woman having a low fertility status.

Claims

1. A system for estimating a fertility status of a woman, particularly for determining a conception probability of a woman, the system comprising: a wearable device, comprising at least one sensor configured to record at least one physiological signal from a woman wearing the wearable device and to generate sensor data from the at least one physiological signal, wherein the wearable device is configured and arranged to provide the sensor data to an evaluation system configured and arranged to receive and process the sensor data from the wearable device, wherein the evaluation system is further configured and arranged to classify the sensor data into at least a first group and a second group, wherein the first group is associated to sensor data indicative of a woman having a high fertility status and wherein the second group is associated to sensor data indicative of a woman having a low fertility status.

2. The system according to claim 1, wherein the system is configured and arranged to record the sensor data from the at least one sensor continuously or intermittently over a period of time, such as days or months, particularly over the period of one or more menstrual cycles of the woman wearing the wearable device, wherein the system is configured and arranged to associate the sensor data to the time at which the sensor data have been generated such that a set of time-associated sensor data is generated, wherein the system is configured and arranged to store the set of time-associated sensor data, wherein the evaluation system is configured and arranged to classify the set of time-associated sensor data into at least a first group or a second group, particularly wherein the first group is associated to time-associated sensor data indicative of a woman having a high fertility status and wherein the second group is associated to time-associated sensor data indicative of a woman having a low fertility status.

3. The system according to claim 1 or 2, wherein the evaluation system is configured and arranged to determine a set of normalized time-associated sensor data from the set of time-associated sensor data and to classify the set of normalized time-associated sensor data into at least the first or the second group, particularly wherein the set of normalized time-associated sensor data is a zero-mean sensor data set, particularly wherein the first group is associated to normalized time-associated sensor data indicative of a woman having a high fertility status and wherein the second group is associated to normalized time-associated sensor data indicative of a woman having a low fertility status.

4. The system according to claim 1 or 2, wherein the physiological signal is at least one of: a temperature, particularly a skin temperature of the woman wearing the wearable device, particularly wherein the at least one sensor comprises a temperature sensor; a conductance of the skin of the woman wearing the wearable device, particularly wherein the at least one sensor comprises a conductance or an impedance sensor; a perfusion, particularly wherein the at least one sensor is an optical sensor configured and arranged to record a photoplethysmogram, particularly wherein the at least one sensor is a pulse oximeter a heart rate, particularly wherein the at least one sensor is an optical sensor configured and arranged to record the heart rate, a breathing rate, particularly wherein the at least one sensor is an optical sensor configured and arranged to record a breathing rate a vascular activity.

5. The system according to claim 1 or 2, wherein the at least one sensor is or comprises a temperature sensor such as a thermometer, an optical sensor, particularly wherein the optical sensor comprises an infrared emitting light source configured to emit light in the wavelength region between 700 nm and 1500 nm, and/or wherein the light source is a green light emitting light source configured to emit light in the wavelength region between 500 nm to 560 nm, a conductance sensor configured to record a skin conductance, an impedance sensor configured to record a skin impedance.

6. The system according to claim 1 or 2, wherein the wearable device is a wrist-wearable sensor device, such as a watch or a smart watch, particularly wherein the at least one sensor is in contact with the skin of the woman wearing the wearable device.

7. The system according to claim 1 or 2, wherein the system, particularly the wearable device, comprises a motion detection sensor generating motion sensor data indicative of movement of the woman, wherein the system, particularly the evaluation system is configured to detect resting phases, particularly sleeping phases of the woman wearing the wearable device from the motion sensor data.

8. The system according to claim 1 or 2, wherein the system is configured and arranged to detect resting phases, particularly sleeping phases of the woman wearing the device, and wherein the evaluation system is configured to use sensor data from the at least one sensor acquired during detected resting phases for classification, particularly wherein the evaluation system is configured to use exclusively sensor data acquired during detected resting phases, particularly wherein the system is configured to acquire sensor data solely during resting phases of the woman wearing the wearable device.

9. The system according to claim 1 or 2, wherein the evaluation system comprises a trained classifier trained to classify the recorded sensor data at least into the first group or the second group, particularly wherein the classifier is a machine learning module, such as a support vector machine, a trained artificial neural network or a random forest classifier

10. The system according to claim 1 or 2, wherein the evaluation system comprises a first model set of time-associated, particularly normalized sensor data associated to the first group and a second model set of time-associated, particularly normalized sensor data associated to the second group, wherein the evaluation system is configured to compare the sensor data, particularly the set of time—associated, particularly normalized sensor data to the first model set and the second model set and to classify the recorded sensor data into the first group or the second group, particularly based on a score value determined from a score function, wherein the score function is configured to determine a similarity between the recorded sensor data and the first and second model set of sensor data, particularly wherein the score function is a chi-square function or a mean square error between the recorded sensor data and the first or the second model set.

11. A computer-implemented method for estimating a fertility status of a woman, particularly with a system according to any of the preceding claims, wherein the method comprises the steps of: recording at least one physiological signal with at least one sensor from a woman, generating sensor data from the recorded physiological signal; classifying the sensor data into at least a first group or a second group, wherein the first group is associated to sensor data indicative of a woman having a high fertility status and wherein the second group is associated to sensor data indicative of a woman having a low fertility status.

12. The method according to claim 11, wherein the sensor data are recorded continuously or intermittently over a period of time, such as days or months, particularly over the period of one or more menstrual cycles of the woman wearing the wearable device, wherein the sensor data are associated to the time at which the sensor data have been generated such that a set of time—associated sensor data is generated, wherein the set of time-associated sensor data is stored, wherein the set of time-associated sensor data is classified into at least a first group and a second group, particularly wherein the first group is associated to time-associated sensor data indicative of a woman having a high fertility status and wherein the second group is associated to time-associated sensor indicative data of a woman having a low fertility status.

13. The method according to claim 11 or 12, wherein resting phases, particularly sleep phases are detected and sensor data are evaluated for resting phases, particularly only for resting phases of the woman, particularly wherein the sensor data acquired during detected resting phases are used for classification, particularly wherein only sensor data acquired during detected resting phases are used for classification, particularly wherein sensor data are acquired solely during resting phases of the woman wearing the wearable device.

14. The method according to claim 11 or 12, wherein a trained classifier is employed to classify the sensor data into at least the first or into the second group, particularly wherein the classifier is machine learning module, such as a support vector machine, a trained artificial neural network or a random forest classifier.

15. The method according to claim 11 or 12, wherein a first model set of time-associated, particularly normalized sensor data associated to the first group and a second model set of time-associated, particularly normalized sensor data associated to the second group are provided, wherein the recorded sensor data, particularly the set of time-associated, particularly normalized sensor data are compared to the first model set and the second model set and classified into the first group or the second group, particularly based on a score value determined from a score function, wherein the score function is configured to determine a similarity between the recorded sensor data and the first and second model set of sensor data, particularly wherein the score function is a chi-square function or a mean square error between the recorded sensor data and the first or the second model set.

Description

Figure Description

[0121] Particularly, exemplary embodiments are described below in conjunction with the Figures. The Figures are appended to the claims and are accompanied by text explaining individual features of the shown embodiments and aspects of the present invention. Each individual feature shown in the Figures and/or mentioned in said text of the Figures may be incorporated (also in an isolated fashion) into a claim relating to the system or method according to the present invention.

[0122] FIG. 1 shows a schematic illustration of an electronic system according to the invention for detecting a plurality of physiological signals, the electronic system comprising a wearable device, in particular a wrist-worn bracelet, and an evaluation system comprising a processing system, an analysing system, a predicting system and a communication system in the wearable device and/or in an external system.

[0123] FIG. 2 shows a schematic illustration of an electronic system according to the invention for detecting a plurality of physiological signals, the electronic system comprising a wearable device, in particular a wrist-worn wearable sensor device associated with an electronic mobile device, and an evaluation system comprising a processing system, an analysing system, a predicting system and a communication system in the wearable device and/or in an external system;

[0124] FIG. 3 shows a schematic illustration of the wearable device;

[0125] FIG. 4 shows conductance of a group of fertile women and a group of sub-fertile women relative to the individual's cycle;

[0126] FIG. 5 shows perfusion measured with infrared of a group of fertile women and a group of sub-fertile women relative to the individual's cycle;

[0127] FIG. 6 shows perfusion measured with a green LED of a group of fertile women and a group of sub-fertile women relative to the individual's cycle;

[0128] FIG. 7 shows the skin temperature of a group of fertile women and a group of sub-fertile women;

[0129] FIG. 8 shows perfusion measured with infrared of a group of fertile women and a group of sub-fertile women;

[0130] FIG. 9 shows perfusion measured with a green LED of a group of fertile women and a group of sub-fertile women;

[0131] FIG. 10 shows conductance of a group of fertile women and a group of sub-fertile women;

[0132] FIG. 11 shows the breathing rate of a group of fertile women and a group of sub-fertile women; and

[0133] FIG. 12 shows the pulse rate of a group of fertile women and a group of sub-fertile women.

[0134] FIG. 1 schematically shows an embodiment of the system 4 according to the invention. The system 4 comprises a computerized evaluation system 1 comprising a processing system 11, an analysing system 12, a predicting or classification system 13, and a communication system 14 configured to receive sensor data from a wearable device 2A. The wearable device 2A can be a single device 2A or, as illustrated in FIG. 2, the device can comprise several particularly mobile devices 2B such as a smart watch 22, associated to an electronic mobile device 23, such as a tablet or mobile phone.

[0135] The wearable device 2A in FIG. 1 comprises a sensor device 21, here a smart watch, which has an integrated sensor system with at least one sensor (not shown) to record the physiological signal(s) of the woman wearing the smart watch. The wearable device 2A is configured for data communication with the evaluation system 1 that is mechanically disconnect from the wearable device 2A, i.e. the evaluation system 1 is arranged in a different housing and in a different location than the wearable device 2A. The evaluation system 1 can be hosted in a cloud, or on a cloud server, having access to databases or sensor data storage (not shown).

[0136] The evaluation system 1 is configured and arranged to perform the following steps: reception of sensor data from the wearable device 2A with the communication system 14, sensor data processing such as normalizing sensor data, receiving time-associated sensor data from a storage device, sensor data analysis, and estimation of the fertility status of the woman wearing the wearable device 2A. The evaluation system is further configured to allow communication with the wearable device 2A.

[0137] As illustrated in FIG. 2, in an alternative embodiment, the wearable device 2B comprises two devices 22, 23, one of which is a wearable sensor device 22 and wherein the other is a mobile electronic device 23 with a display. The wearable sensor device 22 is configured to be worn in close contact with the skin. It can be worn on the wrist, on the finger, on the arm, leg, foot, around the abdomen or the head. The wearable sensor device 22 communicates with the mobile electronic device 23, such as e.g. a mobile phone, a smart watch or tablet computer, for example via close range communication, WiFi or a mobile data network. For close-range communication, the wearable sensor device 22 and the mobile electronic device comprise a Bluetooth communication module (not shown), e.g. a Low Energy Bluetooth module, or another close-range communication module configured for direct data communication with the external mobile device 23.

[0138] As illustrated in FIG. 2, the mobile electronic device 23 is configured to facilitate the data communication between the wearable sensor device 22 and the evaluation system 1, e.g. by relaying the sensor data from the wearable sensor device 22 via a data network 3 to the remote evaluation system 1, for further processing.

[0139] Although not illustrated in FIGS. 1 and 2, the wearable sensor devices 21, 22 further comprise a timer module configured to generate current time and date information, e.g. a clock circuit or a programmed timer module. The timer module is further configured to generate time stamps including the current time and date, such that time-associated sensor data can be generated.

[0140] As illustrated schematically in FIG. 3, the wearable sensor device 21, 22 comprises several sensor systems 200 in a housing 215. Such sensor systems 200 comprises a first sensor system 201 with at least one optical sensor configured to generate photoplethysmography (PPG) signals for measuring heart signals, heart rate, heart rate variability, perfusion, and/or a breathing rate. For example, the first sensor system 201 comprises a PPG-based sensor system for measuring heart signals, heart rate and heart rate variability.

[0141] In yet another embodiment, the PPG signal is used to perform a pulse wave analysis.

[0142] According to the embodiment shown in FIG. 3, the sensor system 200 further comprises a second sensor system 202 with one or more motion detecting sensors such as accelerometers, for measuring body movements (acceleration). For the purpose of sleep phase detection, the motion sensor data from the accelerometers are processed in combination with the sensor data generated from the PPG-based sensor system.

[0143] The sensor system 200 in FIG. 3 further comprises a temperature sensor system 204 for measuring the temperature of the woman wearing the wearable sensor device 21, 22; specifically, the user's skin temperature; more specifically, the skin temperature of the wrist. The temperature sensor system 204 comprises one or more sensors, including at least one temperature sensor, and in an embodiment one or more additional sensor(s) for measuring further parameters like perfusion, bioimpedance and/or heat loss for determining the user's temperature.

[0144] Depending on the embodiment, the sensor systems 200 can further comprise a bioimpedance sensor system 203 with an electric impedance or conductance measuring system. The optical sensors of the first sensor system 201, the bioimpedance sensor system 203, and the temperature sensor system 204 are integrated in a housing 215 of the wearable sensor device 21, 22 and are arranged on a rear side 250 of the wearable sensor device 21, 22, facing the user's skin in a mounted state of the wearable sensor device 21, 22.

[0145] In the mounted state, when the wearable sensor device 21, 22 is actually worn, e.g. on the wrist, just as one would wear a watch, the rear side 250 of the wearable sensor device 21, 22 or the rear side 250 of its housing 215, respectively, is in contact with the skin, e.g. the skin of the wrist. The optical sensors of the first sensor system 201, the bioimpedance system 203, and the temperature sensor system 204 touch the skin or at least face the skin, e.g. the skin of the wrist.

[0146] The wearable sensor device 21, 22 further comprises a data storage 212, e.g. a data memory such as RAM or flush memory, and an operational processor 213 connected to the data storage 212 and the sensor systems 200.

[0147] The wearable sensor device 21, 22 further comprises a communication system 214 connected to the processor 213. Depending on the embodiment, the communication system 214 is configured for data communication with a separate external system 1, as illustrated in FIG. 1, or with a mobile electronic device 23, as illustrated in FIG. 2. Accordingly, the communication system 214 is configured for data communication via a close-range communication interface or other data communication networks. For example, for close range communication, the communication system 214 comprises a Bluetooth communication module, e.g. a Low Energy Bluetooth module, or another close-range communication system configured for direct data communication with the external mobile communication device 23.

[0148] The data network 3 comprises a mobile radio network such as a GSM-network (Global System for Mobile communication), a UMTS-network (Universal Mobile Telephone System), or another mobile radio telephone system, a wireless local area network (WLAN), and/or the Internet.

[0149] As further illustrated in FIG. 3, the wearable sensor device 21, 22 further comprises one or more data entry elements 218 enabling the user to enter data and/or event indications. Depending on the embodiments, data entry elements 218 comprise data entry buttons, keys and/or rotary selection switches. The wearable sensor device 21, 22 is worn in close contact to the skin. The strap 211 can for example be a band, a cuff, a bracelet, an elastic rubber band, a head band, a ring, or a belt.

[0150] FIG. 4 shows a first model set of time-associated normalized sensor data 401 for conductance for a first group comprising women having a high fertility status and a second model set of time-associated normalized sensor data 402 for conductance for a second group comprising sub-fertile women, i.e. having a low fertility status. The sensor data of the women from the first group have been averaged as well as the sensor data of the women from the second group in order to generate the model data sets 401, 402. The first and the second model data set are normalized to the average value for the cycle, such that the sensor data values are transformed to zero-mean values data sets. The model sets of normalized sensor data for conductance significantly differ between the first group of high fertility status and the second group of sub-fertile women. The temporal evolution for the first group shows a local minimum 411 during late luteal phase of the menstrual cycle, while for the second group a local minimum 412 was observed around ovulation during the menstrual cycle.

[0151] Data were acquired during an observational study that analysed changes in physiological signals, such as resting pulse rate, heart rate variability features, breathing rate, temperature, perfusion, and conductance for a total of 268 menstrual cycles of 77 women. The menstrual cycles and the measured physiological signals as well as sensor data were classified as fertile (first group) or sub-fertile (second group) based on the standard medical classification: a couple is considered sub-fertile (or clinically infertile) if it does not conceive within a year of active unprotected intercourse.

[0152] To that end, 34 women did not conceive during the observation (second group, sub-fertile), and 33 women got pregnant during the study (first group, fertile). For the second group, all menstrual cycles were included in the analysis, for the first group, the menstrual cycles when conception took place were excluded, as the behaviour of the physiological signals changes significantly after conception.

[0153] In order to estimate the fertility status of an individual woman, acquired time-associated sensor data for the conductance during the menstrual cycle of this woman are provided to the evaluation system, where the time-associated sensor data might be processed, e.g. normalized by the processing system 11, the analysing system 12 performs an analysis comparing the set of acquired time-associated sensor data generated from the physiological signals of the woman and the first and second model sets of time-associated sensor data for conductance of the first and the second group followed by a classification to the first group or the second group depending to which model set a higher degree of similarity can be established.

[0154] The communication system 14 communicates to the wearable device 2A, 2B e.g. a mobile electronic device 23, smart phone, or tablet computer, that the woman's chances of getting pregnant are high, i.e. that the woman has a high fertility status, if the classification found a higher degree of similarity to the first model set or that the woman's chances of getting pregnant are low, i.e. that the woman has a low fertility status, if the classification found a higher degree of similarity to the second model set.

[0155] If classification is associated with a degree of uncertainty, information about the degree of uncertainty can be transmitted to the wearable device as well.

[0156] FIG. 5 shows the first and the second normalized model sets 501, 502 for perfusion measured with a PPG sensor utilizing an infrared LED. The values were normalized to the individual average value for the menstrual cycle. The local minimum 511 of the graph for the first model set 501 associated to the first group was around the time of ovulation, while the local minimum 512 of the function of graph for the second model set 502 associated to the second group can be observed 5 days later, i.e. in early luteal phase.

[0157] Therefore, estimation of the fertility status can be performed also based on PPG sensor data acquired with an infrared emitting LED for example by identifying the time of occurrence of the local minimum 511, 512.

[0158] FIG. 6 shows the normalized values 601, 602 for perfusion measured with a PPG sensor utilizing a green LED. The gradient of the function of the sub-fertile group was significantly smaller than the gradient of the function of the fertile group.

[0159] In another embodiment of the invention, the system measures perfusion with an infrared LED (perfusion IR) and/or a green LED (perfusion green) during the menstrual cycle of a woman, the physiological signals are processed in the evaluation system 1, the analysing system 12 performs an analysis comparing the set of acquired time-associated sensor data generated from the perfusion signal of the woman and the first and second model sets of time-associated sensor data for perfusion of the first and the second group followed by a classification to the first group or the second group depending to which model set a higher degree of similarity can be established.

[0160] The communication system 14 communicates to the wearable device 2A, 2B e.g. a mobile electronic device 23, smart phone, or tablet computer, that the woman's chances of getting pregnant are high, i.e. that the woman has a high fertility status, if the classification found a higher degree of similarity to the first model set or that the woman's chances of getting pregnant are low, i.e. that the woman has a low fertility status, if the classification found a higher degree of similarity to the second model set.

[0161] If classification is associated with a degree of uncertainty, information about the degree of uncertainty can be transmitted to the wearable device as well.

[0162] In FIGS. 7, 8 and 9, the comparison of the absolute values for temperature 701 (data of first group), 702 (data of second group), perfusion 801 (data of first group), 802 (data of second group) measured with infrared and perfusion 901 (data of first group), 902 (data of second group) measured with green light show that the sensor data values of the second group are lower throughout the menstrual cycle compared to the sensor data from the first group for all physiological signal acquired.

[0163] FIGS. 10, 11 and 12 show that the absolute values for breathing rate 1101 (data of first group), 1102 (data of second group), heart rate 1201 (data of first group), 1202 (data of second group), and conductance 1001 (data of first group), 1002 (data of second group), in the second group are higher throughout the menstrual cycle compared to the sensor data from the first group for these physiological signals.

[0164] Therefore, the invention allows an estimation of the fertility status by evaluating some, all or just one of the physiological signals as described.