Patent Description:
Capacitive sensors can be used to detect biopotential signals such as ECG (electrocardiogram) or EEG (electroencephalogram) signals. Such sensing systems may comprise a plurality of sensors which may be positioned at different locations on a subject's body.

The present invention is directed to a method according to claim <NUM> as well as to an apparatus according to claim <NUM>. Further embodiments of the invention are defined in dependent claims.

According to various, but not necessarily all, examples of the disclosure there may be provided a method comprising: receiving a first biopotential signal, comprising information indicative of a first type of biopotential, obtained by a first capacitive sensor; receiving a second biopotential signal, comprising information indicative of the first type of biopotential, obtained by a second capacitive sensor, the first capacitive sensor and the second capacitive sensor being positioned at different locations on a subject; synchronising further biopotential signals, comprising information indicative of a second, different type of biopotential, obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor;
wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the further biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.

Synchronising the biopotential signals may comprise determining a delay between the first biopotential signal and the second biopotential signal.

Synchronising the biopotential signals may comprise combining the first biopotential signal and the second biopotential signal and adjusting the time alignment of the biopotential signals to provide a combined signal comprising features. The method may comprise using a machine learning process to recognise the identifiable features.

The first biopotential signal and the second biopotential signal may comprise electrocardiogram signals and the further biopotential signals comprise at least one of; electroencephalogram signals , electro-oculogram signals, electronystagmogram signals, electromyogram signals, electroneurogram signals, or skin potentials.

The method may comprise causing processing of the synchronised biopotential signals wherein the processing comprises at least one of; removing noise from one or more of the biopotential signals, reconstructing at least part of the one or more biopotential signals. The machine learning process may be used to reconstruct at least part of the one or more biopotential signals.

The biopotential signals obtained by the first capacitive sensor and the second capacitive sensor may be received via wireless communication links.

The biopotential signals may be obtained from more than two capacitive sensors.

The method may comprise providing a control signal for at least one of the capacitive sensors wherein the control signal causes the at least one capacitive sensor to be active for a first time period and inactive for a second time period.

The method may comprise providing control signals to the capacitive sensors so that different capacitive sensors are arranged to detect the biopotentials at different times.

According to various, but not necessarily all, examples of the disclosure there may be provided an apparatus comprising: means for receiving a first biopotential signal, comprising information indicative of a first type of biopotential, obtained by a first capacitive sensor; means for receiving a second biopotential signal, comprising information indicative of the first type of biopotential, obtained by a second capacitive sensor, the first capacitive sensor and the second capacitive sensor being positioned at different locations on a subject; means for synchronising further biopotential signals, comprising information indicative of a second, different type of biopotential, obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor; wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the further biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.

According to various, but not necessarily all, examples of the disclosure there may be provided an apparatus comprising: processing circuitry; and memory circuitry including computer program code, the memory circuitry and the computer program code configured to, with the processing circuitry, cause the apparatus to: receive a first biopotential signal, comprising information indicative of a first type of biopotential, obtained by a first capacitive sensor; receive a second biopotential signal, comprising information indicative of the first type of biopotential, obtained by a second capacitive sensor, the first capacitive sensor and the second capacitive sensor being positioned at different locations on a subject; synchronise further biopotential signals, comprising information indicative of a second, different type of biopotential, obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor; wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the further biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.

According to various, but not necessarily all, examples of the disclosure there may be provided a computer program comprising computer program instructions that, when executed by processing circuitry, causes receiving a first biopotential signal, comprising information indicative of a first type of biopotential obtained by a first capacitive sensor; receiving a second biopotential signal, comprising information indicative of the first type of biopotential, obtained by a second capacitive sensor, the first capacitive sensor and the second capacitive sensor being positioned at different locations on a subject; synchronising further biopotential signals, comprising information indicative of a second, different type of biopotential obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor; wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the further biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.

According to various, but not necessarily all, examples of the disclosure there are provided examples as claimed in the appended claims.

Examples of the disclosure relate to methods, apparatus and computer programs which may be used to synchronize biopotential signals obtained from capacitive electrodes. The methods, apparatus and computer programs and computer programs can be used with wireless electrodes. The wireless electrodes may be easier and more convenient to attach to a subject than electrodes which are coupled together by one or more wires or cables.

<FIG> schematically illustrates an apparatus <NUM> which may be used to implement examples of the disclosure.

The apparatus <NUM> illustrated in <FIG> may be a chip, a chip-set or any other suitable arrangement. In some examples the apparatus <NUM> may be provided within any suitable device such as a processing device or a communications device.

The apparatus <NUM> comprises controlling circuitry <NUM>. The controlling circuitry <NUM> may provide means for controlling an electronic device such as processing device or a communications device. The controlling circuitry <NUM> may also provide means for performing the methods, or at least part of the methods, of examples of the disclosure.

The controlling circuitry <NUM> comprises processing circuitry <NUM> and memory circuitry <NUM>. The processing circuitry <NUM> may be configured to read from and write to the memory circuitry <NUM>. The processing circuitry <NUM> may comprise one or more processors. The processing circuitry <NUM> may also comprise an output interface via which data and/or commands are output by the processing circuitry <NUM> and an input interface via which data and/or commands are input to the processing circuitry <NUM>.

The memory circuitry <NUM> may be configured to store a computer program <NUM> comprising computer program instructions (computer program code <NUM>) that controls the operation of the apparatus <NUM> when loaded into processing circuitry <NUM>. The computer program instructions, of the computer program <NUM>, provide the logic and routines that enable the apparatus <NUM> to perform the example methods described. The processing circuitry <NUM> by reading the memory circuitry <NUM> is able to load and execute the computer program <NUM>.

In examples of the disclosure the memory circuitry <NUM> is arranged to store one or more databases <NUM>. The databases <NUM> may be used to store information that can be used to enable a plurality of biopotential signals to be synchronised. In some examples the databases <NUM> may comprise reference signals that can be compared to biopotential signals obtained by a plurality of capacitive electrodes to enable the biopotential signals to be synchronised. The information stored in the databases <NUM> may comprise information which enables a machine learning algorithm to be used to synchronise the biopotential signals.

The computer program <NUM> may arrive at the apparatus <NUM> via any suitable delivery mechanism. The delivery mechanism may be, for example, a non-transitory computer-readable storage medium, a computer program product, a memory device, a record medium such as a compact disc read-only memory (CD-ROM) or digital versatile disc (DVD), or an article of manufacture that tangibly embodies the computer program. The delivery mechanism may be a signal configured to reliably transfer the computer program <NUM>. The apparatus may propagate or transmit the computer program <NUM> as a computer data signal. In some examples the computer program code <NUM> may be transmitted to the apparatus <NUM> using a wireless protocol such as Bluetooth, Bluetooth Low Energy, Bluetooth Smart, 6LoWPan (IPv<NUM> over low power personal area networks) ZigBee, ANT+, near field communication (NFC), Radio frequency identification, wireless local area network (wireless LAN) or any other suitable protocol.

Although the memory circuitry <NUM> is illustrated as a single component in the figures it is to be appreciated that it may be implemented as one or more separate components some or all of which may be integrated/removable and/or may provide permanent/semi-permanent/dynamic/cached storage.

Although the processing circuitry <NUM> is illustrated as a single component in the figures it is to be appreciated that it may be implemented as one or more separate components some or all of which may be integrated/removable.

References to "computer-readable storage medium", "computer program product", "tangibly embodied computer program" etc. or a "controller", "computer", "processor" etc. should be understood to encompass not only computers having different architectures such as single/multi-processor architectures, Reduced Instruction Set Computing (RISC) and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), signal processing devices and other processing circuitry.

As used in this application, the term "circuitry" refers to all of the following:.

The term "circuitry" would also cover, for example and if applicable to the particular claim element, a baseband integrated circuit or applications processor integrated circuit for a mobile phone or a similar integrated circuit in a server, a cellular network device, or other network device.

<FIG> schematically illustrates an example system <NUM> which may be used to implement embodiments of the disclosure. The example system <NUM> comprises a processing device <NUM> and a plurality of capacitive sensors <NUM>.

The processing device <NUM> comprises an apparatus <NUM> comprising controlling circuitry <NUM> which may be as described above. In the example of <FIG> the memory circuitry <NUM> comprises a database <NUM>. In other examples one or more databases <NUM> may be stored in one or more different devices. The processing device <NUM> may be arranged to communicate with the one or more different devices so as to enable the one or more databases <NUM> to be accessed as needed.

The processing device <NUM> also comprises one or more transceivers <NUM>. In the example of <FIG> the transceiver is shown as a single component. It is to be appreciated that separate transmitters and receivers may be provided in some processing devices <NUM> in other examples of the disclosure.

The transceiver <NUM> may comprise any means which enables a communication link <NUM> to be established between the processing device <NUM> and the plurality of capacitive sensors <NUM>. The transceivers <NUM> may enable wireless communication links to be established. The wireless communication links could be any suitable type of communication links such as Bluetooth, Bluetooth Low Energy, Bluetooth Smart, 6LoWPan (IPv<NUM> over low power personal area networks) ZigBee, ANT+, Radio frequency identification, wireless local area network (wireless LAN) or any other suitable types of wireless communication links.

The system <NUM> also comprises a plurality of capacitive sensors <NUM>. In the example of <FIG> two electrodes are shown however it is to be appreciated that any number of capacitive sensors <NUM> could be used in other examples of the disclosure.

The capacitive sensors <NUM> are positioned on the body of a subject <NUM>. In the example of <FIG> the subject <NUM> is a person however examples of the disclosure could also be used for animals. The capacitive sensors <NUM> may be positioned on any suitable location on the subject <NUM>. The location of the capacitive sensors <NUM> may be determined by the biopotential signals that the capacitive sensors <NUM> are arranged to detect. For instance, where the capacitive sensors <NUM> are arranged to detect an ECG signal the capacitive sensors <NUM> may be positioned on the subject's <NUM> chest and where the capacitive sensors <NUM> are arranged to detect an EEG signal the capacitive sensors <NUM> may be positioned on the subject's <NUM> head.

The capacitive sensors <NUM> may comprise any means which may be arranged to capacitively sense a biopotential signal from the subject's body. The capacitive sensors <NUM> may be arranged so that the conductive portions of the capacitive sensors <NUM> are not in direct electrical contact with the subject's body.

The capacitive sensors <NUM> may be wireless capacitive sensors <NUM>. In examples of the disclosure there might be no cables or wires between any of the capacitive sensors <NUM> on the subject <NUM>. Each of the plurality of capacitive sensors <NUM> may therefore be standalone capacitive sensors <NUM> which can operate independently so that each capacitive sensor <NUM> measures a biopotential signal independently of the other capacitive sensors <NUM>.

The capacitive sensors <NUM> may also comprise one or more transceivers which enable the capacitive sensors <NUM> to establish a wireless communication link between the capacitive sensors <NUM> and the processing device <NUM>. This enables the system <NUM> to be arranged without any wires or cables needed to connect the capacitive sensors <NUM> and processing device <NUM> together. This may make the system <NUM> easier and more convenient to use as there are no cables which might restrict the possible positions of the capacitive sensors <NUM>. In other examples a partially wired system <NUM> may be used. The partially wired system <NUM> may comprise some wires connecting one or more of the sensors <NUM> to other components within the system <NUM>. The partially wired system <NUM> might not comprise any wires directly between sensors <NUM>.

The biopotential signals that are measured by the capacitive sensors <NUM> may comprise any time varying electrical signal that is generated by the subject <NUM>. The biopotential signal may comprise an autonomic signal. The autonomic signal may be controlled subconsciously by the subject <NUM>. In some examples the biopotential signals may comprise electrical signals that are generated within the subject's body by the user's heartbeat. In some examples the biopotental signals could comprise electrical activity of the user's brain or other parts of their nervous system. The biopotential signal could comprise at least one of an electrocardiogram signal, electroencephalogram signal, electromyogram signal, electrooculogram signal, electrogastrogram signal, electonystagmogram signal, skin potential signal or any other suitable biopotential signal.

In the example system <NUM> of <FIG> the processing device <NUM> has established a communication link <NUM> directly with each of the capacitive sensors <NUM>. In this example there are no intervening elements between the capacitive sensors <NUM> and the processing device <NUM>. In other examples, one or more additional devices could be provided between the capacitive sensors <NUM> and the processing device <NUM>. For instance the system could comprise a communication device. The capacitive sensors <NUM> may be arranged to transmit the biopotential signals to the communication device via any suitable means. The communication device could then transmit the biopotential signals to the processing device <NUM>.

In the example of <FIG> the processing device <NUM> is provided as a separate device to any of the sensors <NUM>. Each of the sensors <NUM> is arranged to provide signals to the processing device <NUM> via the communication link <NUM>. In other examples one or more sensors <NUM> could be provided within the processing device <NUM>. In such examples the sensors <NUM> which are not part of the processing device <NUM> may provide signals via a communication link <NUM> while the sensors <NUM> which are provided within the processing device <NUM> do not need such a communication link <NUM>.

<FIG> illustrates a method for synchronizing biopotential signals that are obtained using the capacitive sensors <NUM>. The method may be implemented using apparatus <NUM> and systems <NUM> as described above. In some examples the method may be performed by a single processing device <NUM>. In other examples the method mat be performed by two or more distributed processing devices <NUM>.

The method comprises receiving, at block <NUM>, a first biopotential signal obtained by a first capacitive sensor <NUM> and at block <NUM>, receiving a second biopotential signal obtained by a second capacitive sensor <NUM>. The first capacitive sensor <NUM> and the second capacitive sensor <NUM> are positioned at different locations on a subject <NUM>.

The first capacitive sensor <NUM> and the second capacitive sensor <NUM> may be operating independently of each other. There might be no wires or cables connecting the respective capacitive sensors <NUM>. The capacitive sensors <NUM> may be arranged to communicate with the processing device <NUM> however the capacitive sensors <NUM> may be arranged so that there is no direct communication between the capacitive sensors <NUM> themselves.

In examples of the disclosure the biopotential signals obtained by the first capacitive sensor <NUM> and the second capacitive sensor <NUM> are received via wireless communication links <NUM>. In examples of the disclosure the processing device <NUM> may establish a separate communication link <NUM> with each of the capacitive sensors <NUM>. This may ensure that the respective capacitive sensors <NUM> can operate independently of each other.

At block <NUM> the method comprises synchronising biopotential signals obtained by the first capacitive sensor <NUM> and the second capacitive sensor <NUM> by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor <NUM> or the second capacitive sensor <NUM>. Features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the biopotential signals obtained by the first capacitive sensor <NUM> and the second capacitive sensor <NUM>.

Other suitable processes may be used to synchronise the biopotential signals. In some examples synchronising the biopotential signals comprises determining a delay between the first biopotential signal and the second biopotential signal. In such examples the delay may be calculated or estimated and information indicative of the delay may be stored in the memory circuitry <NUM>. The information indicative of the delay can then be retrieved when needed to synchronise biopotential signals obtained by the capacitive sensors <NUM>.

Other suitable methods may be used to determine the delay between the respective biopotential signals. In some examples the process of determining the delay may comprise cross collating the respective biopotential signals and comparing the delay between features in the signals. The features could comprise features of the biopotential signals. For example they could comprise the distinctive maxima and minima of an ECG signal. In some examples the features could comprise noise that is present in all of the bio potential signals obtained by the capacitive sensors <NUM>.

In some examples the biopotential signals could be synchronised without explicitly calculating or estimating the delay. For instance, in some examples synchronising the biopotential signals comprises combining the first biopotential signal and the second biopotential signal and adjusting the time alignment of the biopotential signals to provide a combined signal comprising identifiable features. The identifiable features may comprise any features within the signals that can be recognised by the processing circuitry <NUM>. For example, the identifiable features may comprise any suitable patterns, shape or sequences within the biopotential signals.

In such examples a machine learning process may be used to recognise the identifiable features. The machine learning process may comprise retrieving information stored in one or more databases <NUM> and comparing the combined biopotential signals with the retrieved information. The combined biopotential signals may be considered to be synchronised when the combined signals correspond to the retrieved information. The combined biopotential signals may correspond to the retrieved information when one or more identifiable features in the combined signals match, or are similar to features from signals stored in the database <NUM>.

In some examples the biopotential signals that are synchronised comprise the first biopotential signal and the second biopotential signal. For instance the first biopotential signal and the second biopotential signal may be stored in the memory circuitry and may be synchronised by the processing device <NUM>. The synchronisation may be performed in real time. For example, it may be performed by the processing device <NUM> while the capacitive sensors <NUM> are obtaining biopotential signals from the user and transmitting these to the processing device <NUM>. This may enable the synchronised output signals to be provided immediately or with a very small delay. In other examples the synchronisation may be performed at a later time.

In some examples the biopotential signals that are synchronised comprise further biopotential signals detected by the first capacitive sensor <NUM> and the second capacitive sensor <NUM>. For example, the first biopotential signal and the second biopotential signal may be used to calibrate the system <NUM> and determine how the synchronisation should be performed. Once the system <NUM> has been calibrated the capacitive sensors <NUM> may then be used to obtain further biopotential signals which can then be synchronised as required.

In some examples one or more of the capacitive sensors <NUM> may be moved between the block <NUM> of obtaining the calibration biopotential signals and the block <NUM> of obtaining the further biopotential signals. For instance, to obtain the calibration biopotential signals the capacitive sensors <NUM> may be positioned on the torso of a subject <NUM> and then the capacitive sensor <NUM> could be positioned on the subject's <NUM> head to obtain the further biopotential signals.

In some examples the calibration biopotential signals and the further biopotential signals may comprise information about different types of biopotentials. In some examples the calibration biopotential signals may comprise signals that comprise distinctive features that can be easily recognised by the processing circuitry <NUM> while the further biopotential signals may comprise features that are not as easily recognised by the processing circuitry <NUM>. For instance the first biopotential signal and the second biopotential signal that are used as the calibration signals could comprise ECG signals and the further biopotential signals could comprise electrooculogram (EOG) signals or vice versa. It is to be appreciated that other types of biopotential signals could be used in other examples of the disclosure. Other types of biopotential signals could comprise electroencephalogram signals, electronystagmogram signals, electromyogram signals, electroneurogram signals, or skin potentials or other types of signals.

In some examples of the disclosure once the biopotential signals have been synchronised they may be processed. The processing may be performed by the processing circuitry <NUM> of the processing device <NUM>. In some examples the biopotential signals may be transmitted to one or more other processing devices <NUM> to enable the processing to be performed. The processing may comprise removing noise from one or more of the biopotential signals and/or reconstructing at least part of the one or more biopotential signals and/or any other suitable processing.

The reconstruction of the biopotential signals may comprise any suitable method which enables a partial signal to be reconstructed. In some examples a machine learning process may be used to enable the reconstruction of at least part of the biopotential signals. In such examples the processing circuitry <NUM> may use signals that are stored in the database <NUM> to enable the missing segments of the obtained biopotential signals to be identified and reconstructed.

In some examples the processing of the biopotential signals may enable feedback to be provided to the subject <NUM> or another user of the system <NUM> such as a medical professional. For instance, if the biopotential signals comprise information indicative of the user's heart rate then the processing of the signal may enable information indicative of the heart rate to be provided. In some examples the system <NUM> may be arranged to give a warning output if the heart rate drops outside of a threshold frequency band.

In the example method described above the method comprises receiving two biopotential signals from two capacitive sensors <NUM>. It is to be appreciated that in implementations of the disclosure the method may comprise receiving the biopotential signals from more than two capacitive sensors <NUM>. For example a plurality of capacitive sensors <NUM> may be positioned at a plurality of different locations on the subject <NUM>. Each of the capacitive sensors <NUM> may be operating independently of the other capacitive sensors <NUM> so that there is no direct communication between any of the capacitive sensors <NUM>.

In some examples one or more of the capacitive sensors <NUM> may act as a gateway for one or more other capacitive sensors <NUM> within the system <NUM>. For example a communication link may be established between a first capacitive sensor <NUM> and a second capacitive sensor <NUM>. This may enable information to be provided from the first capacitive sensor <NUM> to the second capacitive sensor <NUM>. The second capacitive sensor <NUM> may then use a communication link <NUM> to transmit the information to a processing device <NUM>.

In some examples the processing device <NUM> may be arranged to provide control signals to one or more of the capacitive sensors <NUM>. The processing device <NUM> may provide the control signals via the wireless communication links <NUM>. The control signals may control the time periods for which the capacitive sensors <NUM> are active. This may enable different capacitive sensors <NUM> to be active for different time periods. For example a first control signal, provided to a first capacitive sensor <NUM>, may cause the first capacitive sensor <NUM> to be active for a first time period and inactive for a second time period while a second control signal, provided to a second capacitive sensor <NUM>, may cause the second capacitive sensor <NUM> to be inactive for the first time period and active for the second time period. This enables different capacitive sensors <NUM> to detect the biopotential signals at different times. In such examples a machine learning algorithm may be used to reconstruct the signals to correct for the time periods where the capacitive sensors <NUM> are inactive.

Having different capacitive sensors <NUM> active at different times may provide for a more efficient sensing system <NUM> because it reduces the power requirements of the system. Having reduced power requirements may also enable the capacitive sensors <NUM> to be worn for a longer period of time and so may enable the information about the biopotential signals to be obtained over longer periods of time. This may also reduce the amount of data that is collected and so reduces the bandwidth required for the communication links <NUM> and may reduce the processing requirements and/or memory requirements for the processing device <NUM>.

In some examples the control signals may also allow for adaptive operation of the capacitive sensors <NUM>. For example, the processing device <NUM> may be arranged to determine optimal operating variables for the capacitive sensors <NUM> and transmit these to the capacitive sensors <NUM> via the communication links <NUM>. The operative variables that are controlled by the control signals could comprise the filters used, the gain applied or any other suitable variables.

<FIG> illustrates another method which may be implemented using apparatus <NUM> and systems <NUM> as described.

At block <NUM> a plurality of biopotential signals are received. In the example method of <FIG> n biopotential signals are received. The n biopotential signals may be received from n capacitive sensors <NUM> which may be as described above. Each of the biopotential signals may comprise time as one of the dimensions.

At block <NUM> the method comprises synchronizing the received biopotential signals. The received biopotential signals could be synchronized using any suitable method. In the example method of <FIG> the biopotential signals may be synchronized by extracting features in the received biopotential signals and using these extracted features to determine the time delay. The biopotential signals can then be synchronized by adding the suitable delay to the respective biopotential signals.

At block <NUM> the method comprises dimensionality reduction. The dimensionality reduction may ensure that the dimensionality of the output signals is fixed for any number of input biopotential signals. The dimensionality reduction may be implemented using principal components analysis, independent components analysis, singular value decomposition, machine learning or any other suitable process.

At block <NUM> the synchronized biopotential signals are processed.

In some examples the processing performed at block <NUM> comprises reducing the noise in the synchronized biopotential signal. The noise that is present in the received biopotential signals could comprise noise caused by internal sources and/or noise caused by external sources. The internal sources may be internal to the body of the subject <NUM>. The internal sources of noise could be caused by motion of the subject <NUM>, muscle signals, poor electrical connection between the capacitive sensors <NUM> and the subject <NUM> or any other suitable source. The external sources of noise could be caused by powerline noise or any other suitable source.

In the example method of <FIG> the processing may comprise machine learning processes. In such examples the machine learning process may enable the biopotential signals to be retrieved from noisy and/or partial signals. This may require machine learning information to be obtained from one or more databases <NUM>. The machine learning information <NUM> that is retrieved from the one or more databases may comprise information <NUM> that has been obtained from a plurality of subjects <NUM>. In some examples the machine learning information <NUM> may comprise a high quality signal. The high quality signal could be obtained using a different system <NUM> to the one used to obtain the biopotential signals in examples of the disclosure. The high quality signal could be obtained using a more reliable and less noisy system. The information from the high quality signals can be used to reconstruct the noisy and/or partial biosignals obtained from the capacitive sensors <NUM>.

In some examples the machine learning information <NUM> may be obtained from the same subject <NUM> being currently monitored. In some examples the machine learning information <NUM> could also comprise, or alternatively comprises, data obtained from one or more other subjects. The machine learning information <NUM> may be determined by using corresponding pairs of input signal and output signal examples to obtain a generalized mapping between them.

The machine learning process may be implemented using fully connected, recurrent or one dimensional convolutional neural network or by using support vector machines using features obtained by discrete cosine transforms, fast Fourier transforms or wavelets or by using any other suitable process.

For instance deep convolutional neural networks can be used as a model between input signals and output signals. The machine learning can be trained using any variant of stochastic gradient descent, where random corresponding pairs of input and output data are shown sequentially to the machine learning training process. The machine learning training process then computes a predicted output and error between predicted output and target output. In some examples error gradients may then be computed with regards to the neural networks weights, and then the weights are adjusted in order to minimize the error between target output and the predicted output. The machine learning training process continues until the model converges or some other external criteria is met. After training, the machine learning process can be used for prediction.

In some examples the machine learning process may also comprise calculating a confidence estimate. This may provide an indication of the amount of error in the processed signal.

At block <NUM> an output signal is obtained. In some examples the output signal is the synchronized biopotential signals. In other examples the output signals may comprise a signal derived from the synchronized biopotential signals. For example, the input biopotential signal could comprise an ECG signal and the output signal could comprise the subject's heart rate or heart rate variability.

In the described examples each of the capacitive sensors <NUM> may have the same clock speed, however, as the capacitive sensors <NUM> are operating independently of each other different capacitive sensors <NUM> may have different reference times. The above described examples enable the reference times to be synchronised by post-processing. In other examples the capacitive sensors <NUM> could have different clock speeds. In such examples additional processes may be used to account for the differences in clock speeds. For examples, dynamic time warping distance based methods or machine learning processes may be used to enable the time alignment between the different capacitive sensors <NUM>.

A technical effect of one or more examples described herein is providing a wireless system <NUM> which can be used to monitor biopotential signals from a subject. As the system is wireless <NUM> this means that the capacitive sensors <NUM> can be positioned on any suitable location on the subject. This may enable different types of biopotential signals to be monitored. This may also allow freedom of movement for the subject <NUM> while the biopotential signals are being measured.

Examples of the disclosure may also enable different types of data to be extracted from biopotential signals. For instance in some examples it may enable both ECG and EOG data to be extracted from the same biopotential signals. This may allow for improved medical diagnostics which may reduce the number of capacitive sensors <NUM> needed and/or the amount of tests that are required on the subject <NUM>.

In this description the term coupled means operationally coupled and any number or combination of intervening elements can exist between coupled components (including no intervening elements).

The term "comprise" is used in this document with an inclusive not an exclusive meaning.

In this brief description, reference has been made to various examples. The use of the term "example" or "for example" or "may" in the text denotes, whether explicitly stated or not, that such features or functions are present in at least the described example, whether described as an example or not, and that they can be, but are not necessarily, present in some of or all other examples. Thus "example", "for example" or "may" refers to a particular instance in a class of examples. It is therefore implicitly disclosed that a feature described with reference to one example but not with reference to another example, can where possible be used in that other example but does not necessarily have to be used in that other example.

Claim 1:
A method comprising:
receiving a first biopotential signal, comprising information indicative of a first type of biopotential, obtained by a first capacitive sensor;
receiving a second biopotential signal, comprising information indicative of the first type of biopotential, obtained by a second capacitive sensor, wherein the first capacitive sensor and the second capacitive sensor are positioned at different locations on a subject, not in direct electrical contact with the subject;
synchronising further biopotential signals, comprising information indicative of a second, different type of biopotential, obtained by the first capacitive sensor and the second capacitive sensor by applying a time adjustment to biopotential signals obtained by at least one of the first capacitive sensor or the second capacitive sensor;
wherein features in at least one of the first biopotential signal and the second biopotential signal are used to synchronise the further biopotential signals obtained by the first capacitive sensor and the second capacitive sensor.