Patent Application
20210134399
The example and non-limiting embodiments of the present invention relate to analysis of a signal that is descriptive of calcium level in a cardiomyocyte as a function of time.
Patient specific, mutation specific or disease specific cardiomyocytes can be obtained with different cell technologies. Non-limiting examples of such technologies include differentiation of cardiomyocytes from reprogrammed stem cells, direct differentiation of cardiomyocytes and different genome altering techniques. Cardiomyocytes so obtained may be employed for study of cardiac functions of various types.
Calcium ions play a fundamental role in cardiac excitation-contraction coupling, which is crucial for a proper cardiomyocyte function and hence for a proper heart function. Depolarization and repolarization of a cardiomyocyte results in cyclically repeated increase and decrease in cytosolic calcium levels. These transient rises and reductions of cytosolic calcium control each cycle of contraction and relaxation of the heart. These calcium transients represent intracellular calcium levels in a cardiomyocyte. The calcium transients may be represented by a calcium signal that represents calcium level in a cardiomyocyte as a function of time.
Analysis of the calcium transients may be carried out, for example, in order to study cardiac functionality. Calcium cycling plays a major role in cardiac contractility and therefore alterations in calcium transients can be seen as contractile dysfunction and arrhythmogenesis associated with cardiac disorders and heart failures. Abnormalities in calcium transients may be seen e.g. as a variation in frequency and amplitude and they can be categorized by their form. By analyzing the calcium transients via inspection of a calcium signal that represents the calcium level in a cardiomyocytes, its cardiac functionality, possible cardiac disorders and drug responses can be studied more thoroughly.
A conventional technique for detection and analysis of abnormal calcium trans-sients involves visual inspection, detection and classification of abnormal transients in a calcium signal by a researcher. Since there are no generally accepted analysis criteria or tools for detection or classification of abnormal calcium transients, this conventional technique leads into subjective results. Moreover, such manual classification process is also relatively slow and repeatability of the process is typically poor.
In related art, WO 2015/158961 A1 discloses a technique for calcium level analysis on basis of a calcium signal that is descriptive of a calcium level in a cell as a function of time. The disclosed technique involves segmenting the calcium signal into a series of sections that each represent a respective peak in the calcium level and analyzing the change in calcium level within these sections in view of one or more detection rules to identify peaks that represent abnormal variations in the calcium level.
The following description further makes references to the following documents:
It is an object of the present invention to provide an analysis technique that enables estimation of a cardiac condition via analysis of a calcium signal in a reliable, objective and repeatable manner.
According to an example embodiment, a method for calcium analysis on basis of a calcium signal that comprises a time series of samples that are descriptive of calcium level in a cardiomyocyte as a function of time is provided, the method comprising identifying calcium peaks in the calcium signal; deriving, for each identified calcium peak, respective values for a plurality of peak characteristics that include at least one of the following: a change in calcium level indicated by the calcium peak, a rate of change in calcium level indicated by the calcium peak, a temporal duration of the calcium peak, and a time difference to an adjacent calcium peak of the calcium signal; classifying each identified calcium peak into one of a plurality of classes on basis of said values derived for the respective peak in dependence of predefined classification information that represents said plurality of classes, wherein each of said plurality of classes represents a respective predetermined cardiac condition; and assigning said cardiomyocyte to one of said plurality of classes in accordance with the respective classifications.
According to another example embodiment, an apparatus for calcium analysis on basis of a calcium signal that comprises a time series of samples that are descriptive of a calcium level in a cardiomyocyte as a function of time is provided, the apparatus comprising means for identifying calcium peaks in the calcium signal; means for deriving, for each identified calcium peak, respective values for a plurality of peak characteristics that include at least one of the following: a change in calcium level indicated by the calcium peak, a rate of change in calcium level indicated by the calcium peak, a temporal duration of the calcium peak, and a time difference to an adjacent calcium peak of the calcium signal; means for classifying each identified calcium peak into one of a plurality of classes on basis of said values derived for the respective peak in dependence of predefined classification information that represents said plurality of classes, wherein each of said plurality of classes represents a respective predetermined cardiac condition; and means for assigning said cardiomyocyte to one of said plurality of classes in accordance with the respective classifications.
According to another example embodiment, an apparatus for calcium analysis on basis of a calcium signal that comprises a time series of samples that are descriptive of a calcium level in a cardiomyocyte as a function of time is provided, the apparatus comprising at least one processor and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to: identify calcium peaks in the calcium signal; derive, for each identified calcium peak, respective values for a plurality of peak characteristics that include at least one of the following: a change in calcium level indicated by the calcium peak, a rate of change in calcium level indicated by the calcium peak, a temporal duration of the calcium peak, and a time difference to an adjacent calcium peak of the calcium signal; classify each identified calcium peak into one of a plurality of classes on basis of said values derived for the respective peak in dependence of predefined classification information that represents said plurality of classes, wherein each of said plurality of classes represents a respective predetermined cardiac condition; and assign said cardiomyocyte to one of said plurality of classes in accordance with the respective classifications.
According to another example embodiment, a computer program for calcium level analysis on basis of a calcium signal that is descriptive of a calcium level in a cardiomyocyte as a function of time is provided, the computer program including one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus at least to carry out the method according the example embodiment described in the foregoing.
The computer program referred to above may be embodied on a volatile or a non-volatile computer-readable record medium, for example as a computer program product comprising at least one computer readable non-transitory medium having program code stored thereon, the program which when executed by a computing apparatus causes the apparatus at least to carry out the method according the example embodiment described in the foregoing.
The exemplifying embodiments of the invention presented in this patent application are not to be interpreted to pose limitations to the applicability of the appended claims. The verb “to comprise” and its derivatives are used in this patent application as an open limitation that does not exclude the existence of also unrecited features. The features described hereinafter are mutually freely combinable unless explicitly stated otherwise.
Some features of the invention are set forth in the appended claims. Aspects of the invention, however, both as to its construction and its method of operation, together with additional objects and advantages thereof, will be best understood from the following description of some example embodiments when read in connection with the accompanying drawings.
The embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings where
As described in the foregoing, the calcium level may serve as an indication of the intracellular calcium level in a cardiomyocyte. The calcium signal 110 is a digital signal comprising a sequence of samples, each representing measured calcium level at a respective time instant. The samples constituting the calcium signal 110 preferably exhibit regular spacing in time, in other words the (digital) calcium signal 110 represents the calcium level at a constant sample rate (i.e. sample frequency). As a typical but non-limiting example, the sample rate may be in the range from 10 to 100 Hz, e.g. 20 Hz and, consequently, the samples of the calcium signal 110 may represent the calcium level at 10 to 100 millisecond intervals, e.g. at 50 millisecond intervals. In other examples, sample rates outside of this range may be employed instead.
The calcium signal 110 may be obtained using any suitable technique known in the art. As an example in this regard, the calcium signal 110 may be obtained on basis of calcium imaging. In this regard, the calcium imaging may be conducted for one or more spontaneously beating, calcium indicator loaded dissociated cardiomyocytes that are perfused with extracellular solution, wherein the calcium indicators comprises fluorescent indicators. Calcium imaging may be based on exciting the fluorescent indicators with a light of suitable wavelength(s) and recording the level or intensity of light emitted by the fluorescent indicators (at a certain wavelength) in response to the excitation. Hence, the recorded information comprises a time series of light level/intensity values, thereby serving as basis for deriving the calcium signal 110 representing the calcium level as a function of time. The calcium signal 110 may be formed e.g. as the recorded light level/intensity obtained on basis of excitation using a single excitation wavelength or as the ratio of recorded light levels/intensities obtained on basis of excitation using two different excitation wavelengths.
The calcium imaging may be based on calcium measurements that are carried out by using e.g. an inverted microscope equipped with an objective that is suitable for capturing light emitted by the fluorescent markers. Moreover, a suitable light source for generating the light serving as excitation for the fluorescent indicator and a suitable imaging device (e.g. a digital camera) for capturing images representing the light emitted by the fluorescent markers may be employed, together with control logic (e.g. a digital signal processor (DSP) with an appropriate software) for operating the light source and the imaging device, for extracting the emitted light levels/intensities on basis of the captured image and for recording (in a memory) the extracted light levels/intensities as a function of time.
As an example of an arrangement for obtaining the calcium signal 110 via calcium imaging as outlined in the foregoing, e.g. the IX70 inverted microscope (by Olympus Corporation, Hamburg, Germany) together with the UApo/340 x20 air objective (by Olympus Corporation) may be employed. Moreover, the images may be capture using e.g. the ANDOR iXon 885 CCD camera (by Andor Technology, Belfast, Northern Ireland) synchronized e.g. with a Polychrome V light source by a real time DSP control unit and TILLvisION or Live Acquisition software (by TILL Photonics, Munich, Germany). The calcium indicators may be excited using light at 340 nanometer (nm) and/or 380 nm wavelength and the emissions are may be recorded at 505 nm wavelength. For further calcium analysis, regions of interest may be selected for spontaneously beating (dissociated) cardiomyocytes and the calcium signal 110 may be acquired as the level/intensity of light emitted in response to the excitation signal at 340 nm or 380 nm wavelengths or as the ratio of the levels/intensities of light emitted in response to excitation signals at 340 nm and 380 nm wavelengths.
The resulting calcium signal 110 may be transferred to an analysis tool or to an analysis device for further analysis for example as a data file (e.g. as a text file) comprising indications of the time instants and corresponding fluorescence intensities or fluorescence intensity ratios. Alternatively, the data file may only include indications of the fluorescence intensities or ratios that represent the fluorescence intensity or ratio at a predefined sample rate. The analysis device/tool may be e.g. an analyzer device that is provided with means for implementing an analysis method described in detail via various examples in the following.
Typically, the calcium level exhibits cyclic variation over time, which results in a time series of transients in the calcium level, represented by a corresponding time series of transients of the calcium signal 110. A calcium transient may also be referred to as a calcium peak. In this text, the terms calcium transient and calcium peak are used interchangeably. As an example, the extract of the calcium signal 110 illustrated in
Normal variation in calcium level that typically does not suggest an unhealthy condition involves constant or substantially constant pattern of change in the calcium level that repeats at constant or substantially constant rate. In the calcium signal 110 such normal variation is represented by a sequence of normal peaks that exhibit constant or substantially constant height and overall shape at regular or substantially regular (temporal) spacing between consecutive peaks of the sequence. However, even for a healthy subject the height, the shape and/or the (temporal) spacing of the peaks of the calcium signal 110 may exhibit small variations and/or gradual evolution over time.
In contrast, in case the calcium signal 110 that fails to indicate constant or substantially constant pattern of change in the calcium level and/or where the pattern of change fails to repeat at constant or substantially constant rate may in some cases serve as an indication of an unhealthy cardiac condition. In this regard, a calcium signal that represents or is likely to represent an unhealthy cardiac condition includes one or more abnormal peaks that substantially differ from a normal peak e.g. in height, in shape and/or in (temporal) spacing to one an adjacent peak, and such a calcium signal is detectable via analysis of peak structure of the calcium signal 110. Moreover, even constant or substantially constant pattern of change in the calcium level that repeats at constant or substantially constant rate may hide an unhealthy cardiac condition that may also be detectable via computational analysis of the peak structure of the calcium signal 110.
As a particular example, analysis of the calcium signal 110 may be carried out in order to determine whether it represents one of a plurality of (i.e. two or more) predetermined cardiac conditions. In this regard, two basic scenarios can be identified:
The one or more unhealthy cardiac conditions considered in the calcium signal analysis may comprise one or more inheritable cardiac conditions, e.g. one or more of the following: catecholaminergic polymorphic ventricular tachycardia (CPVT), which is an exercise-induced malignant arrhythmogenic disorder, long QT syndrome 1 (LQT1), which is an electric disorder of the heart that predisposes patients to arrhythmias and sudden cardiac death, and hypertrophic cardiac myopathy (HCM), a disorder that affects the structure of heart muscle tissue leading to arrhythmias and progressive heart failure. However, CPVT, LQT1 and HCM serve as non-limiting examples of unhealthy cardiac conditions that may be considered in the calcium signal analysis.
An outcome of the calcium signal analysis may comprise, for example, an indication of the most likely one of the cardiac conditions under consideration. In another example, the outcome of the calcium signal analysis comprises respective indications of relative probabilities for the cardiac conditions under consideration. In both these scenarios the outcome of the calcium signal analysis may be provided for further analysis and/or for use as basis for a diagnosis by a medical practitioner.
Before and/or in the course of analysis, the calcium signal 110 under analysis may be pre-classified either as a normal signal or an abnormal signal and an indication of the outcome of the pre-classification may be provided as input for the calcium analysis. In an example, the indication whether the calcium signal 110 under analysis is considered as a normal signal or an abnormal signal may be used in the calcium analysis procedure to select and/or adjust classification of the calcium signal 110 to likely represent one of a plurality of predetermined cardiac conditions.
In an example, such pre-classification may be carried out by a human observer (e.g. a medical practitioner). Such pre-classification may be based e.g. on visual analysis of one or more curves that represent the calcium signal 110 under analysis. In another example, the pre-classification may be carried out by using a computational analysis of the calcium signal 110 under consideration. An example of such pre-classification is described in detail in WO 2015/158961 A1. In a further example, the pre-classification may be a combination of an automated classification by computational analysis and visual inspection by a human observer, e.g. such that the automated classification is verified and corrected where needed via visual inspection by a human expert.
Regardless of the manner of carrying out the pre-classification procedure (e.g. by a human observer, by a computational technique, by a combination of the two), the applied pre-classification criteria may be selected according to desired sensitivity of pre-classifying the calcium signal 110 under consideration as an abnormal one. In an example, the calcium signal 110 may be pre-classified as an abnormal signal in response to identifying at least one abnormal calcium peak therein and pre-classified as a normal signal in response to absence of abnormal peaks. In another example, the calcium signal 110 may be pre-classified as an abnormal signal in response to identifying at least a predefined amount of abnormal peaks therein and pre-classifying the calcium signal 110 as a normal signal otherwise. In the latter scenario, the predefined amount may be defined via an absolute number of abnormal peaks, via a percentage of abnormal peaks, or via combination of the two.
As further background concerning normal and abnormal calcium signals,
Considering the pre-classification of the calcium signal in view of the examples of
In an example, the method 300 may commence from a priori knowledge that the calcium signal 110 under analysis originates from a cardiomyocyte obtained from a person that suffers from an inheritable cardiac condition and the calcium analysis according to the method 300 serves to determine respective relative probabilities of two or more predetermined unhealthy cardiac conditions. In another example, there is no a priori knowledge of the health of the person from whom the cardiomyocyte whose calcium level is represented by the calcium signal 110 under analysis, and the calcium analysis according to the method 300 serves to determine respective relative probabilities of a healthy cardiac condition and one or more predetermined unhealthy cardiac conditions.
The method 300 comprises identifying calcium peaks in the calcium signal 110 under analysis, as indicated in block 320. Conceptually, a calcium peak in the calcium signal 110 is defined by a pair local minima in the calcium signal 110 and the local maxima between these local minima: the local minimum of the pair that appears first in the calcium signal 110 represents the beginning of a peak, the local maxima represents the top of the peak, and the local minimum of the pair that appears later in the calcium signal 110 represents the end of the peak. Consequently, samples of the calcium signal 110 that represent a signal segment from the beginning of the peak to the top of the peak constitute an ascending side (left side) of the peak, whereas samples of the calcium signal 110 that represent a signal segment from the top of the peak to the end of the peak constitute a descending side (right side) of the peak.
As an example in this regard, the illustration (i) in
In an example, identification of the calcium peaks in the calcium signal 110 involve using an automated peak detection, for example the one described in WO 2015/158961 A1. This, however, serves as a non-limiting example of an automated calcium peak detection procedure and another calcium peak detection procedure known in the art may be applied instead. Alternatively, the calcium peak identification may be carried out as a manual procedure by a human expert via visual inspection of a curve that represents the calcium signal 110. In a further example, a combination of the automated peak detection and manual procedure may be employed, e.g. such that outcome of the automated peak detection procedure is followed by a visual inspection by a human expert to verify the outcome of the automated procedure and to correct and/or complement the outcome of the automated procedure where needed.
Regardless of the manner of carrying out the procedure (automated, manual, a combination of the two), the outcome the peak identification comprises information that defines the calcium peaks in the calcium signal 110, e.g. by defining, for each identified peak, the beginning of the peak (e.g. sample position a), the top of the peak (e.g. sample position d) and the end of the peak (e.g. sample position g).
After identification of the calcium peaks in the calcium signal 110, the method 300 proceeds to derivation of respective values of a plurality of peak characteristics for each identified peak of the calcium signal 110, as indicated in block 330. The considered peak characteristics may be descriptive of one or more of the following: a change in calcium level indicated by the peak, a rate of change in calcium level indicated by the peak, a temporal duration of the peak, temporal spacing to an adjacent peak. In a particular example, one or more of the following peak characteristics may be considered:
The exemplifying peak characteristics #1 to #10 introduced in the foregoing are provided in no particular order and the numbering applied therefor merely serves as identification of the listed peak characteristics to enable conveniently referring back to some of these peak characteristics in the following.
To further illustrate some of the peak characteristics described in the foregoing, the illustration (ii) of
After derivation of the peak characteristic values (cf. block 330) for the identified peaks of the calcium signal 110, the method 300 proceeds to classification of the identified peaks into one of a plurality of classes on basis of the peak characteristic values derived for the respective peak, as indicated in block 350. Hence, each calcium peak is separately classified into one of the classes on basis of the peak characteristic values derived therefor. Each of the classes represents a respective predetermined cardiac condition and the classification of the identified calcium peaks to these classes is carried out in dependence of predefined classification information that defines mapping between the peak characteristics and the class. The classification information will be described in more detail in the following.
The classification of block 350 is followed by assignment of the cardiomyocyte that the calcium signal 110 under analysis represents into one or more of the above-mentioned classes in accordance with the classifications, as indicted in block 360. In an example, the assignment involves assigning the cardiomyocyte to represent the class that has the highest number of calcium peaks classified thereto via operation of block 350. In a scenario where there are two or more classes that have the (equal) highest number of calcium peaks classified thereto one of the following approaches may be applied:
The outcome of the assignment (of block 360) may be further output for further analysis and/or for use as basis for a diagnosis by a medical practitioner, as indicated in block 370. In an example, the outcome of the assignment includes an indication of the class to which the cardiomyocyte under analysis (via analysis of the calcium signal measured therefrom) is assigned, which may be considered as an indication of the most likely one of the cardiac conditions under consideration via operation of the method 300. In another example, the outcome of the assignment includes, additionally or alternatively, a respective indication or estimate of relative probability of one or more of the classes considered in the classification procedure of block 360. In this regard, the outcome of the assignment may comprise a respective indication of the ratio between the number of calcium peaks classified into a certain class divided by the overall number of considered calcium peaks e.g. for one or more classes having the highest number of calcium peaks classified thereto or for all classes considered in the classification procedure.
The classification information applied in the classification procedure of block 350 serves to define a mapping between a set of peak characteristic values derived for a calcium peak and the considered classes. The classification information may also be referred to as classification data. In order to enable the mapping, the classification information comprises predefined mapping information derived on basis of training data via a training procedure. The training procedure is typically carried out before operation of the method 300, thereby making the classification information readily available for computationally efficient calcium analysis. Alternatively, the training procedure may be carried out in the course of the method 300 (e.g. as part of the method 300) before proceeding to the assignment of the identified peaks to available classes in block 350. The latter approach is computationally more demanding and also requires access to the training data during operation of the method 300. On the other hand, it enables continuously complementing the training data with new information derived from the calcium peaks under analysis by the method 300, thereby facilitating improved accuracy of the classification procedure via repeated operations of the method 300.
The training data includes a respective sub-set of data for each cardiac condition to be considered in the analysis according to the method 300. Each sub-set includes respective set of peak characteristic values derived for a plurality of calcium peaks that are known to represent the respective cardiac condition. The peak characteristics included in the training data are the same ones that are derived for calcium peaks identified in the calcium signal 110 (in block 330) and that are considered in the classification procedure (in block 350) in the course of the method 300.
If assuming a classification/training procedure that employs K different peak characteristic, the training procedure results in mapping information that, depending on the applied training/assignment approach, defines a respective reference point or a respective partition in a K-dimensional space for each of the considered classes. Furthermore, without losing generality, a set of peak characteristic values derived for a calcium peak (e.g. in block 330) may be (at least conceptually) arranged as a K-dimensional peak vector, where each peak characteristic value is provided as one element of the peak vector, the peak vector thereby defining a point in the K-dimensional space. In this regard, the classification procedure (e.g. block 350) may involve assigning a calcium peak via the point in the K-dimensional space that represents the calcium peak e.g. in one of the following ways (depending on the employed training/assignment approach):
The description of the foregoing briefly outlines examples of classification approaches together with the underlying training procedure that are applicable in context of the method 300. In general, a multitude of applicable training and classification approaches are available in the art and the choice of the most advantageous one depends on the number and characteristics of the underlying cardiac conditions under consideration via the calcium analysis according to the method 300. However, in particular the following classification approaches are found to yield reliable analysis via operation of the method 300 when carrying out the calcium analysis:
The tie-breaking rule referred to in the foregoing in context of block 360 may rely on the training data in order to select one of the two or more classes that have the (equal) highest number of calcium peaks classified thereto via operation of block 350. As a non-limiting example in this regard, the tie-breaking rule may involve the following steps:
The method 300 may optionally further comprise pre-classifying the calcium signal 110 as one of a normal signal or an abnormal signal, as indicated in block. 340. Such pre-classification has been introduced and described via a number of examples in the foregoing. As described therein, pre-classification of the calcium signal 110 as a normal signal does not as such imply a healthy cardiac condition or pre-classification of the calcium signal 110 as an abnormal signal does not imply an unhealthy cardiac condition, but the outcome of the pre-classification may be used in the method 300 to select and/or adjust classification of calcium peaks identified in the calcium signal 110.
As an example in this regard, the method 300 may have access to two different sets of classification data, where a first set is prepared for classification of calcium signals pre-classified as normal signals, while a second set is prepared for classification of calcium signals pre-classified as abnormal signals. Consequently, the pre-classification of block 340 results in selecting the first set of classification data in response to finding the calcium signal 110 to represent a normal signal and selecting the second set of classification data in response to finding the calcium signal 110 to represent an abnormal signal. In this regard, the first set of classification data may be prepared, e.g. according to the training procedure outlined in the foregoing, using training data that comprises peak characteristic values extracted from calcium signals and/or calcium peaks that represent normal calcium signals, whereas the second set of data may be prepared using training data that comprises peak characteristic values extracted from calcium signals and/or calcium peaks that represent abnormal calcium signals. Preferably, the same or similar criteria as in pre-classification of block 340 is applied also in selection of the calcium signals and/or calcium peaks for use as the training data in order to ensure properly accounting for possible differences in peak characteristic values between calcium signals considered as normal and abnormal.
In another example, the method 300 may employ a single set of classification data but e.g. the reference points or the partitions that at least in part define the classification within the classification data are adjusted in accordance with the outcome of the pre-classification. As an example in this regard, reference points in the classification data may be defined for a calcium signal that is pre-classified as a normal signal and used as such in response to finding the calcium signal 110 to represent a normal signal, whereas the reference points may be adjusted in a predefined manner for use for a calcium signal that is pre-classified as an abnormal signal and the adjusted reference points are applied instead in response to finding the calcium signal 110 to represent an abnormal signal.
Several experiments have been carried out to validate the approach described in the foregoing via a number of examples. These experiments involve, among others, the following:
Moreover, the experiments outlined in the foregoing we repeated using several different classification techniques known in the art, including the random forests classifier, the BT-LSSVM classifier with a RBF and the k-NN classifier together with a number of other classification techniques known in the art, where the classification techniques identified herein were found to yield the best classification performance.
Throughout these experiments the results indicate that good classification performance can be obtained by using a single peak characteristic. As an example in this regard, the classification (e.g. via the method 300) may consider only a single peak characteristic considered in the classification, where the single peak characteristic comprises one of the exemplifying peak characteristics #3, #4 or #10.
On the other hand, the experimental results further indicate that classification performance can be improved by using two or three peak characteristics. Hence, in another example two or three peak characteristics are considered in the classification, where the considered peak characteristics comprise one of the following combinations:
The experimental results further indicate that at least some further improvement in the classification performance can be obtained by using more than three peak characteristic. Hence, in a further example, the peak characteristics #1 to #10 are considered in the classification.
The method 300 may be implemented by an apparatus or device, and such apparatus/device may be referred to as calcium analyzer.
Although in
A portion of the program code stored in the memory 630, when executed by the processor 620, may be arranged to provide an operating system arranged to control operation of the computing apparatus 600. Another portion of the program code stored in the memory 630 may be arranged, when executed by the processor 620, together with the operating system to provide a user interface that allows the user to operate the computing apparatus 600 with the aid of the I/O components 650. Hence, the processor 620 may be arranged to control operation of the computing apparatus 600 in accordance with program code stored in the memory 630 and/or in accordance with user input received via the user interface.
The memory 630 may be further arranged to store a computer program code 635 comprising one or more sequences of one or more instructions that, when executed by the processor 620, causes the computing apparatus 600 to implement at least some of operations, procedures, functions and/or methods described in the foregoing in context of the method 300. The computer program code 635 may constitute that a stand-alone computer program that is executable by the computing apparatus 600 independently of further applications in the framework provided by the operating system. As another example, the computer program code 635 may comprise instructions that are executable in context of another application provided in framework provided by the operating system. An example of such another application is a browser application.
The components of the computing apparatus 600, e.g. the processor 620, the memory 630, the communication interface 640 and the I/O components 650, are typically interconnected by a bus 660. The bus 660 is arranged to provide electrical connection(s) between components of the computing apparatus 600 for transfer of control information, address information and/or data. The computing apparatus 600 serves as an illustrative and non-limiting example of an apparatus that is suitable for executing the program code 635 arranged to implement at least some of operations, procedures, functions and/or methods described in the foregoing in context of the method 300. Hence, an apparatus comprising additional components and/or an apparatus not comprising all components described in context of the computing apparatus 600 may be employed instead.
Features described in the preceding description may be used in combinations other than the combinations explicitly described. Although functions have been described with reference to certain features, those functions may be performable by other features whether described or not. Although features have been described with reference to certain embodiments, those features may also be present in other embodiments whether described or not.
| Number | Date | Country | Kind |
|---|---|---|---|
| 20175003 | Jan 2017 | FI | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/FI2017/050945 | 12/28/2017 | WO | 00 |
