Technical field of the Invention

The proposed technology relates to seismocardiography and techniques for assisting in diagnosing heart failure .

Background of the Invention

Seismocardiography ( SCG) is the analysis o f sub-audible low- frequency vibrations at the chest wall caused by the beating heart . More generally, SCG relates to non-invasive measurement of accelerations in the chest wall produced by myocardial movement . Heart sounds are audible components of the chest wall vibrations that typically are above 40- 60 Hz , while SCG frequencies are typically below 5 Hz .

SCG is typically measured using an accelerometer . However, when an accelerometer is used, both low frequency SCG components and audible components are simultaneously sampled . The SCG components and the audible components reveal di f ferent cardiovascular functions , thus enabling di f ferent approaches to diagnosing a cardiovascular function . For example , SCG is typically suitable for estimation of time intervals between features in the cardiac cycle , while heart sounds are appropriate for detection of murmurs caused by flow disturbances .

Heart failure does not have a single origin but can be caused by di f ferent underlying conditions . Therefore , heart failure can alter SCG signals in many ways . For example , amplitude measures can both increase and decrease due to heart failure . This poses a challenge in identi fying heart failure in an SCG signal of a subj ect .

Obj ect of the Invention

An obj ect of the present invention is to provide an improved technology for identi fying heart failure . I t is a further obj ect to provide a technology that is reliable and easy to use . Summary of the Invention

According to a first aspect of the proposed technology, a method for determining an indication of heart failure of a subj ect , or person, is proposed . The method comprises : obtaining a first signal interval from a source signal recorded with an accelerometer placed on the chest of a subj ect ; input ting the first signal interval into a first autoencoder, wherein the first autoencoder is trained on the corresponding first signal intervals obtained from healthy subj ects and outputs a reconstructed first signal interval ; determining a first correlation, or first error, between the first signal interval and the reconstructed first signal interval ; and determining the indication of heart failure based on the first correlation . Worded di f ferently, a method is proposed for determining an indication of heart failure of a subj ect , or person, based on a first signal interval from a source signal recorded with an accelerometer placed on the chest of a subj ect , wherein the method comprises : input ting the first signal interval into a first autoencoder, wherein the first autoencoder is trained on the corresponding first signal intervals obtained from healthy subj ects and outputs a reconstructed first signal interval ; determining a first correlation, or first error, between the first signal interval and the reconstructed first signal interval ; and determining the indication of heart failure based on the first correlation .

Wording the last two steps of the methods di f ferently, the method comprises : determining, or estima ting, the indication of heart failure based on a correlation, or error, between the first signal interval and the reconstructed first signal interval . The first signal interval may correspond, or correlate in time , to a first subinterval of a heart cycle . Alternatively, it may correspond to , or correlate in time , to a complete heart cycle .

According to a second aspect of the proposed technology, a system for determining an indication of heart failure of a subj ect is proposed, wherein the system comprises : (A) an accelerometer configured to be placed on the chest of a subj ect , and (B ) a processor operatively connected to the accelerometer and configured to perform the method according to the first aspect of the proposed technology .

According to a third aspect of the proposed technology, a system for determining an indication of heart failure of a subj ect is proposed . The system comprises a plurality of modules . It is understood that the modules j ointly perform the , or is configured to j ointly perform, the method according to the first aspect of the proposed technology, wherein each module performs one of the steps speci fied in relation to the first aspect of the proposed technology .

According to a fourth aspect of the proposed technology, a computer program product is proposed for being used in a system for determining an indication of heart failure of a subj ect . The system comprises : (A) an accelerometer for being placed on the chest of a subj ect ; and (B ) a processor operatively connected to the accelerometer . The computer program product comprising program code instructions configured to , when executed by the processor of the system, cause the processor to perform the method according to the first aspect of the proposed technology .

According to a fi fth aspect of the proposed technology, a non-transient memory is proposed on which a computer program product according to the fourth aspect of the proposed technology is stored .

The first autoencoder is trained on healthy subj ects . This means that it is ef ficient in reconstructing first signal intervals from healthy subj ects and that it is less ef ficient in reconstructing the often more complex first signal intervals of subj ects with heart failure . The first correlation is then lower for subj ects with heart failure . This di f ference in correlation is utili zed when determining the indication of heart failure .

It is understood that the accelerometer is configured for measuring accelerations and vibrations of the chest wall of the subj ect that are caused by myocardial movement . That the first signal interval corresponds to a first subinterval of a heart cycle means that it covers, or corresponds to, a first portion of a single heart cycle.

In the method of the first aspect, the accelerometer may be placed on the chest of a subject and attached to the skin of the subject by an adhesive for measuring the accelerations and vibrations. The systems of the second, third and fourth aspects may further comprise an adhesive patch configured for supporting the accelerometer, or the housing described below, and for being attached to the skin of the subject. By attaching the accelerometer, or housing, to the skin, the quality of the recorded signals is improved.

The accelerometer may be placed on the front of the chest of the subject. The accelerometer being placed on the chest of a subject means that it is placed on the outside and not on the inside of the body. This has the advantage of a simple application that does not require trained staff. It also has the advantage that no invasive procedure is required and that it can be used in non-sterile environments.

The accelerometer may comprise a piezoelectric element. The signal may represent a voltage generated by the piezoelectric element. Thus, the signal strength or amplitude of the source signal may represent a voltage value.

The different aspects described above may be modified as described below.

The first signal interval, or first subinterval, may cover the systole, or a portion of the systole, of the heart cycle. Alternatively, it may cover the diastole, or a portion of the diastole, of the heart cycle.

The first autoencoder may compress the first signal interval to a number of nodes, or variables, wherein the number of nodes is less than 15, less than 12, or less than 10, or in the range 5 to 15, 6 to 12, or 7 to 9. The first autoencoder may be a single layer autoencoder. Preferably, the first autoencoder is an Undercomplete Autoencoder, meaning that the number of layers of the first autoencoder is smaller than the number of samples in the signal interval. Alternatively, the first autoencoder is a Sparse Autoencoder, Convolutional Autoencoder, Variational Autoencoder, Contractive Autoencoder, or Deep Autoencoder.

The first correlation may be a correlation measure, or error measure, between the first signal interval and the reconstructed first signal interval, for example based on a Pearson correlation coefficient, a Mean-Square Error (MSE) , or a Root-Mean-Square Error (RMSE) . Worded differently, determining the first correlation may comprise: determining a correlation measure, or error measure, between the first signal interval and the reconstructed first signal interval. For example, the correlation measure may be a Pearson correlation coefficient, a spearman correlation coefficient, a MSE, or a RMSE between the first signal interval and the reconstructed first signal interval.

The indication of heart failure may be a measure, such as a measure that indicates the likelihood for the subject having heart failure. The indication of heart failure may be a probability score, or risk score, for heart failure, for example based on a logistic regression model and the first correlation. Worded differently, determining the indication of heart failure may comprise: determining a probability score, or risk score, for heart failure, for example based on a logistic regression model and the first correlation. Determining a probability score, or the probability score, may further be based on demographic data, such as gender and age.

It is specified above that the first signal may correspond to a first subinterval of a heart cycle. The method may further comprise: obtaining a second signal interval from the source signal recorded with an accelerometer placed on the chest of a subject, wherein the second signal interval corresponds, or correlates in time, to a second subinterval of a heart cycle; inputting the second signal interval into a second autoencoder, wherein the second autoencoder is trained on the corresponding second signal intervals obtained from healthy subjects and outputs a reconstructed second signal interval; and determining a second correlation, or second error, between the second signal interval and the reconstructed second signal interval. In the alternative wording of the first aspect of the proposed technology, the method may further be based on a second signal interval from the source signal , wherein the second signal interval corresponds , or correlates in time , to a second subinterval of a heart cycle , wherein the method further comprises : input ting the second signal interval into a second autoencoder, wherein the second autoencoder is trained on the corresponding second signal intervals obtained from healthy subj ects and outputs a reconstructed second signal interval ; and determining a second correlation, or second error, between the second signal interval and the reconstructed second signal interval .

Determining the indication of heart failure may then be further based on the second correlation . Wording the determining of the second correlation and the indication di f ferently, the method may comprise : determining the indication of heart failure based on the correlation, or error, between the first signal interval and the reconstructed first signal interval and between the second signal interval and the reconstructed second signal interval . Alternatively, the indication based on the first signal interval may be a first indication and the indication based on the second signal interval may be second indication that is independent from the first indication .

The first and second subintervals of the heart cycle may overlap . The first signal interval , or first sub interval , may have a center within the systole of the heart cycle , or cover the systole , and the second signal interval , or second subinterval , may have a center within the diastole of the heart cycle , or cover the diastole . It has been found that this contributes to an improved indication of heart failure .

The second autoencoder may compress the first signal interval to a number of nodes , or variables , wherein the number of nodes is less than 15 , less than 12 , or less than 10 , or in the range 5 to 15 , 6 to 12 , or 7 to 9 . The second autoencoder may be a single layer autoencoder . The second autoencoder may be any of the types of autoencoders mentioned above in relation to the first autoencoder . For example , it may be an Undercomplete Autoencoder. The first autoencoder and the second autoencoder may be of the same type.

The second correlation may be a correlation measure, or error measure, between the second signal interval and the reconstructed second signal interval, for example based on a Pearson correlation coefficient, MSE, or RMSE. Worded differently, determining the second correlation may comprise: determining a correlation measure, or error measure, between the second signal interval and the reconstructed second signal interval. For example, the correlation measure may be a Pearson correlation coefficient, spearman correlation coefficient, a MSE, or a RMSE between the second signal interval and the reconstructed second signal interval.

The indication of heart failure may be a probability score, or risk score, for heart failure, for example based on a logistic regression model, the first correlation, and the second correlation. Worded differently, determining the indication of heart failure may comprise: determining a probability score, or risk score, for heart failure based on a logistic regression model, the first correlation, and the second correlation.

It is understood that the source signal may extend, or be recorded over, a period covering a plurality of cardiac cycles. It is further understood that that the first signal interval may be determined from, or within, a mean segment based on, or from, a plurality of signal segments of the source signal, wherein each signal element covers a single cardiac cycle.

Obtaining the first signal interval may comprise: recording a source signal with an accelerometer placed on the chest of a subject, wherein the source signal is recorded over a period covering a plurality of cardiac cycles of the subject; dividing the source signal into a plurality of signal segments, wherein each signal segment covers a single cardiac cycle; aligning the plurality of signal segments; determining a mean segment based on, or from, the plurality of signal segments, and determining the first signal interval in the mean segment.

In the alternative wording of the first aspect of the proposed technology, the method may further comprise: downl oading the source signal from a digital storage , such as a computer server system or cloud storage . The method may further comprise : di viding the source signal into a plurality of signal segments , wherein each signal segment covers a single cardiac cycle ; aligning the plurality of signal segments ; determining a mean segment based on, or from, the plurality of signal segments , and determining the first signal interval in the mean segment . It is understood that the digital storage may be at a remote location relative to the accelerometer that recorded the source signal . The source signal may have been obtained for a di f ferent purpose than determining a risk for heart failure . For example , it may have been recorded for investigations relating to seismocardiography ( SCG) or ballistocardiography (BCG) . This means that the proposed technology can be used in large statistical studies on stored data .

Dividing the source signal into a plurality of signal segments may comprise : identifying a plurality of heart sounds in the source signal . For example , the heart sounds may be the S I or the S2 heart sounds . Each heart sound relates to a single cardiac cycle . The recorded source signal is divided into the plurality of segments based on the identi fied plurality of heart sounds . This may involve extracting an audio signal from the source signal and determining the heart sounds in the audible signal . For example , the audio signal may be extracted by filtering the source signal with a high-pass filter having a lower cut-of f frequency in the range 40- 60 Hz , or approximately equal to 50 Hz .

Alternatively, the method may further comprise : recording an audio signal with a microphone placed on the chest of the subj ect simultaneously to recording the source signal with the accelerometer, and dividing the source signal into a plurality of signal segments may comprise : identi fying a plurality of heart sounds in the audio signal , wherein each heart sound relates to a single cardiac cycle , and dividing the source signal into the plurality of segments based on the identi fied plurality of heart sounds . The first signal interval may cover the Aortic valve Opening (AO) of the heart cycle. Additionally, or alternatively, it may cover the Aortic valve Closure (AC) of the heart cycle.

Determining the first signal interval in the mean segment may comprise: identifying a first fiducial point in the mean segment; positioning the first signal interval relative to the first fiducial point. For example, the first fiducial point may be the Gs point in the systole, which for example is defined in Sorensen, K., Schmidt, S.E., Jensen, A.S. et al. Definition of Fiducial Points in the Normal Seismocardiogram . Sci Rep 8, 15455 (2018) (https://doi.org/10.1038/s41598-018- 33675-6) . The first fiducial point may be located at the Aortic valve Opening (AO) of the heart cycle. The first signal interval may have a fixed length. For example, it may have a length of 750 ms. The first signal interval may extend from - 250 to +500 ms relative to the Gs point.

Similar to the first signal interval, the second signal interval may be determined from, or within, the mean segment. Obtaining the second signal interval may comprise: determining the second signal interval in the mean segment. In the alternative wording of the first aspect of the proposed technology with the abovementioned downloading of the source signal, the method may comprise: determining the second signal interval in the mean segment. The second signal interval may cover the Aortic valve Closure (AC) of the heart cycle. This way, the second signal interval includes information regarding the effeminacy of diastolic relaxation, which is considered an imported parameter.

Determining the second signal interval in the mean segment may comprise: identifying a second fiducial point in the mean segment; and positioning the second signal interval relative to the second fiducial point. For example, the second fiducial point may be the Dd point in the diastole, which for example is defined in Sorensen, K., Schmidt, S.E., Jensen, A.S. et al. Definition of Fiducial Points in the Normal Seismocardiogram . Sci Rep 8, 15455 (2018) (https://doi.org/10.1038/s41598-018- 33675-6) . The second fiducial point may be located at the Aortic valve Closure (AC) of the heart cycle. The first signal interval may have a fixed length. For example, it may have a length of 750 ms. The first signal interval may extend from - 300 to +500 ms relative to the Ds point.

The method may further comprise: outputting, or displaying, the indication of heart failure. For example, the indication of heart failure may be displayed as a number, such as in the interval 0 to 100. The system may comprise a display for displaying the indication of heart failure.

The method may further comprise: filtering the source signal, the plurality of segments, or the mean segment with a high-pass filter having a cutoff frequency below 1. Worded differently, the source signal may be an SCG signal. The source signal may encompass sub-audible and audible frequency components .

The method may further comprise: discarding noisy signal segments. This may be done as specified in WO2017/216374 Al.

The system of the above aspects may comprise: (C) a nontransient memory storing program code instructions that, when executed by the processor, configures the processor to perform the method.

The system may comprise a smartphone. The processor and/or the non-transient memory may be integral parts of the smartphone. Further, the accelerometer may be an integral part of the smartphone. The system may also comprise a casing or holder for supporting the smartphone, and the casing or holder may comprise an adhesive patch configured for attaching the casing or holder to the skin of the subject. Alternatively to the accelerometer being an integral part of the smartphone, the accelerometer may form part of an auxiliary unit configured to communicate with the smartphone by wire or wirelessly, such as a band that can be strapped around the chest of subject.

The processor may be further configured to: operate the accelerometer for recording a source signal, for example with the accelerometer placed on the chest of a subject, wherein the signal is recorded over a period of time covering a plurality of cardiac cycles of the subject. The processor may be further configured to: divide the recorded signal into a plurality of signal segments.

The system of the above aspects may comprise: (D) a microphone configured to be placed on the chest of the subject for measuring sounds generated by the beating heart, wherein the processor is further operatively connected to the microphone and configured to: operate the accelerometer to record a source signal with the accelerometer, e.g. placed on the chest of a subject; operate the microphone to record an audio signal with the microphone placed on the chest of a subject simultaneously to the source signal being recorded with the accelerometer; identify a plurality of heart sounds in the audio signal, wherein each heart sound relates to a single cardiac cycle; and divide the recorded source signal into the plurality of segments based on the identified plurality of heart sounds to obtain the plurality of signal segments. Alternatively, the processor may be further configured to: operate the accelerometer to record a source signal, e.g. with the accelerometer placed on the chest of a subject; filtering the source signal to obtain an audio signal; identify a plurality of heart sounds in the audio signal, wherein each heart sound relates to a single cardiac cycle; and divide the recorded signal into the plurality of signal segments based on the identified plurality of heart sounds. The filtering may comprise a high-pass filter having lower cut-off frequency in the range 40-60 Hz, or approximately equal to 50 Hz.

Here, the plurality of heart sounds may be the first heart sound (SI) . Alternatively, the plurality of heart sounds may be the second heart sound (S2) .

The system may comprise a housing or cover that supports and encloses or covers the processor. The housing or cover may further enclose or cover at least a portion of, or the whole of, the accelerometer and/or enclose or cover at least a portion of, or the whole of, the microphone.

Obtaining the first signal interval, and optionally the second signal interval, may further comprise: storing the plurality of signal segments, and/or the mean in the non- transient memory or in an auxiliary non-transient memory . The auxiliary non-transient memory may form part of computer server system, which may be at a remote location .

Determining the indication of heart failure may comprise : storing the indication of heart failure , or storing the first indication and the second indication of heart failure , in the non-transient memory or in an auxiliary non-transient memory . The auxiliary non-transient memory may form part of computer server system, which may be at a remote location .

Outputting, or displaying, the indication of heart failure may further comprise : obtaining a previously determined indication of heart failure for the subj ect and the indication of heart failure output information may further be based on the previously obtained indication of heart failure . The previously obtained measure may be stored in the non- transient memory or in the auxiliary non-transient memory . Outputting, or displaying, the indication may further comprise : output ting, or displaying, the previously determined indication of heart failure .

The previously determined indication of heart failure may have been determined in the same manner as the indication of heart failure . The previously determined indication of heart failure may have been determined at an earlier point in time , such as more than five days or ten days prior to determining the measure or the mean segment .

Further advantages with and features of the di f ferent aspects will be apparent from the following description of the drawing .

Brief description of the drawings

A more complete understanding of the abovementioned and other features and advantages of the present invention will be apparent from the following detailed description of the drawings , wherein :

Fig . l is a schematic illustration of an embodiment of a system for determining an indication of heart failure ,

Fig . 2 is a flow chart illustrating the steps of a method performed by the system described in relation to Fig . l , Fig . 3 shows a portion of an audio signal ,

Fig . 4 shows a mean segment with first and second signal intervals indicated by dashed lines ,

Fig . 5 is a schematic illustration of an autoencoder,

Fig . 6 is a schematic illustration of an alternative embodiment of a system for determining an indication of heart failure ,

Figs . 7a and 7b are schematic illustrations of alternative embodiment of a system for determining an indication of heart failure ,

Figs . 8a and 8b are graphs showing the first signal interval ( solid) and the reconstructed first signal interval ( dashed) for a subj ect with no heart failure (No HF) and with heart failure (HFrEF) , respectively, and

Fig . 9 shows box plots of the indications of heart failure (HF-scores ) for di f ferent sets of subj ects in the evaluation of the proposed technology .

Detailed description of drawings

Fig . l schematically illustrates an embodiment of a system 12 for determining an indication of heart failure of a subj ect 18 . The system 12 has an accelerometer 14 in the form of a piezoelectric element that can be placed on the chest of a subj ect 18 and for measuring vibrations of the chest wall caused by movements of the heart . A processor 20 is connected with the accelerometer 14 . The processor 20 has a transient memory 22 which can store a signal received from the accelerometer 14 , and by which it can execute program code instructions . The system 12 comprises a support 26 that supports the accelerometer 14 and a housing 28 that accommodates the processor 20 . The system 12 also has a nontransient memory 24 storing program code instructions for the processor 20 . For example , the system 12 as a whole can be an integral part of a smartphone , or all parts except the accelerometer 20 and the support 26 can form part of a smartphone . In one embodiment , the accelerometer is an integrated accelerometer of a smartphone . In one embodiment of the system 12 , it additionally has a display 25 that can display output information from the processor 20 , such as a number .

The program code instructions in the non-transient memory 24 cause the processor 20 to perform the method that is schematically illustrated in Fig . 2 . A source signal is recorded 110 with an accelerometer placed on the chest of a subj ect 18 . The source signal is recorded over a period covering a plurality of cardiac cycles of the subj ect 18 .

In an alternative embodiment (not shown) , instead of the source signal being recorded, the source signal is downloaded from a computer server system (not shown) that is a general storage for source signals obtained by accelerometer placed on the chest of subj ect , for example for SCG and BCG purposes . This means that the source signal is not speci fically intended for studying heart failure .

The source signal is filtered 120 with a high-pass filter having a cutof f frequency below 1 . The source signal is then divided 130 into a plurality of signal segments by extracting 152 an audio signal from the source signal using a high-pass filter having a lower cut-of f frequency at 50 Hz . A portion of an audio signal 32 is shown in Fig . 3 . The abscissa represents the signal strength X (no unit ) and the ordinate the time in milliseconds (ms ) . The S I heart sounds are identi fied 134 in the audio signal , which are used to divide 130 the source signal into a plurality of signal segments such that each signal segment covers a single cardiac cycle . Noisy signal segments are discarded as speci fied in WO2017 /216374 Al .

A mean segment 34 is then determined 150 based on plurality of signal segments by aligning 152 the plurality of signal segments using the S I heart sounds associated with the signal segments . An example of a mean segment 34 is shown in Fig . 4 . The abscissa represents an acceleration in g (ms ^-2 ) and the ordinate the time in milliseconds (ms ) . Here , g is proportional to the voltage from the accelerometer 14 .

A first signal interval 36 is obtained 100a by identi fying 172 the Gs point in the systole as a first fiducial point 38 in the mean segment 34 . The first fiducial point 38 is located in the systole . The first signal interval 36 extends from -250 to +500 ms relative to the first fiducial point 38 . This means that the first signal interval 36 essentially covers the diastole of the mean segment 34 and corresponds to a first subinterval of the heart cycle . Similarly, a second signal interval 40 is obtained 100a by identi fying 182 the Dd point in the diastole as a second fiducial point 42 . The second signal interval 40 extends from -300 to +500 ms relative to the first fiducial point 38 . Only the start of the second signal interval 40 is indicated in Fig . 4 . This means that a second signal interval 40 essentially covers the systole of the mean segment 34 .

An autoencoder 46 and 48 is schematically illustrated in Fig . 5 . The autoencoder 46 and 48 has an encoder 50 that maps the input into the code , and a decoder 54 that maps the code to a reconstruction of the input . The input is a signal interval 36 and 40 having the sample length i . The encoder 50 maps the input down to j nodes . In the current embodiment , the number of nodes is eight and the sample length i is 401 . The decoder 54 then maps the nodes to an output being a reconstructed signal intervals 52 and 56 having the same sample length i as the input . The weight matrices W and W ' and the bias vectors b and b ' have been determined by training the autoencoder 46 and 48 on healthy subj ects .

The first signal interval 36 is inputted 200a into a first autoencoder 46 that has been trained on the corresponding first signal intervals from healthy subj ects . The first autoencoder 46 outputs a reconstructed first signal interval 52 . Similarly, the second signal interval 40 is inputted 200b into a second autoencoder 48 . The second autoencoder 48 has been trained on the corresponding second signal intervals from healthy subj ects . The second autoencoder 48 outputs a reconstructed second signal interval 56 .

A first correlation between the first signal interval 36 and the reconstructed first signal interval 52 is determined 300a by calculating a first Pearson correlation coef ficient for the two signals . Similarly, a second correlation between the second signal interval 40 and the reconstructed second signal interval 56 is determined 300b by calculating a second Pearson correlation coef ficient for the two signals . In alternative embodiments , the MSE or RMSE between the signal intervals and the reconstructed signal intervals are calculated .

The indication of heart failure is determined 400 as a probability score for heart failure by a logistic regression model for the first and second Pearson correlation coef ficients . Thus , the indication is based on the first correlation and the second correlation . The indication of heart failure is then displayed 500 on the display 25 .

In an alternative embodiment of a system 12 , no second signal interval is obtained and inputted in a second autoencoder . No second correlation is determined, and the indication of heart failure is based only on the first correlation .

Fig . 6 schematically illustrates an alternative embodiment of a system for determining an indication of heart failure of a subj ect . The system 12 is similar to the system described in relation to Fig . l and features with the same or related functions have the same number indices . Additionally, the system 12 has a microphone 30 in the form of a transducer that can convert sound into an electrical signal . The microphone 30 is supported by the support 26 .

The program code instructions in the non-transient memory 24 correspond to those described in relation to Fig . 2 but di f fers in the dividing 130 of the source signal into the plurality of signal segments .

With the accelerometer 14 and the microphone 30 placed on the chest of the subj ect , the program code instructions cause the processor 20 to operate the microphone 30 to record an audio signal with the microphone 30 simultaneously to the source signal being recorded with the accelerometer 14 . S I heart sounds are identi fied in the audio signal . The source signal is divided into the plurality of signal segments based on the time correlation between the source signal , the audible signal , and the identi fied S I heart sounds . The subsequent alignment of the plurality of signal segments is then based on the S I heart sound in each signal segment . Fig.7a illustrates an alternative embodiment of the system 12 described in relation to Fig.l, with the only difference that the support 26 forms part of the housing 28 such that the housing 28 covers at least a portion of the accelerometer 14. In this embodiment, the housing 28 is placed on the chest of a subject, which means that the accelerometer 14 is also placed on the chest of a subject. Similarly, Fig.7b illustrates an alternative embodiment of the system 12 described in relation to Fig. 6, with the only difference that the support 26 forms part of the housing 28 such that the housing 28 covers at least a portion of the accelerometer 14 and the microphone 30. In this embodiment, the housing 28 is placed on the chest of a subject, which means that the accelerometer 14 and the microphone 30 are simultaneously placed on the chest of the sub j ect .

Proof of concept

A system was used based on the system described in relation to Figs.l and 2. The method was developed to estimate the risk of having Heart Failure (HF) with low Ejection Fraction (HFrEF) . The corresponding source signal is hereafter called an SCG signal.

SCG signals from 200 subjects were used for development and validation. The dataset was divided into a Training set and Test set with 100 subjects in each set.

Table 1 shows baseline demographics and known HF diagnosis of the Training set and the Test set .

The autoencoders were trained only on the 75 patients of the Training set with no heart failure diagnosis (No HF) . Since the autoencoders were not trained to detect HFrEF, the autoencoders can be considered as unsupervised machine learning models .

The first and second signal intervals of the 200 subj ects were inputted in the trained first and second autoencoders , respectively, outputting first and second reconstructed signal intervals . An example of the first signal interval 34 solid and the reconstructed first signal interval 44 for a subj ect with No HF is shown in Figure Fig . 8a . The corresponding signals for a subj ect with known HFrEF are shown in Fig . 8b . The Gs points 58 are indicated in Figs . 8a and 8b . It can be seen in Figs . 8a and b that the signal intervals and the reconstructed signal intervals di f fer more for the HFrEF subj ect than for the No HF subj ect . The first and second correlations were determined as described above in relation to Fig . 2 . Hereafter, the first correlation is denoted rSys and the second correlation is denoted rDia . It was found that the correlations rSys and rDia were higher for the No HF subj ects than for the HFrEF subj ects .

A logistic regression model was built for providing a risk prediction score based on the rSys and rDia . The risk prediction score is hereafter called HF-score and ranges from 0 to 100 . The HF-score for the Training set , the Test set , and the combined sets (All ) are shown in the box plots of Fig . 9 . A 5% risk threshold was defined as high risk for HFrEF, which is indicated by a hori zontal dashed line .

Table 3 showing the performance of the first autoencoder (rSys) and the second autoencoder (rDia) measured as the Area Under the receiver characteristic operator Curve (AUG) .

Table 4 showing the classification performance of the HF- score . It was concluded that the AUG values obtain with both the autoencoders ( rSys and rDia ) and the classi fication performance of the HF-score confirm the strong HFrEF risk assent potential of the proposed technology .