Multiparameter estimation of cardiorespiratory fitness from oscillatory electrocardiograms.

Abstract

A proposed method relates to quantifying cardiorespiratory endurance. The method includes acquiring a vibratory electrocardiogram (SCG) recorded by an accelerometer configured to measure accelerations and vibrations of a person's chest wall caused by movement of the heart muscle. A characteristic of a first signal feature (AC) in the vibratory electrocardiogram (SCG) is determined, the first signal feature (AC) corresponding to aortic valve closure (AC) of a heartbeat. A measurement indicative of cardiorespiratory endurance (VO2max) is then determined based on the characteristic of the first signal feature (AC).

Description

[Technical field]

[0001] The proposed approach relates generally to fitness applications and in particular to a method and system for determining indicators of cardiorespiratory fitness. [Background technology]

[0002] Cardiorespiratory endurance refers to the ability of the circulatory and respiratory systems to deliver oxygen to muscles. The term is generally used specifically to deliver oxygen to skeletal muscles during sustained physical activity and therefore may be considered a subset of cardiovascular fitness.

[0003] Cardiorespiratory endurance is affected by physiological parameters including heart rate, stroke volume, cardiac output, and maximum oxygen consumption. Regular exercise enlarges the myocardium, allowing more blood to be pumped with each heartbeat, and increases the number of small arteries in skeletal muscles after training, making the system more efficient.

[0004] A common measure of cardiorespiratory endurance is VO2max, which corresponds to the maximum rate of oxygen consumption measured during exercise at increasing loads. In some cases, this measure is normalized by body weight.

[0005] There is both clinical and consumer demand for a low-cost and portable method capable of indicating an index of cardiorespiratory fitness, or VO2max.

[0006] Vibratory electrocardiogram is the analysis of a seismocardiogram (SCG), which shows subaudible, low-frequency vibrations at the chest wall caused by the beating heart. More generally, vibratory electrocardiograms relate to the non-invasive measurement of accelerations at the chest wall, usually caused by myocardial motion. Heart sounds are an audible component of the chest wall, usually above 40-60 Hz, while SCG vibrations are usually below 25 Hz.

[0007] The vibratory electrocardiogram (SCG) is usually measured using an accelerometer. However, when an accelerometer is used, both the low frequency vibratory electrocardiogram component and the audible component are sampled simultaneously. And the signal from the accelerometer is usually filtered so that it does not contain any audible components.

[0008] SCG reveals different cardiovascular functions by which cardiorespiratory endurance can be determined. For example, the oscillatory electrocardiogram is usually suitable for estimating the time intervals between features in the cardiac cycle.

[0009] The accelerometer signal is characterized by high-load, low-frequency vibrations caused by the beating heart. If the accelerometer signal is low-pass filtered, for example with a high cutoff frequency of 40 Hz, the effects of heart sounds are removed. In the filtered signal or SCG signal, the main features of the cardiac cycle are Mitral valve Closure (MC), Isovolumic Movement (IM), Aortic valve Opening (AO), Isovolumic Contraction (IC), Rapid Ejection (RE), Aortic valve Closure (AC), Mitral valve Opening (MO), and Rapid Filling (RF). Summary of the Invention [Problem to be solved by the invention]

[0010] It is an object of the present invention to meet the above-mentioned need for a technique that can provide an index of cardiopulmonary fitness, and in particular for a technique that has this capability, which is inexpensive and portable. [Means for solving the problem]

[0011] According to a first aspect of the proposed approach, the aforementioned object is achieved by a method comprising the step of obtaining a vibration electrocardiogram (SCG) recorded by an accelerometer (14) configured to measure accelerations and vibrations of the person's chest wall caused by movements of the heart muscle. The method comprises the step of determining a characteristic of a first signal feature (AC) in the vibration electrocardiogram (SCG) or determining a characteristic of a first signal feature (AC) from the vibration electrocardiogram (SCG), the first signal feature (AC) corresponding to or associated with aortic valve closure (AC) of the heartbeat. The method may be for quantifying cardiopulmonary endurance or determining an indicator of said cardiopulmonary endurance. The method may further comprise the step of determining a measurement indicative of cardiopulmonary endurance (VO2max) or determining an indicator of said cardiopulmonary endurance (VO2max) based on the characteristic of the first signal feature.

[0012] Here, the step of acquiring a vibration electrocardiogram does not specify the source from which the vibration electrocardiogram is acquired or the manner in which the vibration electrocardiogram is acquired. It is understood to include, for example, downloading from a data storage or directly from an accelerometer, and utilizing an accelerometer. Instead of a specific step of acquiring a vibration electrocardiogram, a first aspect of the proposed approach may relate to a method of quantifying cardiopulmonary endurance or determining an index of said cardiopulmonary endurance from a vibration electrocardiogram (SCG) recorded by an accelerometer configured to measure acceleration and vibration of a person's chest wall caused by the movement of the heart muscle.

[0013] The proposed approach is centered on the realization that a signal feature in an oscillatory electrocardiogram (SCG) corresponding to an aortic valve closure (AC) can be used to determine cardiopulmonary endurance. The oscillatory electrocardiogram may include a portion of a heartbeat, such as a multiple heartbeat, a single heartbeat, or a diastolic segment of a heartbeat. It is understood that the first signal feature may correspond to the aortic valve closure (AC) of a single heartbeat or an average of multiple heartbeats. Here, a heartbeat is understood to encompass a complete cardiac cycle. It is further understood that the signal feature may be an ensemble of combined features, for example, a single peak connected to surrounding local minima.

[0014] Here, and throughout the specification, it is understood that determining or quantifying a measurement indicative of cardiopulmonary endurance does not clearly and unambiguously indicate abnormal cardiovascular or cardiopulmonary function, condition, or structure, cardiovascular or cardiopulmonary disorder or disease. However, determining a measurement indicative of cardiopulmonary endurance is understood to include determining an index of aerobic capacity, such as maximum oxygen consumption or intake (VO2max). Of course, this measurement may be indirectly affected by some abnormal function of the heart, such as a disorder or disease. However, a measurement indicative of cardiopulmonary endurance (VO2max) does not indicate a specific abnormal function and, as such, does not constitute a diagnostic measurement.

[0015] The accelerometer may include a piezoelectric element. The signal may represent a voltage generated by the piezoelectric element. Thus, the signal strength or amplitude of the temporal feature may represent the voltage value of the temporal feature. It is understood that the output from the accelerometer does not include any audible components. For example, the recorded signal may be filtered with a low pass filter having an upper cutoff frequency that is less than 100 Hz, 60 Hz, 40 Hz, 20 Hz, 10 Hz, or 5 Hz.

[0016] In a second aspect of the proposed technique, a system is provided comprising an accelerometer configured to be placed on a person's chest to measure accelerations and vibrations of the person's chest wall caused by myocardial movement. The system further comprises a processor operatively connected to the accelerometer, the processor configured to perform any of the steps of the method according to the first aspect of the proposed technique. The system may be for quantifying cardiopulmonary endurance or determining an index of said cardiopulmonary endurance.

[0017] In a third aspect of the proposed approach, a system is provided comprising an accelerometer configured to be placed on a person's chest to measure accelerations and vibrations of the person's chest wall caused by myocardial movements and to obtain a vibratory electrocardiogram (SCG). The system further comprises a first determination module for determining a characteristic of a first signal feature in or from the vibratory electrocardiogram (SCG), the first signal feature corresponding to or associated with aortic valve closure (AC) of the heartbeat. The system may be for quantifying cardiopulmonary endurance or determining an index of said cardiopulmonary endurance. The system may further comprise a second determination module for determining a measurement indicative of cardiopulmonary endurance (VO2max) or determining an index of said cardiopulmonary endurance (VO2max) based on the characteristic of the first signal feature.

[0018] In a fourth aspect of the proposed technique, a computer program product is provided for use in a system, which may be for quantifying cardiorespiratory endurance or determining an index of said cardiorespiratory endurance. The system comprises an accelerometer configured to be placed on a person's chest to measure accelerations and vibrations of the person's chest wall caused by myocardial movement, and a processor operatively connected to the accelerometer. The computer program product comprises program code instructions configured to perform any of the steps of the method according to the first aspect of the proposed technique when executed by the processor of the system.

[0019] In a fifth aspect, there is provided a non-transitory memory having stored thereon a computer program product according to the proposed fourth aspect.

[0020] Optional further details of the proposed approach are described below.

[0021] The step of determining the measurement indicative of cardiopulmonary endurance (VO2max) may further be based on a first trained machine learning model. The step of determining the measurement indicative of cardiopulmonary endurance (VO2max) may include providing or loading the first machine learning model. The first machine learning model may be trained on characteristics of the signal feature that are operationally similar to the characteristics of the first signal feature (AC). The step may further include applying or inputting the characteristics of the first signal feature (AC) into the first machine learning model to determine the measurement indicative of cardiopulmonary endurance (VO2max). By the term "operationally similar" it is understood that the characteristics of the signal feature used in the training are similarly defined or generated or have the same general characteristics as the characteristics of the first signal feature (AC).

[0022] More generally, determining the measurement indicative of cardiopulmonary fitness (VO2max) may include providing a first machine learning model trained to determine a measurement indicative of cardiopulmonary fitness (VO2max) based on characteristics of a first signal feature (AC) and applying or inputting the characteristics of the first signal feature (AC) to the first machine learning model, or may consist of providing a first machine learning model trained to determine a measurement indicative of cardiopulmonary fitness (VO2max) based on characteristics of the first signal feature (AC) and applying or inputting the characteristics of the first signal feature (AC) to the first machine learning model. More generally, determining the measurement indicative of pulmonary fitness (VO2max) may include determining the measurement indicative of cardiopulmonary fitness (VO2max) by applying the characteristics of the first signal feature (AC) to the trained first machine learning model.

[0023] Determining the characteristic of the first signal feature may include determining a plurality of segments of a SCG, determining one or more segments of the plurality of segments as noisy, and discarding the noisy segments. The non-discarded segments include the first signal feature, and the first signal feature is determined in the non-discarded segments. The segments may include diastolic and / or systolic segments. A diastolic segment is understood to be a segment of the SCG that corresponds to the diastolic portion of the cardiac cycle. Similarly, a systolic segment is understood to be a segment of the SCG that corresponds to the systolic portion of the cardiac cycle.

[0024] The step of determining the characteristic of the first signal feature may include identifying one or more (first) fiducial or reference points of the first signal feature. The measurement indicative of cardiopulmonary endurance may further be determined based on the one or more (first) fiducial points. The fiducial points may include a maximum (ACmax) of the first signal feature and / or a first minimum (ACmin) immediately preceding the maximum (ACmax). It is understood that the first minimum and the second minimum may correspond to a specific state in the cardiac cycle. For example, the second minimum may correspond to mitral valve opening (MO).

[0025] Several characteristics, or sub-characteristics, of the first signal feature are described below, which are used in determining a measurement indicative of cardiopulmonary endurance. Each of the characteristics has been found to contribute to the quantification, it being understood that the characteristics can be used individually or in combination with each other. Preferably, all of the sub-characteristics described below are used in the same manner.

[0026] For example, determining the characteristic of the first signal feature may further include determining an amplitude difference (ACPeakToPeak) between a maximum value (ACmax) and a first minimum value (ACmin) immediately preceding the maximum value (ACmax) of the first signal feature, and determining a measure indicative of cardiopulmonary endurance (VO2max) may further be based on the amplitude difference (ACPeakToPeak), or the amplitude difference (ACPeakToPeak) may constitute or form part of the characteristic of the first signal feature (AC).

[0027] Alternatively or additionally, determining the characteristic of the first signal feature may further comprise determining a first time interval (ACTimePeakToPeak) between a maximum value (ACmax) of the first signal feature and a first minimum value (ACmin) immediately preceding the maximum value (ACmax) of the first signal feature. And determining the measure indicative of cardiopulmonary endurance (VO2max) may be further based on the first time interval (ACTimePeakToPeak), or the first time interval (ACTimePeakToPeak) may constitute or form part of the characteristic of the first signal feature (AC).

[0028] Alternatively or additionally, the step of determining a characteristic of the first signal feature may further include a step of determining a morphology measure (ACMorphology) of the first signal feature. And the step of determining a measure indicative of cardiopulmonary endurance (VO2max) may further be based on the morphology measure (ACMorphology), or the morphology measure (ACMorphology) may constitute or form part of the characteristic of the first signal feature (AC). The morphology measure (ACMorphology) is understood to be determined in the time domain of the oscillatory electrocardiogram (SCG). The morphology measure is understood to represent or indicate the shape, contour, and / or outline of the first signal feature.

[0029] Determining the morphometry may include determining a first window of a vibratory electrocardiogram (SCG), where the first window includes the first signal feature, and determining the morphometry may be based on a time domain of the first window.

[0030] It is understood that the first window may be determined from the average diastolic segment, or alternatively, the first window may be determined for each diastolic segment of the oscillatory electrocardiogram (SCG), and the average first window may be determined prior to determining the ACMorphology.

[0031] In a preferred embodiment, individual morphometry values ​​are determined for each first window or for each diastolic segment, and a morphometry value (ACMorphology) is determined as the average of the individual morphometry values.

[0032] The width of the first window may be predefined. Based on the time domain of the first window is understood to include based on the morphology (ACMorphology) of the signal value or amplitude over time in the first window. The first window may have a width in the range of 200ms to 500ms, 250ms to 450ms, or 300ms to 400ms. This means that the method can be applied at heart rates up to about 100 beats per minute. Determining the first window may include identifying a portion of the first signal feature that forms a fiducial point, and positioning the first window relative to the fiducial point. The fiducial point may be a maximum (ACmax) of the first signal feature. It is understood that the maximum (ACmax) of the first signal feature may be a maximum (ACmax) of the diastolic segment that includes the first signal feature. The first window may start within 40ms-60ms, or 50ms before the reference point and end within 200ms-500ms, 300ms-400ms, or 350ms after the reference point.

[0033] The step of determining the morphometric value (ACMorphology) may include a step of supplying or loading a second machine learning model. The second machine learning model may be trained on a window that is operationally similar to the first window of the oscillatory electrocardiogram (SCG) (to determine the morphometric value). It may further include a step of applying or inputting the first window in the second machine learning model to determine the morphometric value (ACMorphology). The term "operationally similar" is understood to include that each of the windows for which the second model is trained is similarly defined or generated or includes similar features as the first window of the oscillatory electrocardiogram (SCG).

[0034] More generally, determining the morphometric value (ACMorphology) may include providing a second machine learning model trained to determine the morphometric value (ACMorphology) based on the first window, and applying the first window to the second machine learning model. More generally, determining the morphometric value (ACMorphology) may include applying the first window to the trained second machine learning model to determine the morphometric value (ACMorphology). Here, the first window is understood to be represented in the time domain.

[0035] The second machine learning model may include determining a first dimensionality measure representing the dimensionality reduction of the first window or its time domain, and the second machine learning model may be trained to determine a morphology measure (ACMorphology) based on the first dimensionality measure. The second machine learning model is then trained on an operationally similar dimensionality measure. The first dimensionality measure may be a principal component of a principal component analysis or a node obtained from an autoencoder configured to compress the first window.

[0036] The step of determining a characteristic of the first signal feature may further include determining a frequency measure (ACFrequency) of the first signal feature. The step of determining a measure indicative of cardiopulmonary endurance (VO2max) may further be based on the frequency measure (ACFrequency) or a morphology measure (ACMorphology) may constitute or form part of the characteristic of the first signal feature (AC). The frequency measure (ACFrequency) may be determined in the frequency domain of a vibration electrocardiogram (SCG).

[0037] Determining the frequency measure (ACFrequency) may include determining a second window of a vibratory electrocardiogram (SCG), where the second window includes the first signal feature, and determining the frequency measure (ACFrequency) may be based on a frequency domain of the second window.

[0038] Determining the frequency metric (ACFrequency) may further include determining a power spectral density or a spectral density of the second window, and determining the frequency metric (ACFrequency) may be based on the power spectral density.

[0039] It will be appreciated that, similar to the first window, the second window or power spectral density may be determined for the average diastolic segment. Alternatively, the second window or power spectral density may be determined for each diastolic segment or second window of the oscillatory electrocardiogram (SCG). In a preferred embodiment, an individual power spectral density is determined for each first window or for each diastolic segment, an individual frequency measurement is determined from each individual power spectral density, and a frequency measurement (ACFrequency) is determined as the average of the individual frequency measurements.

[0040] The second window may have any of the characteristics of the first window. The first window and the second window may be the same or have the same characteristics or properties. In the above, based on the frequency domain of the second window is understood to include that the frequency measurement value (ACFrequency) is based on the change in signal value or amplitude over frequency in the first window.

[0041] The step of determining the frequency measurement (ACFrequency) may include a step of providing or loading a third machine learning model. The third machine learning model may be trained on a frequency domain or power spectral density of a window that is operationally similar to the frequency domain or power spectral density of the second window of the oscillatory electrocardiogram (SCG) to determine the frequency measurement (ACFrequency). It may further include a step of applying or inputting the second window, or its frequency domain or power spectral density, to the second machine learning model to determine the frequency measurement (ACFrequency). By "operationally similar", it is meant that the frequency domain or power spectral density of each oscillatory electrocardiogram (SCG) window used in training is similarly defined or generated or has the same general characteristics as the frequency domain or power spectral density of the second window.

[0042] More generally, determining the frequency metric (ACFrequency) may include providing a third machine learning model trained to determine the frequency metric (ACFrequency) based on the second window, or its frequency domain or power spectral density, and applying the second window, or its frequency domain or power spectral density to the third machine learning model. More generally, determining the frequency metric (ACFrequency) may include applying the second window, or its frequency domain or power spectral density to the trained third machine learning model to determine the frequency metric (ACFrequency).

[0043] The third machine learning model may include determining a second dimension measure representing the dimensionality reduction of the second window, or its frequency domain or power spectral density, and the third machine learning model may be trained to determine a frequency measure (ACFrequency) based on the second dimensionality measure. The third machine learning model is then trained on the operationally similar dimensionality measure. The measured second dimension may be a principal component of a principal component analysis, or a node obtained from an autoencoder configured to compress the first window, or power spectral density.

[0044] The step of determining the measurement indicative of cardiorespiratory endurance (VO2max) may further be based on demographic data, such as gender, age, height, weight, and body mass index (BMI). The first machine learning model may be trained to determine the measurement indicative of cardiorespiratory endurance (VO2max) based on the demographic data, and the step of determining the measurement indicative of cardiorespiratory endurance (VO2max) may further include applying the demographic model to the first machine learning model. More generally, the step of determining the measurement indicative of cardiorespiratory endurance (VO2max) may include applying the demographic data to the trained first machine learning model.

[0045] The method according to the first aspect of the proposed technique may further comprise determining heart rate variability (HRV). Moreover, the step of determining the measure indicative of cardiopulmonary endurance (VO2max) may further be based on the heart rate variability (HRV). The heart rate variability (HRV) may be determined from a oscillating electrocardiogram (SCG) or from an electrocardiogram (ECG) obtained simultaneously with or in association with the oscillating electrocardiogram (SCG).

[0046] The step of determining the heart rate variability (HRV) may further include determining a number of time intervals between reference points of the same type in subsequent heart beats, and determining the heart rate variability (HRV) as or based on the standard deviation of the number of time intervals.

[0047] Determining the measurement indicative of cardiorespiratory endurance (VO2max) may include providing or loading a first machine learning model trained on a heart rate variability measurement that is operationally similar to heart rate variability (HRV). It may further include applying or inputting the heart rate variability (HRV) to the first machine learning model to determine the measurement indicative of cardiorespiratory endurance (VO2max). By "operationally similar" it is understood that the heart rate variability measurement used in the training is similarly defined or generated or has the same general characteristics as heart rate variability (HRV).

[0048] More generally, the first machine learning model may be further trained to determine a metric indicative of cardiorespiratory endurance (VO2max) based on heart rate variability (HRV), and determining the metric indicative of cardiorespiratory endurance (VO2max) may further include applying the heart rate variability (HRV) to the first machine learning model. More generally, determining the metric indicative of cardiorespiratory endurance (VO2max) may include applying the heart rate variability (HRV) to the trained first machine learning model.

[0049] The method according to the first aspect of the proposed technique may further comprise determining a characteristic of a second signal feature in the oscillatory electrocardiogram (SCG), where the second signal feature is different from the first signal feature. And, determining a measure indicative of cardiopulmonary endurance (VO2max) may further be based on the characteristic of the second signal feature. It is understood that the second signal feature may be from a single heart beat or may constitute an average of several heart beats, as described above for the first signal feature.

[0050] Determining the characteristic of the second signal feature comprises determining a plurality of systolic segments of a scintillator electrocardiogram (SCG) and discarding noisy systolic segments, whereby non-discarded diastolic segments may comprise the second signal feature, where a systolic segment is understood to be a segment of the scintillator electrocardiogram (SCG) that corresponds to a systolic portion of the cardiac cycle.

[0051] The second signal feature may correspond to mitral valve closure (MC) and / or aortic valve opening (AO) of the cardiac cycle. Determining the characteristic of the second signal feature may include identifying one or more (second) fiducial or reference points of the second signal feature. The measurement indicative of cardiopulmonary endurance may further be determined based on the one or more (second) fiducial points. The fiducial points may include a zero crossing of mitral valve closure (MC) and a first maximum (A0max) of aortic valve opening (AO) after mitral valve closure (MC).

[0052] And determining the characteristic of the second signal feature may include determining a second time interval (SysTimeMCToAO) between a zero crossing of mitral valve closure (MC) and a first maximum (AOmax) of aortic valve opening (AO) after mitral valve closure (MC), and determining a measure indicative of cardiopulmonary endurance (VO2max) may further be based on the second time interval (SysTimeMCToAO).

[0053] In the proposed method, the step of determining a measurement indicative of cardiorespiratory endurance (VO2max) may further be based on a first trained machine learning model. Preferably, the method is based on at least three machine learning models, namely a first model for determining a measurement indicative of cardiorespiratory endurance (VO2max) as such, a second model for determining a morphology measurement (ACMorphology), and a third model for determining a frequency measurement (ACFrequency). This means that the first model may include the results of the second and third models. For example, the machine learning model may be based on linear regression, neural network regression, convolutional neural network regression, or support vector machine regression.

[0054] A more complete understanding of the above-discussed, as well as other features and advantages of the present invention will become apparent from the following detailed description of the drawings. [Brief description of the drawings]

[0055] [Figure 1] 1 is a schematic diagram of an embodiment of a system for determining an index of cardiopulmonary endurance. [Diagram 2] 2 is a flow chart showing the basic steps of a method used in the system described with respect to FIG. 1; [Figure 3a] 13 is a flow chart illustrating another embodiment for determining morphometric measurements. [Figure 3b] 13 is a flow chart illustrating another embodiment for determining morphometric measurements. [Figure 4a] 11 is a flow chart illustrating another embodiment for determining a frequency measurement. [Figure 4b] 11 is a flow chart illustrating another embodiment for determining a frequency measurement. [Diagram 5] 1 is a graph showing an electrocardiogram of a cardiac cycle. [Figure 6] FIG. 6 is a graph showing an oscillatory electrocardiogram of the cardiac cycle, recorded simultaneously with the electrocardiogram of FIG. 5. [Figure 7]7 is a graph showing a window superimposed on the oscillatory electrocardiogram of FIG. 6. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0056] FIG. 1 shows a schematic representation of an embodiment of a system 12 for quantifying cardiorespiratory endurance. The system 12 comprises an accelerometer 14 in the form of a piezoelectric element that can be placed on the chest of a person 18 to measure vibrations of the chest wall caused by the movement of the heart. A processor 20 is connected to the accelerometer 14. The processor 20 has its temporary memory 22 capable of storing signals received from the acceleration 14 and thereby capable of executing program code instructions. The system 12 comprises a support 26 that supports the accelerometer 14 and a housing 28 that contains the processor 20. The system 12 also has a non-transient memory 24 that stores program code instructions for the processor 20. For example, the system 12 as a whole may be an integral part of a smartphone, or all parts except the accelerometer 20 and the support 26 may form part of the smartphone. In one embodiment, the accelerometer 14 is an integrated accelerometer of the smartphone.

[0057] In one embodiment of system 12, system 12 additionally includes an indicator 30 operatively connected to processor 20. Indicator 30 may include, for example, an LCD display or the like capable of displaying output information from processor 20, such as numbers indicating a cardiopulmonary fitness measurement.

[0058] The system also includes electrocardiogram electrodes 32 (two leads, and a ground) supported by the support 26. The electrodes 32 are connected to the processor 20.

[0059] The primary function of the accelerometer 14 is to sample the oscillatory electrocardiogram (SCG) for further analysis. The primary function of the electrodes 32 is to sample the electrocardiogram (ECG) which is used to segment the oscillatory electrocardiogram (SCG).

[0060] The program code instructions in the non-transitory memory 24 cause the processor 20 to execute the method shown in Figure 2. A vibratory electrocardiogram (SCG) is acquired (102) by the accelerometer 14 placed on the chest of the person 18. In an alternative embodiment, the SCG is downloaded from a server where it has been previously uploaded. Characteristics of two signal features are determined in the vibratory electrocardiogram (SCG) (104). How this is accomplished is described in detail below. A measurement indicative of cardiopulmonary endurance (VO2max) is then determined (106) based on the characteristics of the first signal feature.

[0061] A number of systolic and diastolic segments are determined in the SCG (108). This is achieved by an automatic segmentation method using an electrocardiogram (ECG) acquired simultaneously by electrodes 32 as a reference. In an alternative embodiment, the segmentation is based on the SCG as such (e.g., similar to the approach disclosed in U.S. Pat. No. 8,235,912), and noisy segments are then discarded (110), for example, as disclosed in WO 2017 / 216375.

[0062] Figure 5 shows the electrocardiogram (ECG) signal from electrodes 32 in units of volts (mV) as a function of time (ms), with the time reset relative to the peak of the R wave. Figure 6 shows the simultaneously recorded oscillatory electrocardiogram (SCG) with the accelerometer signal in units of equivalent gravity (g) as a function of time (ms) reset in the same way as the electrocardiogram (ECG).

[0063] Each of the resulting diastolic segments includes or comprises a first signal feature corresponding to an aortic valve closure (AC) of a single heart beat, and similarly, each of the resulting systolic segments includes a second signal feature corresponding to a combination of mitral valve closure (MC) and aortic valve opening (AO) of a cardiac cycle.

[0064] Then, fiducial points are identified 112 for each signal feature in the systolic and diastolic segments, similar to the identification described in WO 2017 / 216375. The following fiducial points are determined for each diastolic segment or first signal feature: Maximum aortic valve closure (AC) max ) Maximum value (AC max ) immediately before the first minimum (AC min ) The following fiducial points are determined at each systolic segment or for each second signal feature. Mitral valve closure (MC) zero crossing First maximum of aortic valve opening (AO) after mitral valve closure (MC) (AOmax) The above-mentioned reference points are illustrated in Figure 6. A characteristic of the signal feature is then determined (114) based at least in part on the reference points.

[0065] A first characteristic of a first signal feature corresponding to aortic valve closure (AC) is the amplitude difference (ACPeakToPeak) between the local maximum (ACmax) and the first local minimum (ACmin). A measurement representative of this characteristic (ACPeakToPeak) is determined from the average of the diastolic segment. A second characteristic of the same signal feature is the (first) time interval (ACTimePeakToPeak) between the local maximum (ACmax) and the first local minimum (ACmin) immediately preceding the local maximum (ACmax). A measurement representative of this characteristic (ACTimePeakToPeak) is determined from the average of the diastolic segment, normalized to one standard deviation. A third characteristic is the morphology measurement (ACTimePeakToPeak). Morphology The fourth characteristic is the morphology of the first signal feature, represented by a frequency measure (AC Frequency ). The determination of the latter two properties is described further below.

[0066] Morphometric values ​​(AC Morphology), a (first) window 34 is identified in the oscillatory electrocardiogram (SCG). The window 34 starts 50 ms before the local maximum (ACmax) and ends 250 ms after the local maximum (ACmax). The corresponding window 34 is shown in FIG. 7, which shows the oscillatory electrocardiogram (SCG) of FIG. 6.

[0067] Morphometric values ​​(AC Morphology ) is determined using early averaging. The steps for early averaging are shown in FIG. 3a. The mean diastolic segment is determined and a window 34 is identified in the mean diastolic segment (202). The amplitude within the window 34 is normalized to one standard deviation to reduce the effect of amplitude variation. Then, dimensionality reduction 204 is performed using a principal component analysis (PCA) performed on the window 34 of the mean diastolic segment to identify the principal components. In an alternative embodiment, the dimensionality reduction 204 is performed using an autoencoder (AE) with the window 34 of the diastolic segment as input, the autoencoder (AE) configured to compress the window to a few nodes in a neural network, for example 10 nodes. Then, regression 206 is performed using a linear regression with the principal components or nodes as input. In an alternative embodiment, the regression function is based on neural network regression, convolutional neural network regression, or support vector machine regression. The regression 206 may include a morphometric measure (AC measure) that represents or quantifies the shape of a first signal feature or a constellation of oscillatory electrocardiograms (SCGs) associated with aortic valve closure (AC). Morphology )210.

[0068] In an alternative embodiment, morphometric values ​​(AC Morphology) is determined using late averaging. The steps for late averaging are shown in FIG. 3b. Windows 34 are identified in each diastolic segment (202'). The amplitude in each window 34 is normalized to one standard deviation to reduce the effect of amplitude variation. A dimensionality reduction 204' is then performed using a principal component analysis (PCA) performed on the matrix of windows 34 to identify the principal components. In an alternative embodiment, the dimensionality reduction 204' is performed using an autoencoder (AE) in a neural network with the matrix of windows 34 as input to compress them into a number of nodes, such as 10. A regression 206 is then performed using a linear regression function with the principal components or nodes as input. In an alternative embodiment, the regression function is based on neural network regression, convolutional neural network regression, or support vector machine regression. The regression 206' results in individual measurements per window in the diastolic segment. An average over the individual measurements is calculated (208') to obtain the morphology measurements (AC Morphology ) 210' is obtained.

[0069] Frequency measurement value (AC Frequency When determining the diastolic segment, the morphometric value (AC Morphology ) is used.

[0070] Frequency measurement value (AC Frequency ) is determined using initial averaging. The steps for initial averaging are shown in FIG. 4a. A window 34 is identified in each average diastolic segment (302'). The amplitudes in the window 34 are normalized to one standard deviation to reduce the effects of amplitude variation. A power spectral density (PSD) is then determined in each window 34 (304). The power spectral density (PSD) is determined (306), which is a morphometric measure (AC MorphologyThe resulting principal components or nodes are then used in dimensionality reduction 308 similar to dimensionality reduction 204 described above with respect to the initial averaging approach to determine the averaging window frequency (AC) of the first signal feature extension. Regression 310 is then performed using the resulting principal components or nodes as inputs similar to regression 206 described above with respect to the initial averaging approach. Regression 310 generates a frequency measure (AC) in the extension of the first signal feature that represents or quantifies the characteristic in the frequency domain of the averaging window. Frequency )314.

[0071] In an alternative embodiment, the frequency measurement (AC Frequency ) is determined using end-phase averaging. The steps involved in end-phase averaging are shown in FIG. 4b. A window 34 is identified in each diastolic segment (302'). The amplitude in each window 34 is normalized to one standard deviation to reduce the effects of amplitude variation. A power spectral density (PSD) is determined in each window 34. Instead of calculating the average power spectral density (PSD), a morphometric measure (AC Morphology A dimension reduction 308' similar to dimension reduction 204' described above with respect to the end averaging approach to determine the AC dsb) is performed on the determined matrix of power spectral densities. A regression 310' is then performed using the principal components or nodes for each window as input, similar to regression 206' described with respect to the end averaging approach above. The regression 310' results in a number of individual measurements, each corresponding to a window of the diastolic segment. The frequency measurements (AC dsb) are then calculated using the frequency measurements (AC dsb) and the corresponding dimensionality measurements (Dsb) of the determined matrix of power spectral densities. Frequency ) 314' is determined as the average 312, or in an alternative embodiment, the median, of the individual measurements.

[0072] The four characteristics of the first signal feature described above are used at input to determine 106 a measurement indicative of cardiopulmonary endurance (V02max). Additionally, heart rate variability (HRV) and characteristics of a second signal feature corresponding to mitral valve closure (MC) and aortic valve opening (AO) of the cardiac cycle are used as inputs.

[0073] Heart rate variability (HRV) is determined by first identifying subsequent heart beats in the oscillating electrocardiogram (SCG) and calculating it as the length of the time interval between the same reference points, such as the maximum of aortic valve closure (AC) (ACmax), in the subsequent heart beats.

[0074] The second signal feature characteristic is the time interval (SysTimeMCToAO) between the zero crossing of mitral valve closure (MC) and the first maximum (A0max) of aortic valve opening (AO) after mitral valve closure (MC). The characteristic measurement is determined from the average of the diastolic segment, normalized to one standard deviation.

[0075] Demographic measurements 116 representing the person's age, sex, and body mass index are also provided.

[0076] All of the above measurements are used as inputs in a multi-parameter regression 118 to determine 106 a measurement indicative of cardiopulmonary endurance (V02max) 120. In practice, in a preferred embodiment, the regression is expressed as follows: V02maxPrKG = ω1 ACPeakToPeak + ω2 ACTimePeakToPeak + ω3 ACFrequency + ω4 ACMorphology + ω5 HRV + ω6 SysTimeMCToAO + ω7 Age + ω8 Gender + ω9 BMI In an alternative embodiment, fewer measurements are used in the regression 118. Here, a measurement indicative of cardiopulmonary endurance (V02maxPrKG) is normalized to body weight.

[0077] [Proof of concept] The proposed method has been validated in 145 measurements from 133 subjects. In each measurement, an oscillatory electrocardiogram was recorded immediately followed by the subject undergoing a regular V02max test. The latter is considered the golden standard, and the performance measure was determined as the correlation between the V02max predicted by the proposed method and the golden standard V02max. In the final validation, the standard error of estimate (SEE) was used to evaluate the error between the predicted and the golden standard V02max.

[0078] Validation of individual features is done using 5-fold cross-validation, with 3-fold used for training, 1-fold used for validation, and 1-fold used for testing. Validation of the final scores is done using 5 iterations of 10-fold cross-validation.

[0079] For frequency measures (ACFrequency), the best performing feature extraction method was linear regression after PCA with early averaging (see Table 1). For morphology measures (ACMorphology), the best performing feature extraction method was linear regression after PCA with late averaging (see Table 2).

[0080] Table 3 shows the correlation between the reference or gold standard V02max and the determined measurements indicative of predicted V02max or cardiorespiratory endurance (V02max). Results including frequency measurements (ACFrequency) and morphology measurements (ACMorphology) and results excluding frequency measurements (ACFrequency) and morphology measurements (ACMorphology) are shown. It can be concluded from Table 3 that both of these measurements increase the correlation between the reference V02max and the predicted V02max. It can be concluded that the addition of both frequency measurements (ACFrequency) and morphology measurements (ACMorphology) improves the performance of the proposed method for determining measurements indicative of cardiorespiratory endurance or V02max (V02max).

[0081]

Table 1

[0082]

Table 2

[0083]

Table 3

Claims

1. 1. A method (100) for quantifying cardiopulmonary endurance, comprising: acquiring (102) a vibratory electrocardiogram (SCG) recorded by an accelerometer (14) configured to measure accelerations and vibrations of the chest wall of a person (18) caused by movements of the myocardium; determining (104) a characteristic of a first signal feature (AC) in the oscillatory electrocardiogram (SCG), the first signal feature (AC) corresponding to an aortic valve closure (AC) of a heart beat; Including, The step of determining (104) a characteristic of the first signal characteristic (AC) comprises: Determining (114) a frequency measure (AC Frequency) of the first signal characteristic (AC); determining (114) a morphology metric (ACMorphology) of the first signal feature (AC); Including, the morphometry measures a shape, contour, and / or outline of the first signal feature; the frequency metric (ACFrequency) and the morphology metric (ACMorphology) form part of the characteristic of the first signal feature (AC); The method comprises: determining (106) a measure indicative of cardiopulmonary endurance (VO2max) based on characteristics of the first signal feature (AC) by applying the characteristics of the first signal feature (AC) to a trained first machine learning model; The method further comprising:

2. The step of determining (104) a characteristic of the first signal characteristic (AC) comprises: determining (108) a plurality of diastolic segments of the oscillatory electrocardiogram (SCG); Discarding noisy diastolic segments (110); Including, The method of claim 1 , wherein the non-discarded diastolic segments include the first signal feature (AC).

3. The step of determining (104) a characteristic of the first signal characteristic (AC) comprises: Identifying (112) one or more fiducial points of said first signal characteristic (AC). Including, the measure indicative of cardiopulmonary endurance is further determined based on the one or more reference points; The method of claim 2 , wherein the reference point comprises a maximum (ACmax) of the first signal characteristic (AC) and / or a first minimum (ACmin) immediately preceding the maximum (ACmax).

4. The step of determining (104) a characteristic of the first signal characteristic (AC) comprises: determining (114) an amplitude difference (ACPeakToPeak) between a maximum (ACmax) of the first signal feature (AC) and a first minimum (ACmin) immediately preceding said maximum (ACmax); Including, The method according to any one of claims 1 to 3, wherein the step of determining (106) a measure indicative of cardiopulmonary endurance (VO2max) is further based on the amplitude difference (ACPeakToPeak).

5. The step of determining (104) a characteristic of the first signal characteristic (AC) comprises: Determining (114) a first time interval (ACTTimePeakToPeak) between a maximum (ACmax) and a first minimum (ACmin) immediately preceding said maximum (ACmax) of said first signal feature (AC); determining (106) a measurement indicative of said cardiopulmonary endurance (VO2max) based on said first time interval (ACTimePeakToPeak); The method according to any one of claims 1 to 4, comprising:

6. The step of determining morphology (ACMorphology) (114) comprises: determining (202, 202') a first window (34) of the oscillatory electrocardiogram (SCG), the first window (34) including or encompassing the first signal feature (AC); determining (210, 210') said morphology metric (ACMorphology) based on a time domain of said first window (34); The method according to any one of claims 1 to 5, comprising:

7. The method of claim 6, wherein the first window (34) has a width in the range of 200 ms to 500 ms, 250 ms to 450 ms, or 300 ms to 400 ms.

8. The step of determining the frequency measurement (ACFrequency) (104) comprises: determining (302, 302') a second window (34) of the oscillating electrocardiogram (SCG), the second window (34) including or encompassing the first signal feature (AC); determining (314, 314') said frequency measurement (AC Frequency) based on the frequency domain of said second window (34); The method according to any one of claims 1 to 7, comprising:

9. determining (114) heart rate variability (HRV); The method according to any one of claims 1 to 8, wherein the step of determining (106) a measure indicative of cardiopulmonary endurance (VO2max) is further based on the heart rate variability (HRV).

10. determining (104) a characteristic of a second signal feature (MC+AC) in the vibratory electrocardiogram (SCG), the second signal feature (MC+AC) being different from or disjoint to the first signal feature (AC); The step of determining (106) a measure indicative of cardiopulmonary endurance (VO2max) further comprises: The method according to any one of claims 1 to 9, wherein the second signal feature (MC+AC) corresponds to mitral valve closure (MC) and / or aortic valve opening (AO) of a cardiac cycle.

11. The step of determining (104) a characteristic of the second signal feature (MC+AC) comprises: determining (114) a second time interval (SysTimeMCToAO) between a zero crossing of the mitral valve closure (MC) and a first maximum (AOmax) of the aortic valve opening (AO) after the mitral valve closure (MC); Including, 11. The method of claim 10, wherein determining (106) the measure indicative of cardiopulmonary endurance (VO2max) is further based on determining (114) the second time interval (SysTimeMCToAO).

12. The method of any one of claims 1 to 11, wherein the first machine learning model is trained on characteristics of signal features that are operationally similar to characteristics of the first signal feature (AC).

13. A system (12) for quantifying cardiopulmonary endurance or determining an index of said cardiopulmonary endurance, comprising: (A) an accelerometer (14) configured to be placed on a chest of a person (18) for measuring accelerations and vibrations of a chest wall of the person (18) caused by movement of the myocardium; (B) a processor (20) operatively connected to the accelerometer (14), the processor (20) being configured to perform any of the steps of the method of any one of claims 1 to 12; A system comprising:

14. A computer program for use in a system (12) for quantifying cardiopulmonary endurance or determining an index of said cardiopulmonary endurance, comprising: (A) an accelerometer (14) configured to be placed on a chest of a person (18) for measuring accelerations and vibrations of a chest wall of the person (18) caused by movement of the myocardium; (B) a processor (20) operatively connected to the accelerometer; A computer program which, when executed by the processor (20) of the system (12), causes the processor (20) to carry out the steps of the method according to any one of claims 1 to 12.

15. A non-transitory memory (24) in which a computer program according to claim 14 is stored.

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