Method of operation of a wearable sensor device for screening structural heart disease

Abstract

A wireless wearable sensor device, system, method, and non-transitory computer-readable medium for screening for structural heart disease based on electrocardiogram, phonocardiogram, and / or accelerometer signals on a patient's skin surface.

Description

[Background technology]

[0001] [CROSS-REFERENCE TO RELATED APPLICATIONS] This application claims the benefit of U.S. Provisional Patent Application No. 62 / 728,199, filed Sep. 7, 2018, entitled “SCREENING FOR STRUCTURAL HEART DISEASE,” which is hereby incorporated by reference in its entirety.

[0002] [Field of the Invention] The present invention relates to medical equipment, and more particularly to screening for structural heart disease using a wearable sensor device that measures one or more electrocardiogram, phonocardiogram and accelerometer leads on the chest skin surface.

[0003] [background] Auscultation is the technique of listening to sounds associated with the functioning of internal organs such as the heart, lungs, and intestines using a stethoscope, and is one of the basic diagnostic tools used by physicians. The structural and functional health of the heart can be diagnosed by listening to sounds mainly generated by the closure of the atrioventricular and semilunar valves, and by manual auscultation of the heart. However, traditional stethoscopes used as spot-check diagnostic tools rely on the physician's skill and experience, are inherently limited by the limits of human audibility in the perception of certain magnitudes and bands of sound, and cannot record sound data for future reference.

[0004] More than 5 million people in the United States are diagnosed with valvular heart disease each year, so advances in objective heart sound analysis using electronic recordings are crucial. With an aging population, acquired structural heart disease, such as calcific aortic stenosis and mitral regurgitation, has become the third most common cardiovascular disease. Approximately 1.5 million adults in the United States and 56% in Europe are aged 70 years or older with aortic stenosis. Moreover, congenital heart disease, the most common disease involving structural defects of the heart at birth, affects more than 71,000 children each year in the United States and the European Union. Effective medical management and surgical treatment planning of structural and congenital heart disease may require objective and reliable monitoring of cardiac function, including heart sounds.

[0005] The advent of digital stethoscopes has enabled clinicians to record heart sounds as digital waveforms, thus incorporating a visual component beyond auscultation into the examination. Heart sound recordings, called phonocardiograms (PCGs), are recorded with microphones placed at common cardiac auscultation sites. These sites include the aorta, pulmonary artery, tricuspid valve, and mitral valve in the chest. PCG recordings consist primarily of the basic heart sounds S1 and S2 (sounds called "lub" and "dub," respectively). In addition to these normal heart sounds, relatively weak S3 and S4 heart sounds may be present in individuals at risk for heart failure, as well as other abnormal sounds such as opening snaps, ejection sounds, murmurs, and mid-systolic clicks. Applying digital signal processing and machine learning techniques to these PCG signals can overcome the limitations of manual auscultation and enable effective clinical diagnosis using heart sounds. However, automatic classification of heart sounds is not straightforward, as PCGs can be obscured by background noise, respiratory sounds, bowel sounds, and sensor movement relative to the body. Continuous monitoring of heart sounds using sensors and advanced algorithms in wearable medical devices can screen or diagnose abnormal cardiac conditions / defects, allowing early appropriate medical intervention.

[0006] Automated classification of heart sound pathology has evolved over the past decade using a variety of feature extractions, including signal amplitude, frequency, wavelets, time-frequency spectrum measurements, and statistical methods, in combination with segmentation and classification methods. However, mass screening for structural heart disease that can accurately segment and classify heart sounds into normal and abnormal classes in a hospital or home environment using continuous wearable sensor devices that can measure multiple physiological signals, including ECG, PPG, and accelerometers, remains a challenge.

[0007] The auditory filter model is designed to mimic how the human ear filters sound, using an array of independent bandpass filters that recognize and discriminate sound signals more effectively at low frequencies than at higher frequencies. Because the spectral power of most heart sounds occurs primarily at and overlaps with low frequencies (especially below 200 Hz), this ability to discern changes at lower frequencies in greater detail is a useful feature for PCG analysis. While auditory filter models have been successfully applied to speech analysis, they remain relatively unexplored in the classification of heart sounds. Previous work using auditory filter models for PCG analysis includes detection of murmurs and assessment of patent ductus arteriosus.

[0008] Therefore, there is a strong need for a solution that overcomes the aforementioned problems, and the present invention addresses such a need. Summary of the Invention [Means for solving the problem]

[0009] A method for screening for structural heart disease using a wearable sensor device that measures one or more leads of electrocardiogram, phonocardiogram, and accelerometer signals on a chest skin surface is disclosed, the method includes measuring electrocardiogram, phonocardiogram, and accelerometer signals on a patient's skin surface, dividing the measured signals into one or more sequences based on a measurement of body acceleration, screening decisions relating to one or more sequences of electrocardiogram and phonocardiogram derived features, determining a sequence of aggregated screening decisions, and combining the individual decisions to screen for structural heart disease. [Brief description of the drawings]

[0010] The accompanying drawings illustrate several embodiments of the present invention and, together with the description, serve to explain the principles of the present invention. Those skilled in the art can readily appreciate that the embodiments shown in the drawings are merely exemplary and are not intended to limit the scope of the present invention.

[0011] [Figure 1] FIG. 1 illustrates a wireless wearable sensor device for health monitoring according to an embodiment. [Diagram 2] FIG. 2 shows a flow chart for screening for structural heart disease. [Diagram 3] FIG. 3 shows a block diagram for the implementation of the actions taken to screen for structural heart disease. [Figure 4a] FIG. 4a illustrates an exemplary process of physiological segmentation. [Figure 4b] FIG. 4b illustrates an exemplary process of model-based segmentation. [Diagram 5] FIG. 5 shows the aggregation of sequences from the sequence screening engine and the finite state machine. [Figure 6] FIG. 6 shows a block diagram of a computing device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Although detailed embodiments of the claimed structures and methods are disclosed herein, it can be understood that the disclosed embodiments are merely examples of the claimed structures and methods that can be embodied in various forms. However, the present invention may be embodied in many different forms and should not be construed as being limited to the exemplary embodiments described herein. In the description, details of features and techniques well known to those skilled in the art may be omitted to avoid unnecessarily obscuring the presented embodiments.

[0013] References herein to "one embodiment," "one embodiment," "exemplary embodiment," and the like indicate that the described embodiment may include a particular feature, structure, or characteristic, but not all embodiments necessarily include the particular feature, structure, or characteristic. Moreover, such phrases do not necessarily refer to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with one embodiment, it is submitted that it is within the knowledge of one of ordinary skill in the art to affect such feature, structure, or characteristic in connection with or in combination with other embodiments, whether or not explicitly described.

[0014] 1 shows a wireless wearable sensor device 100 for measuring electrocardiogram (ECG), phonocardiogram (PCG), and accelerometer (ACC) signals on a patient's skin surface, for example on the patient's chest. The wireless wearable sensor device 100 or wearable device includes one or more sensors 102, a processor 104 coupled to the one or more sensors 102, a memory 106 coupled to the processor 104, a wireless wearable sensor device application 108 coupled to the memory 106, and a transmitter 110 coupled to the wireless wearable sensor device application 108.

[0015] The wireless wearable sensor device 100 is worn by a user to measure ECG, PCG, and ACC signals on the patient's skin surface, for example, on the patient's chest. The one or more sensors 102 include, but are not limited to, single-lead or multi-lead ECG, single-axis or multi-axis accelerometer, and PCG sensor. The one or more sensors 102 acquire ECG, PCG, and ACC signals on the patient's skin surface, which are transmitted to the memory 106 and further transmitted to the wireless wearable sensor device application 108 via the processor 104. The memory 106 may be a flash memory. The processor 104 executes the wireless wearable sensor device application 108 to process and acquire information about the user's health. The information may be sent to the transmitter 110 and transmitted to another user or device for further processing, analysis, and storage. That is, the transmitter 110 may transmit various ECG, PCG, and ACC data to a remote device / server (e.g., a smartphone, a cloud-based server) for processing, analysis, and storage. The transmitter 110 may be a Bluetooth Low Energy (BLE) transceiver. Alternatively, the wireless wearable sensor device 100 may process and analyze the temperature data locally via a wireless wearable sensor device application 108 stored in the memory 106 and implemented by the processor 104.

[0016] The processor 104 is either a microprocessor or a reusable electronic module. Those skilled in the art can readily recognize that a variety of devices can be utilized for the one or more sensors 102, the processor 104, the memory 106, the wireless wearable sensor device application 108, and the transmitter 110 and are within the spirit and scope of the present application.

[0017] The wireless wearable sensor device 100 may be a very low-cost, fully disposable, battery-operated, adhesive biopatch biosensor with integrated sensors and Bluetooth Low Energy (BLE) transceiver that is worn on the skin of a user and used in conjunction with an electronic module to detect, record, and analyze multiple ECG, PCG, and ACC signals. The wireless wearable sensor device 100 continuously collects ECG, PCG, and ACC signals from the patch wearer. The wireless wearable sensor device 100 then encrypts and transmits the encrypted data in a bidirectional communication to a hub relay, which may forward the data to a secure server where it is stored and available for viewing, downloading, and analysis. With this information, a healthcare provider can monitor the improvement or deterioration of a patient's structural heart disease and intervene as necessary. To improve biosensor event delivery, events, including events that are occurring, are stored in the flash memory of the wireless wearable sensor device 100 to prevent data loss. By storing the data locally, the wireless wearable sensor device 100 does not need to maintain a Bluetooth connection all the time. For example, no data is lost when the wearer is out of Bluetooth range, and reconnection occurs automatically when the wireless wearable sensor device 100 is in range of the hub relay. When the wireless wearable sensor device 100 has a connection with the relay, it transmits data at regular intervals and receives confirmation from the relay that the transmission was successful. The wireless wearable sensor device 100 may include on-board flash memory that stores firmware, configuration files, and sensor data. Healthcare providers can configure how the sensor collects data. Depending on how the biosensor is used, individual data streams (such as ECG, PCG, and ACC signals) can be enabled or disabled.

[0018] The ECG, PCG, and ACC signals are then processed, filtered, modeled, and analyzed using either the wearable device 100's integrated processors and algorithms (such as a reusable electronics module or a system-on-chip board) or an external processing device (such as a smartphone device or a cloud-based server network).

[0019] Moreover, one skilled in the art will readily recognize that a variety of wireless wearable sensor devices may be utilized without being limited to wearable devices within the spirit and scope of the present invention.

[0020] Solutions further described herein relate to the application of auditory filter models for accurate classification of segmented heart sound signals using canonical features derived from the ECG and probabilistic ordering of physiological events using multiple signals.

[0021] 2 illustrates a flowchart 200 for an exemplary implementation of actions taken to screen for structural heart disease in accordance with exemplary embodiments described herein. Exemplary embodiments may include methods, programs (e.g., computer-executable instructions and components), systems, apparatus, and / or devices.

[0022] In this example, the wearable sensor measures, at 210, signals of an electrocardiogram (ECG), a phonocardiogram (PCG), and an accelerometer (ACC) on the skin surface of a patient, for example, on the patient's chest. According to at least one exemplary embodiment, at 220, the signals may be divided into one or more sequences based on measurements of body acceleration derived from accelerometer data. To determine structural heart disease, at 230, a screening algorithm may be individually applied to each sequence of the ECG and PCG signals, that is, a screening determination may be made. At 240, the screening outputs of the individual sequences are aggregated, that is, a sequence of aggregated screening determinations is determined. The final screening determination may be determined from the individual determinations, at 250, by combining the individual determinations to screen for structural heart disease.

[0023] More details of the above system and method for screening for structural heart disease are shown in flowchart 300 of FIG. 3. The sensor system 310 includes a single-lead or multi-lead ECG, a uniaxial or multi-axial accelerometer, and a PCG sensor 320. Exemplary embodiments of the form factor of the wearable sensor device include an adhesive patch sensor, a chest band, or one or more electronic modules adhered to the patient's body. The signals may be filtered to remove noise and time-aligned considering any propagation delays. Next, the time-aligned ECG, PCG, and accelerometer signals 330 are divided into windows W of any duration i at 340, and x i (t), y i (t), and z i (t) are obtained respectively (i is the window number, and 1 ≤ i ≤ M). In each window W i , the measurement of body acceleration is calculated as a function of z i (t) and may be compared with a threshold K such that W i =(f(z i (t)) < K) (341), where

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[0024] The sequence screening engine 370 detects segmentation-specific features 374 and sequence-specific features 375 Dependent on the two categories of features, including j Screening for heart sounds. j For each heart beat in (t), segmentation specific features may be derived after segmentation into constituent cardiac phases such as S1, systole, S2, and diastole. 371 and model-based segmentation 372 The single channels of segmentation such as are used independently to determine the constituent cardiac phases. The final position of the segment can then be obtained using a weighted sum of the detection values ​​of the single channels. The intrinsic features of the sequence can be obtained without segmentation. jIt is derived directly from (t).

[0025] FIG. 4a shows a physiological segmentation 371. This segmentation includes x, y, z, z, z z z ... j Then, as shown in FIG. 4a, the reference point at (t) is determined. j Using the duration between the peak of the R wave (T1) and the end of the T wave (T2) of a beat from (t), y j Determine the position of S1 and systole (Sys) at (t). Similarly, x j The duration between the end of the T wave of one beat from (t) and the peak of the R wave of the next beat is y j (t) is used to determine the location of S2 and diastole (Dia) in (t). The boundaries between S1 and systole and between S2 and diastole may be learned from a training set.

[0026] Model-based segmentation 372 uses a hidden Markov model (HMM) to j As an example, the PCG signal y j (t) is filtered using a second-order Butterworth bandpass filter between 25 and 400 Hz, F s = 1000 Hz. An illustration of this process for determining T1 and T2 is shown in Figure 4b. The expected durations of T1 and T2 are modeled using a probabilistic distribution, and this model may be trained to derive the emission probabilities of the HMM. Specifically, given the true states Q = {q1, q2, ..., q t While} is unknown, the observed sequence O is known. Thus, the model finds the state sequence that is most likely to produce the observed state.

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[0027] A weighted sum of single channel detection values ​​(physiological and model-based segmentation) may be compared to a threshold to determine the final location of the segment (i.e., 373 channel fusion and segment detection).

[0028] The feature is y j In addition to extracting features directly from (n), they may be extracted based on the output of segmentation. Below is an example of extracting segmentation-specific features 374 and sequence-specific features 375 based on auditory frequency cepstral coefficients (AFCC).

[0029] First, the PCG signal y(n) may be divided into i frames of duration d with some overlap, assuming that speech frequencies are relatively stationary within these time frames. Then, the power spectrum P of each frame is calculated as i (k) may be calculated by applying a Hanning window w(n) of length N to reduce spectral leakage, similar to the function of the cochlea, to estimate the amount of energy at different frequencies in the signal as follows:

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[0030]

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[0031] Finally, we apply a discrete cosine transform to decorrelate the logarithm of the energy of the filter bank to obtain the coefficient c i,j can be obtained.

[0032]

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[0033] For segmentation specific features 374, a feature vector may be calculated by applying a statistical measure such as the mean, standard deviation, or higher order moments to each coefficient over the frames of each phase of the cardiac cycle determined by the weighted sum of the single channel segmentations. The total number of features calculated by the segmentation is n c *n p *n s is equal to n c is the number of extracted coefficients, n p is the number of phases of the segmented cardiac cycle, n s is the number of statistical measures used. On the other hand, a sequence-specific feature vector without segmentation may be computed by applying a statistical measure to each coefficient across all frames, and the total number of features is n c *n s is equal to.

[0034] Classification may be performed independently on the segment-specific and sequence-specific features using classifier A 376 and classifier B 377. The classifiers may belong to machine learning algorithms such as logistic regression, support vector machines, artificial neural networks, gradient boosting, etc. As an example, the radial basis function (RBF) kernel

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[0035] To address the uneven ratio of normal and abnormal heart sound recordings in clinical practice, the weighted SVM uses a cost function for a set of weights w defined as follows:

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[0036] Screening Output D j 379a and 379b are vectors D = [D a D b ...D n ] and fed into a sequence aggregation model of 380 to determine the final decision. j379a, 379b may be binary (1,0), categorical, numeric (0,1,2,3,4,...). The sequence aggregation model may be a weighted combination of voting schemes, recurrent neural networks, and finite state machines to recognize sequences. FIG. 5 shows an exemplary embodiment of sequence aggregation and state machines for identifying four consecutive positive sequences, i.e., an exemplary embodiment of sequence aggregation and finite state machines from the sequence screening engine for identifying the final screening sequence 1111. Symbol D a is first fed to state A, and the next symbol D b and so on until it reaches a final state E, which indicates the presence of structural heart disease. Voting schemes such as majority voting or threshold crossing, and recurrent neural networks such as long short-term memory, gated recurrent units, bidirectional networks, and variations thereof, are also within the scope of the present invention. This allows for a final screening decision 390 to be obtained for screening for structural heart disease.

[0037] Those skilled in the art will recognize that, for this and other processes and methods disclosed herein, the functions performed in the processes and methods may be performed in differing orders. Additionally, the outlined steps and operations are provided only as examples, and some steps and operations may be optionally combined into fewer steps and operations or expanded into additional steps and operations without detracting from the essence of the disclosed embodiments.

[0038] Furthermore, the disclosure is not limited to the specific embodiments described herein, which are intended as illustrative of various aspects. As will be apparent to those skilled in the art, many modifications and variations are possible without departing from the spirit and scope thereof. In addition to those enumerated herein, functionally equivalent methods, as well as devices, within the scope of the disclosure will become apparent to those skilled in the art from the foregoing description. Such modifications and variations are intended to fall within the scope of the appended claims. The disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that the disclosure is not limited to particular methods, reagents, compounds, compositions, or biological systems, which may of course vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

[0039] 6 illustrates a sample computing device 600 in which various embodiments of wearable sensors in a ubiquitous computing environment may be implemented. More specifically, FIG. 6 illustrates an exemplary computing embodiment in which any of the operations, steps, etc. described herein may be implemented as computer-readable instructions stored on a computer-readable medium. The computer-readable instructions may be executed by a processor of, for example, a mobile unit, a network element, and / or any other computing device.

[0040] In a very basic configuration 602, the computing device 600 typically includes one or more processors 604 and a system memory 606. A memory bus 608 may be used for communication between the processor 604 and the system memory 606.

[0041] Depending on the desired configuration, the processor 604 may be of any type, including but not limited to a microprocessor (μP), a microcontroller (μC), a digital signal processor (DSP), or any combination thereof. The processor 604 may include one or more levels of caching, such as a level 1 cache 610 and a level 2 cache 612, a processor core 614, and registers 616. An exemplary processor core 614 may include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An exemplary memory controller 618 may also be used with the processor 604, or in some implementations, the memory controller 618 may be an internal part of the processor 604.

[0042] Depending on the desired configuration, system memory 606 may be of any type, including but not limited to, volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 606 may include an operating system 620, one or more applications 622, and program data 624.

[0043] Applications 622 may include a client application 680 arranged to perform functions described herein, including those described above with respect to Figures 1-5. Program data 624 may include tables 650, which may alternatively be referred to as "graphics tables 650" or "distribution tables 650," that are useful for measuring ECG, PCG, and ACC signals on a patient's skin surface, as described herein.

[0044] The computing device 600 may have additional features or functions and additional interfaces to facilitate communication between the basic configuration 602 and any necessary devices and interfaces. For example, a bus / interface controller 630 may be used to facilitate communication between the basic configuration 602 and one or more data storage devices 632 via a storage interface bus 634. The data storage devices 632 may be removable storage devices 636, non-removable storage devices 638, or a combination thereof. Examples of removable and non-removable storage devices include magnetic disk drives such as floppy disk drives and hard disk drives (HDDs), optical disk drives such as compact disk (CD) drives or digital versatile disk (DVD) drives, solid state drives (SSDs), and tape drives, to name a few. Examples of computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data.

[0045] System memory 606, removable storage device 636, and non-removable storage device 638 are examples of computer storage media. Computer storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or other medium used to store the desired information and that can be accessed by computing device 600. Any such computer storage media may be part of computing device 600.

[0046] The computing device 600 may also include an interface bus 640 for facilitating communication from various interface devices, such as an output device 642, a peripheral interface 644, and a communication device 646 to the basic configuration 602 via the bus / interface controller 630. An exemplary output device 642 may include a graphics processing unit 648 and an audio processing unit 650, which may be configured to communicate with various external devices, such as a display or speakers, via one or more A / V ports 652. An exemplary peripheral interface 644 may include a serial interface controller 654 or a parallel interface controller 656, which may be configured to communicate with external devices, such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I / O ports 658. An exemplary communication device 646 may include a network controller 660, which may be arranged to facilitate communication with one or more other computing devices 662 over a network communication link via one or more communication ports 664.

[0047] A network communication link may be an example of a communication medium. Communication media may typically be embodied with computer-readable instructions, data structures, program modules, or other data in a carrier modulated data signal or other transmission mechanism and may include any information delivery media. A "modulated data signal" may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. In one example, and not by way of limitation, communication media may include wired media, such as a wired network or direct-wired connection, and wireless media, such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media. As used herein, the term computer-readable media may include both storage media and communication media.

[0048] Computing device 600 may be implemented as part of a small form factor portable (or mobile) electronic device such as a mobile phone, a personal data assistant (PDA), a personal media playback device, a wireless web watch device, a personal headset device, an application specific device, or a hybrid device including any of the above functionality. Computing device 400 may also be implemented as a personal computer, including both laptop and non-laptop computer configurations.

[0049] There is little distinction between hardware and software implementations of system aspects. The use of hardware or software is generally (although not always, and in certain circumstances the choice between hardware and software may be important) a design choice that presents a trade-off between cost and efficiency. There are various means, e.g., hardware, software, and / or firmware, for implementing the processes and / or systems and / or other techniques described herein, and the preferred means may vary depending on the context in which the processes and / or systems and / or other techniques are deployed. For example, if an implementer determines that speed and accuracy are paramount, the implementer may select a primarily hardware and / or firmware means; if flexibility is paramount, the implementer may select a primarily software implementation; or, still alternatively, the implementer may select some combination of hardware, software, and / or firmware.

[0050] In the foregoing detailed description, various embodiments of devices and / or processes for determining a patient's temperature have been illustrated using block diagrams, flow charts, and / or examples. To the extent that such block diagrams, flow charts, and / or examples include one or more functions and / or operations, it will be appreciated by those skilled in the art that each function and / or operation within such block diagrams, flow charts, or examples, individually and / or collectively, can be implemented by a wide variety of hardware, software, firmware, or substantially any combination thereof. In an embodiment, some portions of the subject matter described herein can be implemented via an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or other integrated form. However, those skilled in the art will recognize that some aspects of the embodiments disclosed herein can be equivalently implemented, in whole or in part, in an integrated circuit, as one or more computer programs running on one or more computers (e.g., as one or more programs running on one or more computer systems), as one or more programs running on one or more processors (e.g., as one or more programs running on one or more processors), as firmware, or substantially any combination thereof. Additionally, designing circuitry and writing software or firmware code is believed to be within the skill of one of ordinary skill in the art in light of this disclosure. Moreover, those skilled in the art will appreciate that the mechanisms of the subject matter described herein can be distributed as program products in a variety of forms, and that the exemplary embodiments of the subject matter described herein apply regardless of the particular type of signal-bearing medium used to actually effect the distribution.Examples of signal bearing media include, but are not limited to, recordable media such as floppy disks, hard disk drives, CDs, DVDs, digital tape, computer memory, and transmission media such as digital and / or analog communications media such as fiber optic cables, wave guides, wired communications links, wireless communications links, and the like.

[0051] Those skilled in the art will recognize that it is common in the art to describe devices and / or processes in the manner described herein and then integrate such described devices and / or processes into a data processing system using engineering techniques. That is, at least some of the devices and / or processes described herein can be integrated into a data processing system through a reasonable amount of experimentation. Those skilled in the art will recognize that a typical data processing system generally includes one or more of a system unit housing, a video display device, memory such as volatile and non-volatile memory, a processor such as a microprocessor and a digital signal processor, computational entities such as an operating system, drivers, a graphical user interface, and application programs, one or more interaction devices such as a touchpad or screen, and / or a control system including feedback loops and control motors (e.g., feedback for sensing position and / or velocity; control motors for moving and / or adjusting components and / or quantities). A typical data processing system can be implemented utilizing any suitable commercially available components such as those found in data computing / communication and / or network computing / communication systems.

[0052] In the present specification, different components may be depicted as being included in or connected to other components. It should be understood that the architectures depicted are merely examples, and that in fact many other architectures that achieve the same functionality may be implemented. Conceptually, the arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Thus, in the present specification, two components that are combined to achieve a particular functionality may be viewed as being "associated" with each other such that the desired functionality is achieved, regardless of the architecture or components between medical devices. Similarly, any two components that are associated in this manner may also be viewed as being "operably connected" or "operably coupled" with each other to achieve the desired functionality, and any two components that can be associated in this manner may also be viewed as being "operably coupled" with each other to achieve the desired functionality. Specific examples of components that can be operably coupled include, but are not limited to, components that can be physically mated and / or physically interacting, and / or components that can be wirelessly interacted and / or wirelessly interacting, and / or components that can be logically interacted and / or logically interacting.

[0053] Finally, with respect to the use of substantially any plural and / or singular terms herein, those skilled in the art can translate from plural to singular and / or from singular to plural as appropriate to the context and / or application. Various singular / plural permutations may be expressly set forth herein for clarity.

[0054] In general, those skilled in the art will understand that the terms used herein, particularly in the appended claims, e.g., in the appended claim body, are generally intended as "open" terms. For example, the term "including" should be interpreted as "including, but not limited to," the term "having" should be interpreted as "having at least," and the term "include" should be interpreted as "including, but not limited to." Those skilled in the art will further understand that if a specific number of introduced claim recitations is intended, such intent will be expressly set forth in the claim, and in the absence of such recitation, no such intent exists. For example, to aid in understanding, the appended claims include the use of the introductory phrases "at least one" and "one or more" to introduce the claim recitations. However, the use of such phrases should not be interpreted to mean that the introduction of a claim recitation with the indefinite article "a" or "an" limits any particular claim containing such introduced claim recitation to an embodiment containing only one of such recitations, even if the same claim contains the introductory phrase "one or more" or "at least one" and an indefinite article such as "a" or "an", for example, "a" and / or "an" should be interpreted to mean "at least one" or "one or more", and similarly for the use of definite articles used to introduce claim recitations. Furthermore, even if a specific number of introduced claim recitations is explicitly recited, one of ordinary skill in the art will recognize that such recitation should be interpreted to mean at least the recited number. For example, when "two recited ones" is recited without the use of any other modifier, it means at least two recited ones, or two or more recited ones. Furthermore, when idiomatic phrases similar to "at least one of A, B, C, etc." are used, such a construction is generally intended in the sense that one of ordinary skill in the art would understand the idiomatic phrase.For example, "a system having at least one of A, B, and C" includes, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together. When phrases similar to "at least one of A, B, or C, etc." are used, such configurations are generally intended in the sense that one of ordinary skill in the art would understand the phrase, for example, "a system having at least one of A, B, or C" includes, but is not limited to, systems having A alone, B alone, C alone, A and B together, A and C together, B and C together, and / or A, B, and C together. Those of ordinary skill in the art will understand that virtually all disjunctions and / or phrases presenting two or more alternative terms, whether in the specification, claims, or drawings, contemplate the possibility of including either term, either term, or both terms. For example, the phrase "A or B" is understood to include the possibilities of "A" or "B" or "A and B."

[0055] Furthermore, where features or aspects of the disclosure are described in terms of the Markush group, those skilled in the art will recognize that the disclosure is also thereby described in terms of any individual members or subgroups of members of the Markush group.

[0056] From the foregoing, it will be understood that the various embodiments of the present disclosure have been described herein for purposes of illustration, and that various changes may be made without departing from the scope and spirit of the present disclosure. Accordingly, the various embodiments disclosed herein are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

1. 1. A method of operation of a wearable sensor device for screening for structural heart disease, comprising: the wearable sensor device includes one or more sensors; a processor coupled to the one or more sensors; a memory coupled to the processor and storing a wearable sensor device application; and a transmitter configured to transmit information acquired by execution of the wearable sensor device application by the processor; the one or more sensors measuring electrocardiogram (ECG), phonocardiogram (PCG), and accelerometer (ACC) signals on a skin surface of the patient and transmitting the ECG, PCG, and ACC signals to the processor; The processor executes the wearable sensor device application stored in the memory, filtering the ECG, PCG, and ACC signals to remove noise; time-aligning the filtered ECG, PCG, and ACC signals; The time-aligned ECG, PCG, and ACC signals are divided into M windows W of arbitrary duration. ｉ to obtain x i (t), y i (t), and z i (t) corresponding to the ECG, PCG, and ACC, respectively (i is a window number; 1≦i≦M); Next, based on the ACC signal, x ｉ (t), y ｉ (t), z ｉ selecting N sequences from the M windows W i of (t), each sequence S j of the N sequences including x j (t) and y j (t) corresponding to the ECG and PCG, respectively (j is a sequence number; 1≦j≦N; N≦M); Each sequence S j of the N sequences is individually provided to a sequence screening engine, which screens each sequence S j , thereby determining sequence-specific features for each y j (t) in the PCG; Segment the PCG by cardiac phase to obtain segmentation-specific features; classifying the sequence-specific features and the segmentation-specific features independently; combining the outputs of the classification results to determine each decision D j which is a screening output; aggregating said assessments D j to determine and output a final screening assessment indicative of the presence of structural heart disease; The segmentation-specific features are derived by performing, for each beat in y j (t), independent physiological and model-based segmentations to determine the cardiac phase; The physiological segmentation comprises: x including the peak of the R wave and the end of the T wave for each heartbeat; ｊ determining a reference point at (t); The x ｊ The peak of the R wave of a certain beat from (t) (T １ ) and the end of the T wave (T ２ ) to determine the duration between y ｊ Determining the location of S1 and systole (Sys) in (t); The x ｊ Using the duration between the end of the T wave of one beat from (t) and the peak of the R wave of the next beat, ｊ Determining the location of S2 and diastole (Dia) at (t); learning the boundaries between S1 and systole and between S2 and diastole from a training set; Including, The model-based segmentation comprises: Using a hidden Markov model (HMM), ｊ determining a reference point at (t), y j (t) is defined as T including S1 and systole. １ and T including S2 and diastole ２ and learning the boundaries between S1 and systole and between S2 and diastole from a training set; determining a reference point in said y j (t) including The sequence-specific features are obtained by dividing the y ｊ (t) is directly derived from How it works.

2. The method of claim 1 , wherein determining and outputting a final screening decision indicative of the presence of structural heart disease comprises applying a screening algorithm to each sequence S j including x j (t) and y j (t).

3. The segmentation-specific features and the sequence-specific features are classified independently using classifier A and classifier B, respectively; 2. The method of claim 1, wherein the classifier A and the classifier B comprise machine learning algorithms including at least one of logistic regression, support vector machines, artificial neural networks, or gradient boosting.

4. The method of claim 1 , wherein aggregating each of the decisions D j to screen for structural heart disease comprises aggregating each of the decisions D j into a vector and feeding it to a sequence aggregation model to determine a final screening decision for structural heart disease.

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