System and method for radar-based heart sound detection

Abstract

A system and method for heart sound detection utilizes a radar sensor, wherein phase-tracking-based motion cancellation is performed to mitigate effects of body motions. Method steps include: receiving a radar return signal reflected from a chest region of the subject; down-converting the received radar return signal to a baseband signal; processing the baseband signal by performing phase-tracking-based motion cancellation to mitigate effects of body motions of the subject and produce a motion-cancelled signal; and processing the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject. Such steps may be performed using a radar transceiver and a signal processor coupled thereto.

Description

SYSTEM AND METHOD FOR RADAR-BASED HEART SOUND DETECTION CROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 609,716 filed on December 13, 2023, wherein the entire contents of the foregoing application are hereby incorporated by reference herein. TECHNICAL FIELD

[0002] This disclosure relates to heart sound detection, and more specifically to radar-based detection of heart sounds. BACKGROUND

[0003] Blood flows through the heart and generates noises known as heart sounds. These noises occur due to heart valves opening and closing as the heart pumps blood. Some sounds, such as the 4thheart sound (i.e., a low-pitched sound coincident with late diastolic filling of the ventricle due to atrial contraction), are very characteristic of significant pathological lesions that have major pathophysiological consequences. Doctors and medical practitioners often use an acoustic stethoscope to conduct auscultation of the heart as a very important first step in any physical examination.

[0004] Accessibility of digital heart sound (HS) data for a subject carries more critical information in addition to HS auscultation. It requires a sensory technology that is convenient to use and provides objective measurements in a continuous fashion. The current solution relies on an electronic stethoscope with wireless connection. However, these wearables still require direct physical contact with a subject, and are susceptible to background noise and subject motion that limit their utility in providing consistently accurate heart sound detection.

[0005] The art continues to seek improvement in heart sound detection to address challenges attributable to subject motion, including involuntary body motion and random body motion. SUMMARY

[0006] Aspects of the present disclosure relate to a novel motion-tolerant approach for heart sound detection using a radar sensor (e.g., a FrequencyModulated Continuous Wave (FMCW) radar sensor), wherein phase-tracking-based motion cancellation is performed to mitigate effects of body motions.

[0007] In one aspect, the disclosure relates to a method for detecting heart sounds of a subject, the method comprising: receiving a radar return signal reflected from a chest region of the subject; down-converting the received radar return signal to a baseband signal; processing the baseband signal by performing phase-tracking- based motion cancellation to mitigate effects of body motions of the subject and produce a motion-cancelled signal; and processing the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject.

[0008] In certain embodiments, the method further comprises extracting heart sounds from the acoustic data.

[0009] In certain embodiments, the radar return signal comprises frequency modulated continuous wave (FMCW) chirp sequences, and the phase-tracking- based motion cancellation comprises tracking large-scale body motions at multiple frequencies of the FMCW chirp sequences.

[0010] In certain embodiments, the phase-tracking-based motion cancellation comprises: extracting subject displacement signals by collecting the baseband signal from range profiles at multiple range bins; extracting modulated phase signals from the subject displacement signals; and applying moving average filtering to the modulated phase signals.

[0011] In certain embodiments, the extracting of subject displacement signals comprises use of Fast Fourier Transformation (FFT).

[0012] In certain embodiments, the moving average filtering returns local mean values, and each mean value is calculated over a sliding window across neighboring ones of the modulated phase signals.

[0013] In certain embodiments, the processing of the motion-cancelled signal to produce acoustic data comprises use of Short-Time Fourier Transformation (STFT).

[0014] In certain embodiments, the processing of the motion-cancelled signal to produce acoustic data further comprises bandpass filtering

[0015] In another aspect, the disclosure relates to a heart sound detection system comprising: a radar transceiver; and a signal processor coupled to the radar transceiver, the signal processor being configured to: receive, from the radar transceiver, a radar return signal reflected from a chest region of the subject; down-convert the received radar return signal to a baseband signal; process the baseband signal by performing phase-tracking-based motion cancellation to mitigate effects of body motions of the subject and produce a motion-cancelled signal; and process the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject.

[0016] In certain embodiments, the signal processor is further configured to extract heart sounds from the acoustic data.

[0017] In certain embodiments, when performing the phase-tracking-based motion cancellation, the signal processor is configured to: extract subject displacement signals by collecting the baseband signal from range profiles at multiple range bins; extract modulated phase signals from the subject displacement signals; and apply moving average filtering to the modulated phase signals.

[0018] In another aspect, the disclosure relates to a non-transitory computer readable medium comprising computer-readable instructions, that when executed by a processor, cause the processor to perform operations, the operations comprising: down-converting a radar return signal reflected from a chest region of the subject to a baseband signal; processing the baseband signal by performing phase-tracking- based motion cancellation to mitigate effects of body motions of the subject and produce a motion-cancelled signal; and processing the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject. In certain embodiments, the operations further comprise extracting heart sounds from the acoustic data.

[0019] In another aspect, any two or more features of aspects and / or embodiments disclosed herein may be combined for additional advantage. BRIEF DESCRIPTION OF DRAWINGS

[0020] FIG.1 is a schematic diagram of an exemplary heart sound detection system that remotely detects heart sounds of one or more subjects using a radar transceiver and a signal processor according to methods described herein.

[0021] FIG.2 is a flowchart identifying steps of a radar-based, motion-tolerant heart sound detection method according to one embodiment.

[0022] FIG.3A is a time-based waveform for heart sounds obtained by a radar- based, motion-tolerant heart sound detection method as disclosed herein.

[0023] FIG.3B is a frequency-based waveform corresponding to the time-based waveform of FIG.3A.

[0024] FIG.4A is a is a time-based waveform for modeled heart sounds.

[0025] FIG.4B is a frequency-based waveform corresponding to the time-based waveform of FIG.4A.

[0026] FIG.5A is a spectrogram of simulated heart sound signals based on modeled heart sounds and body motions, without applying phase-tracking-based motion cancellation.

[0027] FIG.5B is a spectrogram of the items represented in FIG.5A, but with application of phase-tracking-based motion cancellation.

[0028] FIG.6A is a spectrogram of simulated heart sound signals based on measured heart sounds and modeled body motions, without applying phase- tracking-based motion cancellation.

[0029] FIG.6B is a spectrogram of the items represented in FIG.6A, but with application of phase-tracking-based motion cancellation.

[0030] FIG.7 is a plot of displacement versus time obtained by measured body motion displacement of a human subject.

[0031] FIG.8 provides plots of two measured time-domain heart sound waveforms, including one (lower) plot before applying phase-tracking-based motion cancellation, and another (upper) plot after applying phase-tracking-based motion cancellation.

[0032] FIG.9A is a spectrogram of measured heart sound signals, without applying phase-tracking-based motion cancellation.

[0033] FIG.9B is a spectrogram of the items represented in FIG.9A, but with application of phase-tracking-based motion cancellation.

[0034] FIG.10 is a block diagram of at least a portion of a heart sound detection system according to one or more embodiments disclosed herein. DETAILED DESCRIPTION

[0035] The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the embodiments and illustrate the best mode of practicing the embodiments. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand theconcepts of the disclosure and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.

[0036] It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0037] It will be understood that when an element such as a layer, region, or substrate is referred to as being "on" or extending "onto" another element, it can be directly on or extend directly onto the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly on" or extending "directly onto" another element, there are no intervening elements present. Likewise, it will be understood that when an element such as a layer, region, or substrate is referred to as being "over" or extending "over" another element, it can be directly over or extend directly over the other element or intervening elements may also be present. In contrast, when an element is referred to as being "directly over" or extending "directly over" another element, there are no intervening elements present. It will also be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present.

[0038] Relative terms such as "below" or "above" or "upper" or "lower" or "horizontal" or "vertical" may be used herein to describe a relationship of one element, layer, or region to another element, layer, or region as illustrated in the Figures. It will be understood that these terms and those discussed above are intended to encompass different orientations of the device in addition to the orientation depicted in the Figures.

[0039] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As usedherein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises," "comprising," "includes," and / or "including" when used herein specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0040] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. It will be further understood that terms used herein should be interpreted as having a meaning that is consistent with their meaning in the context of this specification and the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0041] Although prior works have utilized radar-based heart sound detection from chest surface vibrations due to sound pressure waves, those prior considered only stationary measurement setups, without providing motion tolerance to account for (e.g., involuntary) human body motions.

[0042] Aspects of the present disclosure relate to a systems and methods tolerant approach for heart sound detection using radar (e.g., a Frequency Modulated Continuous Wave (FMCW) radar sensor) and signal processing, including performance of phase-tracking-based motion cancellation in order to mitigate effects of body motions. In certain embodiments, a motion cancellation technique involves a range-gated phase-based tracking for estimation and suppression of motion of a subject.

[0043] FIG.1 is a schematic diagram of exemplary heart sound detection system 12 that remotely detects heart sounds of one or more subjects according to embodiments described herein. The heart sound detection system 12 provides a non-contact approach to measuring acoustic features of a heart 15 of a human subject 14. The heart sound detection system 12 includes a radar sensor 16 (e.g., an ultra-wide band (UWB) radar sensor) to remotely measure acoustic features of the heart 15 of the subject 14. The heart sound detection system 12 also includes asignal processor 18 which processes radar signals received by the radar sensor 16 to extract heart acoustic features of the subject 14.

[0044] In an exemplary aspect, the radar sensor 16 is coupled to the signal processor 18, which processes a radar return signal 20 (reflected from the subject 14) received by the radar sensor 16. The radar return signal 20 carries acoustic information corresponding to the heart 15 of the subject 14, and the signal processor 18 extracts one or more acoustic features (e.g., heart sounds) of the subject 14 therefrom.

[0045] The radar sensor 16 includes a radar receiver 24 to receive the radar return signal 20 and may further include a radar transmitter 26 which emits a radar signal 22. The radar sensor 16 (via the receiver 24) can receive a radar return signal 20 in any RF band, such as terrestrial radio frequencies, gigahertz (GHz) bands, terahertz bands, microwave bands, etc. The radar return signal 20 can be a reflection of the radar signal 22 emitted by the radar transmitter 26 as reflected off a chest or torso of the subject 14. The radar signal 22 may penetrate a portion of the skin of the subject 14 before reflecting back to the radar sensor 16. In some examples, the radar sensor 16 operates on a frequency-modulated continuous-wave (FMCW) signaling scheme with a wide bandwidth in the millimeter or terahertz RF bands. In various embodiments, the radar transmitter 26 can emit the radar signal 22 at predefined intervals over a period of time. The radar receiver 24 can receive a plurality of reflected radar return signals 20, where each radar return signal 20 can correspond to a respective radar signal 22. The signal processor 18 can identify acoustic data by analyzing the radar return signals 20, in particular by processing the radar return signals by comparing phase differences between respective radar return signals 20 which can indicate movement of the subject 14, and in particular, movement caused by vibrations associated with heart sounds or respiratory sounds caused by the lungs 17 or other portion of the subject 14. Based on the frequency band of the radar return signal 20, the amount and frequency of occurrence of the phase differences (indicating magnitude and frequency of acoustic sound) the signal processor 18 can identify acoustic features and vital signs from the radar return signal 20.

[0046] In an embodiment, the radar transmitter 26 can transmit the radar signals 22 at predefined frequencies and / or bands based on the signal desired to beextracted. The radar transmitter 26 can also alternate the frequency and / or band according to some predefined pattern (e.g., sequentially) to capture acoustic data associated with different sources.

[0047] FIG.2 is a flow chart identifying steps employed by a signal processor to perform a radar-based, motion-tolerant heart sound detection method 30 according to one embodiment. One step 32 involves down-converting one or more received radar signals to baseband signals. Another step 34 involves range gating. A further step 36 involves phase-based motion tracking. Another step 38 involves moving average filtering. In combination, the foregoing steps 34, 36, 38 provide motion cancellation utility, which operate on FMCW dechirped data samples prior to performance of range FFT steps to address range migration. A range compression step 42, a bandpass filtering step 44, and a Short Time Fourier Transfer (STFT) step 46 are performed to permit heart sounds (e.g., a heart sound histogram) to be obtained at step 48. Further details regarding implantation of the radar-based, motion-tolerant heart sound detection method 30 follow.

[0048] When Frequency-Modulated Continuous Wave (FMCW) radar is used, such radar exploits linearly frequency-modulated chirp sequences. A transmitted FMCW signal reflected by a target (i.e., a single human subject) is received and down-converted to baseband signals. This radar beat signal is expressed according to Equation (1):where t and τ represent slow-time and fast-time index, respectively. f0 is the chirp start frequency, d0 is an initial target range, and γ is a slope of the FMCW chirp, where B is signal bandwidth and Tc is a chirp duration. Also, RT is a target motion displacement. To extract the target motion displacement, Fast Fourier Transforms (FFTs) are performed over Equation (1) along τ. A target displacement signal may be extracted by collecting the baseband signal from range profiles at the desired target range bins. The normalized complex baseband signal at the target range gives Equation (2):

[0049] When a radar signal is reflected from a target (i.e., a human subject) having random body motions (RBM), the reflected radar signal may capture RBM b(t), vital motion v(t), including respiration and heartbeat, and heart-sound-induced surface skin motion h(t). Motion of the target (i.e., subject) is a sum of the foregoing three types of motion, namely, RT(t) = b(t) + v(t) + h(t). The baseband signal model of the composite signal gives Equation (3):where l(t) = b(t)+v(t) denotes the low-frequency vital motion.

[0050] Existing radar-based methods estimating heart rate rely on spectral analysis. The spectral peaks at the heartbeat frequency region of interest are the estimation candidates. However, this is not feasible when the human body motion is involved because RBM will mask v(t). The spectral support of RBM can extend beyond a few Hz.

[0051] For radar-based HS detection, the major radar measurable HS frequency resides above 20 Hz, which is not directly overwhelmed by the motion artifact spectrum. However, the HS frequency will be shifted as much as the Doppler frequency induced by RBM velocity in Eqn.4 due to the convolution effect, which is represented by Equation (4):

[0052] To successfully recover HS frequency, a deconvolution method is required. This is accomplished by the motion cancellation technique disclosed herein.

[0053] To mitigate the effect of unwanted motion artifacts, a phase-tracking- based motion cancellation technique is provided for radar-based HS detection,according to the flowchart shown in FIG.2. The RBM cancellation directly operates on the FMCW dechirped data samples prior to the range FFT, which deals with the range migration issue.

[0054] Before tracking the motion-induced phase variations, the desired target is to be isolated in the range profiles. To this end, FFTs may be performed along τ to have range profiles, in which several range bins of the desired target are extracted and zero-padded before being converted back into time-domain complex baseband signals by using inverse FFT (i.e., STFT according to step 46 in FIG.2).

[0055] To extract the large-scale body motion, a multifrequency-based phase- tracking algorithm (according to step 36 in FIG.2) is adopted for the FMCW radar. At a fixed frequency fn, the sampled beat signal in t gives Equation (5):where ∆f denotes the frequency difference between subbands. For a fixed fn and τn, Eqn.5 has the form of a phase-modulated CW radar with a frequency of fn. For N samples per frequency sweep, N number of CW signals are leveraged in parallel to estimate motion displacement, from f1to fN. The modulated phase signals ϕ(τn,t) = 4π(d0+RT(t)) / λnare extracted by taking phase angles or other phase demodulation method.

[0056] The estimated phase of motion displacement ϕ(τn,t) dominates the spectral and potentially spreads beyond 20 Hz. In order to recover the high- frequency HS in the subsequent cancellation step, a moving average filter (according to step 38 in FIG.2) is applied to ϕ(τn,t). The number of samples M to average is determined by the designed cut-off frequency at 20 Hz. Thus, the RBM phase estimate is provided by Equation (6):where f denotes a filtering operator which returns a local Mpoint mean values and each mean is calculated over a sliding window of length M across neighboring elements in ϕ(.).

[0057] Note that the RBM cancellation occurs in complex baseband, the desired signal after cancellation has the form expressed in Equations (7) and (8):

[0058] Given that sc(τ,t) is constant within one chirp, the summation of sc(τ,t) along fast-time increases the signal-to-noise ratio of the desired HS signal. Therefore, the ideal HS signal without motion artifacts is finally obtained according to Equation 9:

[0059] By applying a short-time Fourier transform (STFT) over hc(t), the HS spectrogram is obtained (corresponding to heart sound detection at step 48 in FIG. 2).

[0060] To validate the approach disclosed hereinabove, simulations were performed, including utilization of a radar heart sound signal model and a random body motion model. Radar Heart Sound Model

[0061] A radar HS signal model is briefly described (and adopted from Isabella Lenz, Yu Rong, and Daniel W. Bliss, “Radarcardiograph Signal Modeling and Time- Frequency Analysis,” in 2023 IEEE Radar Conference. IEEE, 2023). This model captures the general structure of each individual radar HS and is derived from actual radar HS from several subjects. Each cardiac cycle consists of two distinguishable heart sounds, S1and S2. In the time domain, the time duration, peak amplitude, andseparation between each HS vary from subject to subject. Visual inspection of individual heart sounds indicates that they reach peak value in the center of the heart sound and taper in a Gaussian shape. Each individual heart sound is found to consist of two main frequency components, with one very strong lower frequency component being in the 20-30 Hz range, and the other much weaker component being in the 30-50 Hz range.

[0062] To capture both main frequency components and their respective strengths, the underlying signal is modeled as a sum of two weighted sine waves. A Gaussian window is applied to taper the shape of the model waveform to resemble the shape of the radar HS. The modeled HS is given according to Equation (10): where f1 and f2 are the strong and weak frequency components, respectively, and w(t) is the windowing function given as Equation (11):where P is the peak amplitude of the respective HS, and σ = Ts / 2 with Ts being the time duration of the HS. The spacing, time duration, frequency components, and amplitudes of the model are parameterized.

[0063] FIGS.3A, 3B, 4A, and 4B permit comparison in time and frequency between radar heart sound (RHS) and model heart sound. FIG.3A is a time-based waveform for heart sounds obtained by a radar-based, motion-tolerant heart sound detection method as disclosed herein. FIG.3B is a frequency-based waveform corresponding to the time-based waveform of FIG.3A. FIG.4A is a is a time-based waveform for modeled heart sounds. FIG.4B is a frequency-based waveform corresponding to the time-based waveform of FIG.4A. In FIGS.3B and 4B, the stronger lower-frequency component and the weaker higher-frequency component are both visible. Random Body Motion Model

[0064] The RBM is generated with a random work model in back-and-forth movement at a varying random pace. For evaluation purposes, the model is parameterized by two parameters, maximum motion velocity in both directions, Vmax=60 mm / s, and maximum motion magnitude, Mmax= 100 mm. Since the goal is to recover the heart sound signal, the other vital signs (namely, heartbeat and respiration) are ignored. The total signal length lasts for 10 s and the RBM persists the entire duration, assuming the subject is located 0.7 meters away, and assuming there is no signal propagation loss in the air for simplicity. The FMCW radar operates from 77 GHz with a bandwidth of 3.6 GHz and a frame rate of 1000 Hz. Simulated Example

[0065] The proposed signal processing was simulated with the modeled heart sound and random body motion described in the foregoing sections concerning the radar heart sound model and the random body motion model. FIGS.5A and 5B show the spectrogram of the simulated heart sound without the RBM cancellation technique, and with the RBM cancellation, respectively. As shown in FIG.5A, before applying the motion cancellation, RBM overwhelms the HS and dominantly appears at the frequency range of the HS. On the other hand, as shown in FIG.5B, the proposed technique detects only heart sounds by totally removing motion artifacts within the whole computerized prognosis index (CPI) (which are patient-based artifacts that occur with involuntary or voluntary patient movement during data acquisition). Synthesized Example

[0066] The same RBM model in the random body motion model is considered here and applied to radar-measured heart sound signals in stationary case in order to mimic the RBM effect on HS detection. The radar measurement is taken from a stationary subject with normal breathing. The RBM parameters are fixed as the same as the simulated example. The effectiveness of the proposed method for the synthesized examples is seen in FIGS.6A and 6B. FIGS.6A and 6B show the spectrogram of the synthesized heart sound without the RBM cancellation technique, and with the RBM cancellation, respectively. Compared to the simulated example (described in connection with FIGS.5A and 5B), the signal-to-noise ratio (SNR) of the heart sound is degraded in the synthesized example depicted in FIGS.6A and 6B, because it includes various radar system noises in the measured heart sound signal.Experimental Measurement and Results

[0067] For measurements, human subject was located at a distance of 0.7 meter from a radar transceiver. The radar capture system had bandwidth 3.6 GHz, the frame repetition interval was 1 ms, the chirp duration was 50 µs, the ADC sampling rate was 2 MHz, and the center frequency of the radar was 77 GHz. During the data capture, the human subject was constantly moving back-and-forth.

[0068] Body motion displacement of the human subject was extracted as shown in FIG.7. The random body motion displacement is approximately 4 cm, which is 10 times larger than the wavelength of the radar. It is worth noting that the direct spectral analysis from the measured displacement may not allow detection of the heart sound signal because phase errors that occur during the phase demodulation procedure overwhelm the heart sound vibration.

[0069] FIG.8 provides plots of two measured time-domain heart sound waveforms obtained after band-pass filtering the processed signals, including one (lower) plot before applying phase-tracking-based motion cancellation, and another (upper) plot after applying phase-tracking-based motion cancellation. Before applying the motion cancellation algorithm, the undesirable motion artifacts are observed, which have a much larger amplitude than that of the heart sound. These motion artifacts can directly mislead the HS event detection. On the other hand, the proposed motion cancellation removes the motion artifacts, which recovers cardiac cycles consisting of two distinguishable heart sounds, S1 and S2.

[0070] FIG.9A is a spectrogram of measured heart sound signals, without applying phase-tracking-based motion cancellation. FIG.9B is a spectrogram of the items represented in FIG.9A, but with application of phase-tracking-based motion cancellation.

[0071] The spectrogram of the measured HS validates the effectiveness of the proposed heart sound detection algorithm. Similar to the results of the time-domain waveform, heart sounds can be clearly identified after removing RBM. Without RBM cancellation, the motion artifacts dominate the spectrum over the frequency region of the HS. With the help of the motion cancellation technique, it is also possible to estimate heartbeat rate or heartbeat rate variability from the spectral signatures of the heart sounds.

[0072] FIG.10 is a block diagram of the heart sound detection system 12 according to embodiments disclosed herein. The heart sound detection system 12 includes or is implemented as a computer system 100, which comprises any computing or electronic device capable of including firmware, hardware, and / or executing software instructions that could be used to perform any of the methods or functions described above. In this regard, the computer system 100 may be a circuit or circuits included in an electronic board card, such as a printed circuit board (PCB), a server, a personal computer, a desktop computer, a laptop computer, an array of computers, a personal digital assistant (PDA), a computing pad, a mobile device, or any other device, and may represent, for example, a server or a user's computer.

[0073] The exemplary computer system 100 in this embodiment includes a processing device 102 or processor, a system memory 104, and a system bus 106. The processing device 102 represents one or more commercially available or proprietary general-purpose processing devices, such as a microprocessor, central processing unit (CPU), or the like. More particularly, the processing device 102 may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor implementing other instruction sets, or other processors implementing a combination of instruction sets. The processing device 102 is configured to execute processing logic instructions 120 for performing the operations and steps discussed herein.

[0074] In this regard, the various illustrative logical blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed with the processing device 102, which may be a microprocessor, field programmable gate array (FPGA), a digital signal processor (DSP), an application- specific integrated circuit (ASIC), or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. Furthermore, the processing device 102 may be a microprocessor, or may be any conventional processor, controller, microcontroller, or state machine. The processing device 102 may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration).

[0075] The system memory 104 may include non-volatile memory 108 and volatile memory 110. The non-volatile memory 108 may include read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), and the like. The volatile memory 110 generally includes random-access memory (RAM) (e.g., dynamic random-access memory (DRAM), such as synchronous DRAM (SDRAM)). A basic input / output system (BIOS) 112 may be stored in the non-volatile memory 108 and can include the basic routines that help to transfer information between elements within the computer system 100.

[0076] The system bus 106 provides an interface for system components including, but not limited to, the system memory 104 and the processing device 102. The system bus 106 may be any of several types of bus structures that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and / or a local bus using any of a variety of commercially available bus architectures.

[0077] The computer system 100 may further include or be coupled to a non- transitory computer-readable storage medium, such as a storage device 114, which may represent an internal or external hard disk drive (HDD), flash memory, or the like. The storage device 114 and other drives associated with computer-readable media and computer-usable media may provide non-volatile storage of data, data structures, computer-executable instructions, and the like. Although the description of computer-readable media above refers to an HDD, it should be appreciated that other types of media that are readable by a computer, such as optical disks, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the operating environment, and, further, that any such media may contain computer-executable instructions for performing novel methods of the disclosed embodiments.

[0078] An operating system 116 and any number of program modules 118 or other applications can be stored in the volatile memory 110, wherein the program modules 118 represent a wide array of computer-executable instructions corresponding to programs, applications, functions, and the like that may implement the functionality described herein in whole or in part, such as through instructions 120 on the processing device 102. The program modules 118 may also reside on thestorage mechanism provided by the storage device 114. As such, all or a portion of the functionality described herein may be implemented as a computer program product stored on a transitory or non-transitory computer-usable or computer- readable storage medium, such as the storage device 114, volatile memory 110, non-volatile memory 108, instructions 120, and the like. The computer program product includes complex programming instructions, such as complex computer- readable program code, to cause the processing device 102 to carry out the steps necessary to implement the functions described herein.

[0079] An operator, such as the user, may also be able to enter one or more configuration commands to the computer system 100 through a keyboard, a pointing device such as a mouse, or a touch-sensitive surface, such as the display device, via an input device interface 122 or remotely through a web interface, terminal program, or the like via a communication interface 124. The communication interface 124 may be wired or wireless and facilitate communications with any number of devices via a communications network in a direct or indirect fashion. An output device, such as a display device, can be coupled to the system bus 106 and driven by a video port 126. Additional inputs and outputs to the computer system 100 may be provided through the system bus 106 as appropriate to implement embodiments described herein.

[0080] A motion-robust signal processing technique for radar-based HS and recovery is provided herein. The phase variations induced by body motions are canceled by tracking large-scale body motions at multiple frequencies of the FMCW chirp sequences. Model-based simulations and real scenario measurements validate that the proposed method successfully detects HS even in the presence of RBM. The presented results demonstrate the feasibility of radar-based heart monitoring in practical applications.

[0081] Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present disclosure. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow. Any of the various features and elements as disclosed herein may be combined with one or more other disclosed features and elements unless indicated to the contrary herein.

Claims

Claims What is claimed is:

1. A method for detecting heart sounds of a subject, the method comprising: receiving a radar return signal from a chest region of the subject; down-converting the received radar return signal to a baseband signal; processing the baseband signal by performing phase-tracking-based motion cancellation to mitigate effects of body motions of the subject and produce a motion- cancelled signal; processing the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject.

2. The method of claim 1, further comprising extracting heart sounds from the acoustic data.

3. The method of claim 1, wherein the radar return signal comprises frequency modulated continuous wave (FMCW) chirp sequences, and wherein the phase- tracking-based motion cancellation comprises tracking large-scale body motions at multiple frequencies of the FMCW chirp sequences.

4. The method of any one of claims 1 to 3, wherein the phase-tracking-based motion cancellation comprises: extracting subject displacement signals by collecting the baseband signal from range profiles at multiple range bins; extracting modulated phase signals from the subject displacement signals; and applying moving average filtering to the modulated phase signals.

5. The method of claim 4, wherein the extracting of subject displacement signals comprises use of Fast Fourier Transformation (FFT).

6. The method of claim 4, wherein the moving average filtering returns local mean values, and each local mean value is calculated over a sliding window across neighboring ones of the modulated phase signals.

7. The method of claim 4, wherein the processing of the motion-cancelled signal to produce acoustic data comprises use of Short-Time Fourier Transformation (STFT).

8. The method of claim 7, wherein the processing of the motion-cancelled signal to produce acoustic data further comprises bandpass filtering.

9. A heart sound detection system comprising: a radar transceiver; and a signal processor coupled to the radar transceiver, the signal processor being configured to: receive a radar return signal from a chest region of the subject; down-convert the received radar return signal to a baseband signal; process the baseband signal by performing phase-tracking-based motion cancellation to mitigate effects of body motions of the subject and produce a motion-cancelled signal; and process the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject.

10. The heart sound detection system of claim 9, wherein the signal processor is further configured to extract heart sounds from the acoustic data.

11. The heart sound detection system of claim 9, wherein the radar return signal comprises frequency modulated continuous wave (FMCW) chirp sequences, and wherein the phase-tracking-based motion cancellation comprises tracking large- scale body motions at multiple frequencies of the FMCW chirp sequences.

12. The heart sound detection system of any one of claims 9 to 11, wherein in performing the phase-tracking-based motion cancellation, the signal processor is configured to: extract subject displacement signals by collecting the baseband signal from range profiles at multiple range bins; extract modulated phase signals from the subject displacement signals; and apply moving average filtering to the modulated phase signals.

13. The heart sound detection system of claim 12, wherein the extracting of subject displacement signals comprises use of Fast Fourier Transformation (FFT).

14. The heart sound detection system of claim 12, wherein the moving average filtering returns local mean values, and each local mean value is calculated over a sliding window across neighboring ones of the modulated phase signals.

15. The heart sound detection system of claim 12, wherein the processing of the motion-cancelled signal to produce acoustic data comprises use of Short-Time Fourier Transformation (STFT).

16. The heart sound detection system of claim 15, wherein the processing of the motion-cancelled signal to produce acoustic data further comprises bandpass filtering.

17. A non-transitory computer readable medium comprising computer-readable instructions, that when executed by a processor, cause the processor to perform operations, the operations comprising: down-converting a radar return signal from a chest region of the subject to a baseband signal; processing the baseband signal by performing phase-tracking-based motion cancellation to mitigate effects of body motions of the subject and produce a motion- cancelled signal; and processing the motion-cancelled signal to produce acoustic data corresponding to a heart of the subject.

18. The non-transitory computer readable medium of claim 17, wherein the operations further comprise extracting heart sounds from the acoustic data.

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