WO2023223358A1 - An information processing apparatus and method for continuous estimation of respiratory signal and respiratory parameters - Google Patents

Abstract

The invention provides an information processing apparatus for the continuous estimation of respiratory signal and respiratory parameters. The server (105) may be configured to decompose the normalized bio-information using a variational mode decomposition method. The server (105) may be further configured to generate a periodogram of the decomposed 5-modes using Fast Fourier Transform to obtain a dominant frequency having a maximum power. The server may be further configured to estimate respiratory rate and other respiratory parameters such as tidal volume and lung capacity based on the obtained dominant frequency with clinical grade accuracy. The respiratory rate is estimated, in breaths per minute, by multiplying the dominant frequency of the respiratory component with 60.

Description

AN INFORMATION PROCESSING APPARATUS AND METHOD FOR CONTINUOUS ESTIMATION OF RESPIRATORY SIGNAE AND RESPIRATORY PARAMETERS

FIELD OF INVENTION

[0001] The present invention generally relates to a continuous estimation of respiratory signal and respiratory parameters from photoplethysmography (PPG) signal using variational mode decomposition (VMD) method.

BACKGROUND OF THE INVENTION

[0002] Background description includes information that may be used in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0003] At present, existing respiratory rate algorithms are not clinically grade accurate. Currently there is a separate capnography machine to measure respiratory rate and respiratory parameters of a patient. The machines that are available in the marketplace are expensive and not easily available in remote parts of the world. Finding other portal alternatives to capnography machines are either more expensive or inaccurate.

[0004] Therefore, there is a need for a simple information processing unit that can be attached to any pulse oximeter readily available even in remote parts of the world. A low cost, portable and easy to use device.

[0005] Further, recent respiratory rate estimation research articles use decomposition techniques such as Empirical Mode Decomposition (EMD) which suffers from the problem of mode-mixing which generates severe aliasing in the time-frequency distribution making the physical significance of intrinsic mode functions (IMFs) imprecise. Further, the Ensemble Empirical Mode Decomposition (EEMD) accepts the residual noise in input signal reproduction.

[0006] Most of the decomposition methods are limited by their algorithmic ad- hoc nature missing mathematical theory (EMD). (1) Recursive shifting in most approaches that prevent backward error correction; (2) The failure to properly handle noise; (3) The hard band restrictions (the wavelet techniques); (4) The selection of predefined filter bank boundaries (the empirical wavelet transform).

[0007] The US patent document US2019/0231257A1 discloses about an apparatus and methods for screening, diagnosis and monitoring of respiratory disorders. The analysis of the ECG data may include estimating a respiratory rate from the artefact - removed ECG data. The analysis of the ECG data may include extracting a respiratory - related component from the artefact - removed ECG data. The analysis of the 3D accelerometry data may include estimating a posture of the patient from the 3D accelerometry data. The analysis of the 3D accelerometery data may include estimating a respiratory effort of the patient from the 3D accelerometry data. The analysis of the 3D accelerometry data may include computing an activity index of the patient from the 3D accelerometer data. The activity index may represent gross bodily motion of the patient.

[0008] The US patent document US 2016/0367186A1 discloses devices and methods for non-invasive ventilation therapy. Non-invasive ventilation therapy systems and methods are disclosed. The system comprises a ventilation device, a computing device coupled to the ventilation device, and a plurality of sensors for acquiring a physiological bioelectrical impedance signal from a patient, wherein the sensors are functionally connected to the computing device. The computing device receives the physiological bioelectrical impedance signal from the sensors, analyzes the physiological bioelectrical impedance signal, and, based on the analyzed physiological bioelectrical impedance signal, transmits a signal to the ventilation device to adjust therapy levels. In a preferred embodiment, the computing device further provides an assessment of minute ventilation, tidal Volume, and respiratory rate of the patient based on the analyzed bioelectrical impedance signal.

[0009] However, the above-mentioned prior arts are not portable, have complex structure, are costly to manufacture, and may suffer from inaccuracies. Therefore, a need exists for a portable and cost-effective information processing apparatus and method for continuous estimation of respiratory signal and respiratory parameters.

[0010] Hence, in order to overcome the above shortcoming a simple information processing unit has been developed that can be attached to any pulse oximeter.

OBJECT OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are listed herein below.

[0011] The principal object of the invention is a continuous estimation of respiratory signal from photoplethysmography (PPG) signal using Variational Mode Decomposition (VMD) method.

[0012] Another object of the invention is to apply the method for estimating respiratory rate, tidal volume, lung volume, and lung capacity to any pulse oximetry system which will allow for low-cost measurement of respiratory parameters in a small form factor. [0013] Another object of the invention is to reduce healthcare expenditures on separate respiratory monitoring machines and also easily allow for remote outpatient monitoring of respiratory health.

[0014] Another object of the invention is to extract respiratory signals using VMD from PPG signals.

[0015] Another object of the invention is to calculate respiratory rate, tidal volume, lung volume and lung capacity.

[0016] Another object of the invention is to manage patients with chronic respiratory diseases and other pulmonary issues using VMD on PPG Signals.

[0017] Another object of the invention is to provide more clinically accurate respiratory rate measurements.

[0018] Another object of the invention is to estimate Respiratory Rate with clinical grade accuracy that falls in the acceptable range defined by the medical device regulatory authorities.

[0019] Another object of the invention is to extract the respiratory signal and parameters like respiratory rate, tidal volume and lung capacity using the respiratory signal, regardless of sampling frequency of the PPG signal.

[0020] Another object of the invention is to solve the problem of mode-mixing which generates severe aliasing in the time-frequency distribution making the physical significance of intrinsic mode functions (IMFs) imprecise. [0021] Another object of the invention is to compute the related bands adaptively, and simultaneously estimate the corresponding modes, and thus properly balance the error between them.

[0022] Another object of the invention is to address the presence of noise in the input signal.

[0023] Another object of the invention is to update each mode directly in the Fourier domain which makes the constraint optimization problem very simple and fast.

These and other objects and advantages of the present disclosure will be apparent to those skilled in the art after a consideration of the following detailed description taken in conjunction with the accompanying drawings in which a preferred form of the present disclosure is illustrated.

SUMMARY OF THE INVENTION

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

[0024] The invention provides an information processing apparatus for the continuous estimation of respiratory signals that can be utilized to calculate respiratory health parameters like respiratory rate, tidal volume, lung volume and lung capacity. The apparatus comprises a wearable device that is configured to acquire bio-information data. The bio -information data includes Photoplethysmography (PPG) signal data and accelerometer data. [0025] An electronic device configured to receive the bio-information data and a server communicatively coupled with the wearable device and the electronic device. The server may be configured to receive the bio-information data from at least one of the wearable device or the electronic device. The server may be further configured to decompose the normalized bio-information based on a variational mode decomposition (VMD) method. The server may be further configured to generate a periodogram on the decomposed normalized bioinformation in each of the plurality of modes based on Fast Fourier Transform (FFT) to obtain a dominant frequency having a maximum power and estimate respiratory rate from the obtained dominant frequency.

Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiment, along with the accompanying drawing figures in which like numerals represent like components.

BRIEF DESCRIPTION OF DRAWINGS

The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:

FIG. 1 illustrates a network environment of a system for estimating respiratory parameters continuously from Photoplethysmography signals, according to one embodiment of the invention; FIG. 2 illustrates a block diagram of a system for estimating respiratory parameters continuously from Photoplethysmography signals, in accordance with an example embodiment of the present invention;

FIG. 3 illustrates a flow diagram of estimating respiratory signals continuously from Photoplethysmography signals using variational mode decomposition method (VMD), according to one embodiment of the invention;

FIG. 4 illustrates estimating respiratory signals continuously using the Overlap- Windowing Technique, according to one embodiment of the invention;

FIG. 5 illustrates a pre-processing (DC Removal) of Photoplethysmography signals, according to one embodiment of the invention;

FIG. 6 illustrates denoising of pre-processed PPG signals using Discrete Wavelet Transform (DWT) based signal denoising technique, according to one embodiment of the invention;

FIG. 7 illustrates different modes after applying Variational mode decomposition (VMD) on PPG Signal, according to one embodiment of the invention;

FIG. 8 illustrates Periodogram Using FFT on different VMD modes, according to one embodiment of the invention; and

FIG. 9 illustrates selected VMD Mode-1 of Range 0.06- 1Hz Band, according to one embodiment of the invention.

DETAILED DESCRIPTION OF INVENTION

The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and / or detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practised and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

[0026] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of the phrase “in an embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiments.

[0027] Moreover, although the following description contains many specifics for the purposes of illustration, anyone skilled in the art will appreciate that many variations and/or alterations to said details are within the scope of the present disclosure. Similarly, although many of the features of the present disclosure are described in terms of each other, or in conjunction with each other, one skilled in the art will appreciate that many of these features can be provided independently of other features. Accordingly, this description of the present disclosure is set forth without any loss of generality to, and without imposing limitations upon the present disclosure.

[0028] As used in the application, the term ‘circuitry’ or ‘circuit’ refers to all of the following: (a) hardware-only circuit implementations (such as implementations in only analog and/or digital circuitry) and (b) to combinations of circuits and software (and/or firmware), such as (as applicable): (i) to a combination of processor(s) or (ii) to portions of processor(s)/software (including digital signal processor(s)), software, and memory(ies) that work together to enable an apparatus, such as a mobile phone or server, to perform various functions) and (c) to circuits, such as a microprocessor(s) or a portion of a microprocessor(s), that require software or firmware for operation, even if the software or firmware is not physically present and d) the interconnected brain and spinal cord circuits (both anatomical and functional circuits).

This definition of ‘circuitry’ applies to all uses of this term in this application, including in any claims. As a further example, as used in this application, the term “circuitry” would also cover an implementation of merely a processor (or multiple processors) or portion of a processor and its (or their) accompanying software and/or firmware.

[0029] The term “circuitry” would also cover, for example and if applicable to the particular claim element, a baseband integrated circuit or applications processor integrated circuit for a mobile phone or a similar integrated circuit in server, a cellular network device, or other network device. The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects.

[0030] In this description, the term “application” may also include files having executable content, such as: object code, scripts, byte code, markup language files, and patches. In addition, an “application” referred to herein, may also include files that are not executable in nature, such as documents that may need to be opened or other data files that need to be accessed. [0031] The term “content” may also include files having executable content, such as: object code, scripts, byte code, markup language files, and patches. In addition, “content” referred to herein, may also include files that are not executable in nature, such as documents that may need to be opened or other data files that need to be accessed.

[0032] As used in this description, the terms “component,” “database,” “module,” “system,” and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device may be a component. One or more components may reside within a process and/or thread of execution, and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components may execute from various computer readable media having various data structures stored thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems by way of the signal).

[0033] In this description, the terms “communication device,” “wireless device,” “wireless telephone,” “wireless communication device,” and “wireless handset” are used interchangeably. With the advent of third generation (“3G”) wireless technology and four generation (“4G”), greater bandwidth availability has enabled more portable computing devices with a greater variety of wireless capabilities. Therefore, a portable computing device may include a cellular telephone, a pager, a PDA, or wearable device, a Smartphone, a navigation device, or a hand-held computer with a wireless connection or link.

[0034] FIG. 1 illustrates a network environment of a system for estimating respiratory signals continuously from Photoplethysmography signals, according to one embodiment of the invention. Referring to FIG. 1, the network environment may include a wearable device (101), an electronic device (103), and a server (105). The network environment may be communicatively coupled via a network (107).

[0035] In an example embodiment, the wearable device (101) may be communicatively coupled to the electronic device (103), and the server (105). The wearable device (103) may be worn on the wrist of a user. The wearable device (101) may be used to acquire Photoplethysmography (PPG) signal data and accelerometer data.

[0036] In an example embodiment, the electronic device (103) is communicatively coupled to the server (107) and wearable device (101). The electronic device (103) may be used to receive Photoplethysmography (PPG) signal data along with accelerometer data from the wearable device (101) and communicate the data collected at the wearable device (101) to the server (105).

[0037] In an example embodiment, the server (105) may be communicatively coupled to the electronic device (103) and the wearable device (101). The server (105) may be used to receive the bio-information data from at least one of the wearable device (101) or the electronic device (103), normalize the received bio information, perform decomposition of the normalized bio information using a variational mode decomposition method (VMD), generate a periodogram on the decomposed 5-modes using Fast Fourier Transform to obtain a dominant frequency having a maximum power, and estimation of respiratory rate from the obtained dominant frequency.

[0038] The network (107) may include the Internet or any other network capable of communicating data between devices. Suitable networks may include or interface with any one or more of, for instance, a local intranet, a PAN (Personal Area Network), a LAN (Local Area Network), a WAN (Wide Area Network), a MAN (Metropolitan Area Network), a VPN (Virtual Private Network), a SAN (Storage Area Network), a frame relay connection, an AIN (Advanced Intelligent Network) connection, a SONET (Synchronous Optical Network) connection, a digital Tl, T3, El or E3 line, DDS (Digital Data Service) connection, DSL (Digital Subscriber Line) connection, an Ethernet connection, an ISDN (Integrated Services Digital Network) line, a dial-up port such as a V.90, V.34 or V.34bis analog modem connection, a cable modem, an ATM 13 (Asynchronous Transfer Mode) connection, or an FDDI (Fiber Distributed Data Interface) or CDDI (Copper Distributed Data Interface) connection. Furthermore, communications may also include links to any of a variety of wireless networks, including WAP (Wireless Application Protocol), GPRS (General Packet Radio Service), GSM (Global System for Mobile Communication), CDMA (Code Division Multiple Access) or TDMA (Time Division Multiple Access), cellular phone networks, GPS (Global Positioning System), CDPD (Cellular Digital Packet Data), RIM (Research in Motion, Limited) duplex paging network, Bluetooth radio, or an IEEE 802.11 -based radio frequency network. The network (107) can further include or interface with any one or more of an RS-232 serial connection, an IEEE-1394 (Firewire) connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI (Small Computer Systems Interface) connection, a USB (Universal Serial Bus) connection or other wired or wireless, digital or analog interface or connection, mesh or Digi® networking. [0039] FIG. 2 illustrates a block diagram of a system for estimating respiratory parameters continuously from Photoplethysmography signals, in accordance with an example embodiment of the present invention. To execute activities associated with the server for estimating respiratory rate, the server (105) has embedded a processor (201), a memory (203), and a communication interface (205).

[0040] In accordance with an embodiment, the processor (201) may be of any type of processor, such as 32-bit processors using a flat address space, such as a Hitachi SHI, an Intel 80386, an Intel 960, a Motorola 68020 (or other processors having similar or greater addressing space). Processor types other than these, as well as processors that may be developed in the future, are also suitable. The processor may include general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), AT89S52 microcontroller firmware or a combination thereof.

[0041] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor receives instructions and data from a read only memory or a random-access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer also includes, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio player, a GPS receiver, to name just a few. Computer readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The memory may be a non-transitory medium such as a ROM, RAM, flash memory, etc. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0042] The processes and logic flows described in the specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0043] In accordance with an embodiment, the memory (301) includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein.

[0044] Although the present specification describes components and functions that may be implemented in particular embodiments with reference to particular standards and protocols, the invention is not limited to such standards and protocols. For example, The ZigBee or ZigBee/IEEE 802.15.4 protocol is a specification created for wireless networking. It includes hardware and software standard design for WSN (Wireless sensor network) requiring high reliability, low cost, low power, scalability and low data rate. Accordingly, replacement standards and protocols having the same or similar functions as those disclosed herein are considered equivalents thereof. [0045] In an example embodiment, the communication interface (303) may include but not limited to traditional interfaces which include only physical connection which could include changes in voltage levels and transformation from balanced to unbalanced signal, communication protocols which may use pre-programmed modules etc. Further, the communication interface may include modem interfaces, which have a high level of intelligence in the interface where a high level of intelligence in the interface is employed to execute operations.

[0046] Further, execution of at least one activity is executed by the system comprising the embedded processor (201), memory (301) and the communication interface (303), based on control and configuration of components associated with the system.

[0047] FIG. 3 illustrates a flow diagram of estimating respiratory rate continuously from Photoplethysmography signals using variational mode decomposition method, according to one embodiment of the invention. Referring to FIG. 3, in an exemplary embodiment, at step 301, the Photoplethysmography (PPG) signal data along with accelerometer data is collected from a wearable device (101) worn on the wrist of a user. In some example embodiments, the electronic device (103) communicates the collected data to the server (105).

[0048] In an example embodiment, at step 303, the PPG data stream may be inputted by overlap-windowing technique (illustrated in FIG. 4) for at least 60 seconds i.e., as illustrated in FIG. 4, for initial Respiratory signal (RS) estimation, a 60 seconds (one-minute) data stream is required post which a one second input data stream is provided by the overlap-windowing technique. In the overlap-windowing technique, the previous 59 seconds input data stream along with the newly arrived 1- second data stream total 60- second data stream is taken as input for estimation is shown in FIG. 4.

[0049] In an example embodiment, at step 305, the input data stream of the PPG signal may be pre-processed by a method called normalization (as illustrated in FIG. 5). In normalization, the lowest value may be subtracted from the entire signal.

[0050] In an example embodiment, at step 307, denoising of the pre-processed PPG signal may be executed. In denoising, the induced noise due to motion artifacts and ambient light may be removed by Discrete Wavelet Transform based signal denoising technique (illustrated in FIG. 6). The signal denoising may include the steps of: a) The signal is decomposed to level- 1 by Daubechies mother wavelet in detailed coefficients corresponding to short- scale, high-frequency elements of the signal, and approximation coefficients corresponding to large-scale, low-frequency elements of the signal. b) A simple nonlinear technique, called thresholding, is applied on one wavelet coefficient at a time which is obtained in the decomposition step. Specifically, hard thresholding is used in which elements whose absolute value is less than a threshold value is set to zero. The threshold value is computed by the Mean Absolute Deviation method. c) The signal is reconstructed using Inverse Discrete Wavelet Transform.

[0051] In an example embodiment, at step 309, the denoised PPG signal may be further decomposed by the variational mode decomposition method. The Variational Mode Decomposition (VMD) is a fully intrinsic and non-recursive method to process the non-stationary signals. The Variational Mode Decomposition (VMD) may include three steps: i. Computation of one-sided frequency spectrum pertaining to the analytical representation of the input signal by means of the Hilbert transform. ii. Each mode is multiplied with an exponential function in order to shift its frequency spectrum to the baseband where the frequency of the exponential function is fixed according to the centre frequency of the mode. iii. The bandwidth of the mode is estimated using the Gaussian smoothness applied to the demodulated signal.

[0052] In an example embodiment, in the VMD method, initialization of the parameters is required for decomposing the input signal which includes the total number of modes (k), quadratic penalty factor (alpha), mode centre frequency, time-step of the dual ascent (tau), the tolerance defined for convergence (tol), and the DC part imposed (de). The initialization of these parameters depends on the application. For the initialization of parameters from VMD, first note its use to capture the centre frequency mode accurately. Large values in the VMD technique are not appropriate, on the contrary small values will cause a trade-off of extracted modes for noise.

[0053] In an example embodiment, the pre-processed PPG signal may be decomposed into five sub-signals also known as 5-modes (illustrated in FIG.7). The value of input parameters may be set as follows: alpha=2000, tau=0, tol=10^A-7, and dc=0. The 5-modes represent the cardiac component and respiratory component of the PPG Signal.

[0054] The result of step 309, the decomposed VMD Mode 1 signal represents the respiratory component of PPG signal. This mode 1 signal can be utilized to calculate various parameters including tidal volume, lung capacity, lung volume and respiratory rate. [0055] The system extracts respiratory rate, tidal volume and lung capacity using the respiratory signal from the PPG signal, regardless of sampling frequency of the PPG signal.

[0056] In an example embodiment, at step 311, the Periodogram using Fast Fourier Transform may be applied on the respiratory component of the signal (eg. mode 1) to obtain the dominant frequency having maximum power (illustrated in FIG.8).

[0057] In an example embodiment, at step 313, a mode that has the dominant frequency in the range of 0.06 - 1Hz may be selected as a respiratory component for estimation of respiratory rate (illustrated in FIG.9). The respiratory rate may be estimated in breaths per minute by multiplying the dominant frequency of the respiratory component with 60.

[0058] In an example embodiment, the proposed respiratory rate estimation method may be validated through experiments performed on the recordings of two publicly available databases MIMIC and Capnobase databases. The clinical data of these databases may be obtained from different patients having dissimilar health problems. The PPG signal waveforms may be acquired at a sampling rate of 125Hz in the MIMIC database and at the sampling rate of 300Hz in the Capnobase database. The proposed algorithm has a device-agnostic design as it has the capability to work with various systems without requiring any special changes. For example, the information processing apparatus may receive a plurality of different signals at different sampling frequencies from the plurality of devices and determine accurate RR regardless of the sampling rate for PPG in the plurality of devices. [0059] In an example embodiment, the performance of the estimated respiratory signal may be analysed by comparing it with the actual respiratory rate. The performance is evaluated by Mean Absolute Error (MAE). The MAE for the MIMIC database is 1.34 breaths per minute (bpm) and the MAE for the Capnobase database is 1.39 bpm. This is clinically grade accurate and falls under the medical device regulatory bodies approved guidelines for RR measurement.

[0001] In some example embodiments, MAE may be calculated as:

n

Where {MAE} = Mean Absolute Error yt = prediction

Xi = true value n = total number of data points

Advantageous effect of the invention:

[0067] The other decomposition technique such as Empirical Mode Decomposition (EMD) suffers from the problem of mode-mixing which generates severe aliasing in the time-frequency distribution making the physical significance of intrinsic mode functions (IMFs) imprecise.

[0068] The Ensemble Empirical Mode Decomposition (EEMD) accepts the residual noise in input signal reproduction.

[0069] Most of the decomposition methods are limited by their algorithmic ad- hoc nature missing mathematical theory (EMD). (1) Recursive shifting in most approaches that prevent backward error correction; (2) The failure to properly handle noise; (3) The hard band restrictions (the wavelet techniques); (4) The selection of predefined filter bank boundaries (the empirical wavelet transform)..

[0070] In this context, the VMD method computes the related bands adaptively, and simultaneously estimates the corresponding modes, and thus properly balances the error between them. Specifically, the variational model can address the presence of noise in the input signal efficiently. In VMD, each mode is directly updated in the Fourier domain which makes the constraint optimization problem very simple and fast.

[0071] Thus, the method of continuous estimation of respiratory rate can be applied to any pulse oximetry system which will allow for low-cost measurement of respiratory parameters in a small form factor.

[0072] The information processing apparatus for continuous estimation of respiratory parameters could help in reducing healthcare expenditures on separate respiratory monitoring machines and could also easily allow for remote outpatient monitoring of respiratory health.

[0073] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases, it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.

[0074] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.

[0075] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particulars claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should typically be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, typically means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B and C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B and C” would include but not be limited to systems that have 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, etc.). In those instances, where a convention analogous to “at least one of A, B, or C, etc.” is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have 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, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B”.

[0076] The above description does not provide specific details of manufacture or design of the various components. Those of skill in the art are familiar with such details, and unless departures from those techniques are set out, techniques, known, related art or later developed designs and materials should be employed. Those in the art are capable of choosing suitable manufacturing and design details.

[0077] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. It will be appreciated that several of the above-disclosed and other features and functions, or alternatives thereof, may be combined into other systems or applications. Various presently unforeseen or unanticipated alternatives, modifications, variations, or improvements therein may subsequently be made by those skilled in the art without departing from the scope of the present disclosure as encompassed by the following claims.

[0078] The claims, as originally presented and as they may be amended, encompass variations, alternatives, modifications, improvements, equivalents, and substantial equivalents of the embodiments and teachings disclosed herein, including those that are presently unforeseen or unappreciated, and that, for example, may arise from applicants/patentees and others.

[0079] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

LAIM:

1. An information processing apparatus for continuous estimation of respiratory signal and respiratory parameters, comprising: a wearable device (101) configured to acquire bio-information data, wherein the bio-information data includes Photoplethysmography (PPG) signal data and accelerometer data; an electronic device (103) configured to receive the bio-information data; a server communicatively coupled with the wearable device (101) and the electronic device (103), wherein the server is configured to: receive the bio -information data from at least one of the wearable device or the electronic device; normalize the received bio-information; decompose the normalized bio-information based on a variational mode decomposition method (VMD), wherein the normalized bio-information is decomposed into a plurality of modes, wherein a Mode 1 of a plurality of modes of the VMD PPG is considered to be respiratory signal; periodogram on the decomposed normalized bio-information in each of the plurality of modes, wherein the periodogram is based on a Fast Fourier Transform (FFT) to obtain a dominant frequency having a maximum power; and estimate respiratory rate based on the obtained dominant frequency, wherein the respiratory rate is estimated, in breaths per minute, by multiplying the dominant frequency of the respiratory component with 60.

2. The information processing apparatus (100) as claimed in claim 1, wherein the VMD method includes: computation of one-sided frequency spectrum pertaining to the analytical representation of the input signal based on the Hilbert transform; multiplication of each mode of the plurality of modes with an exponential function in order to shift its frequency spectrum to the baseband where the frequency of an exponential function is fixed according to the centre frequency of the mode; and estimation of bandwidth of each of the plurality of modes based on the Gaussian smoothness applied to the demodulated signal. The information processing apparatus (100) as claimed in claim 2, wherein initialization of parameters for decomposing the input signal includes at least one of a total number of modes (k), a quadratic penalty factor (alpha), a mode centre frequency, a time-step of the dual ascent (tau), a tolerance defined for convergence (tol), and the DC part imposed (de); the pre-processed PPG signal is decomposed into five sub-signals; the 5-modes represent the cardiac component and respiratory component of the PPG signal; the value of input parameters is set as alpha=2000, tau=0, tol=10^A-7, and dc=0. The information processing apparatus (100) as claimed in claim 1, wherein the plurality of modes represents the cardiac component and respiratory component. The information processing apparatus (100) as claimed in claim 4, wherein a mode of the plurality of modes that has the dominant frequency in the range of 0.06 - 1Hz is selected as a respiratory component of the PPG signal for estimation of respiratory rate. The information processing apparatus (100) as claimed in claim 1, wherein in the normalization, the lowest value is subtracted from the entire signal. The information processing apparatus (100) as claimed in claim 1, wherein the denoising of normalized bio information data includes: decomposition of the acquired bio -information data to level- 1 by Daubechies mother wavelet in detailed coefficients corresponding to short-scale, high- frequency elements of the signal, and approximation coefficients corresponding to large-scale, low-frequency elements of the signal; thresholding on one wavelet coefficient at a time which is obtained in the decomposition step; and reconstructing the bio -information data using Inverse Discrete Wavelet transform. The information processing apparatus (100) as claimed in claim 1, wherein the bioinformation is acquired based on overlap -windowing technique. A system, comprising: a wearable device (101) configured to acquire bio-information data, wherein the bio-information data includes Photoplethysmography (PPG) signal data and accelerometer data, wherein at least one processor of a wearable device configured to: decompose the normalized bio-information based on a variational mode decomposition method (VMD), wherein the normalized bio-information is decomposed into a plurality of modes, wherein a Mode 1 of a plurality of modes of the VMD PPGVDM PPG is considered to be respiratory signal, wherein the VMD method includes: computation of one-sided frequency spectrum pertaining to the analytical representation of the input signal based on the Hilbert transform; multiplication of each mode of the plurality of modes with an exponential function in order to shift its frequency spectrum to the baseband where the frequency of an exponential function is fixed according to the centre frequency of the mode; estimation of bandwidth of each of the plurality of modes based on the Gaussian smoothness applied to the demodulated signal; and periodogram on the decomposed normalized bio -information in each of the plurality of modes, wherein the periodogram is based on a Fast Fourier Transform (FFT) to obtain a dominant frequency having a maximum power. An information processing method for continuous estimation of respiratory signal and respiratory parameters, the method comprising: receiving the bio-information data from at least one of the wearable device or the electronic device; normalizing the received bio information; decomposing the normalized bio -information based on a variational mode decomposition method (VMD), wherein the normalized bio-information is decomposed into a plurality of modes; periodograming on the decomposed normalized bio -information in each of the plurality of modes, wherein the periodogram is based on a Fast Fourier Transform (FFT) to obtain a dominant frequency having a maximum power; and estimating respiratory rate based on the obtained dominant frequency. 1