Method and a system for determining quality of photoplethysmogram (PPG) signal

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive a plurality of raw input signals from a device, via one or more hardware processors, wherein the plurality of raw input signals comprises a plurality of data sets associated with a PPG signal of a plurality of subjects, wherein the device comprises a wearable, a plurality of medical diagnostic tools of an Intensive Care Unit (ICU), and a plurality of medical grade devices, wherein the plurality of raw input signals is collected from ICU patients using the medical grade device at a sampling rate of 125 Hz or by using a pulse oximeter with a sampling rate of 60 Hz or by using a wrist sensor at 64 Hz or dataset at the ICU from an adult population at a sampling rate of 300 Hz;
• obtain annotations of the received raw input signals of PPG segments from varied datasets in an annotator tool and the annotations correspond to marking windows of a clean PPG segment in the PPG segments considered as a ground truth;
• filter the plurality of raw input signals to obtain a plurality of filtered signals, via the one or more hardware processors, wherein the plurality of raw input signals is filtered with a low-pass Chebyshev filter at 3 Hz cut-off frequency;
• extract a plurality of heart frequency components and a plurality of breathing frequency components, via the one or more hardware processors, from the plurality of raw input signals and the plurality of filtered signals based on an extraction technique, wherein fs is a sampling rate of the plurality of raw input signals, and frequency (f) and a power (P) are frequency-power outputs from Fast Fourier Transform (FFT), respectively, wherein the plurality of heart frequency components is in the range of 0.61-2 Hz, and the plurality of breathing frequency components is in the range of 0.2-0.6 Hz;
• determine a plurality of metrices, via the one or more hardware processors, using the plurality of heart frequency components and the plurality of breathing frequency components for a pre-defined time interval, wherein the one or more hardware processors are configured by the instructions to determine the plurality of metrices, wherein the plurality of metric includes a mean heart component (Hm), a mean breathing component (Bm), a change in the heart component (P1), and a change in the breathing component (P2) wherein:
• (a) the Hm and the Bm is an average value of the plurality of heart frequency component and a plurality of breathing frequency component for the raw input signals and the plurality of filtered signals, and;
• (b) the P1 and the P2 is the percentage change in the plurality of heart frequency component and a plurality of breathing frequency component for the raw input signals and the plurality of filtered signals, wherein the percentage change in the heart component (P1) and the percentage change in the breathing component (P2) is expressed as:
• P
• 1
• =
• 1
• ⁢
• 0
• ⁢
• 0
• *
• ❘
• "\[LeftBracketingBar]"
• H
• raw
• -
• H
• filtered
• ❘
• "\[RightBracketingBar]"
• H
• raw
• ;
• P
• 2
• =
• 1
• ⁢
• 0
• ⁢
• 0
• *
• ❘
• "\[LeftBracketingBar]"
• P
• raw
• -
• P
• filtered
• ❘
• "\[RightBracketingBar]"
• P
• raw
• ;
• wherein the plurality of metrices Hm, Bm, P1 and P2 are represented as Xi,j (j=1;
• 2;
• 3;
• 4 metrics and i=1;
• 2;
• 3;
• :::N number of instances), for each instance Xi of PPG, 4 metrics of Hm, Bm, P1 and P2 are extracted;
• classify the plurality of metrics Hm, Bm, P1 and P2 against thresholds into a good PPG segment and a bad PPG segment;
• estimate, during a training phase, a plurality of optimum threshold, via the one or more hardware processors, using the plurality of metrices Hm, Bm, P1 and P2 based on a differential evolutionary optimization;
• determine the quality of the plurality of input signals at real time during a testing phase, wherein the quality of the plurality of input signals is determined as one of bad signal and a good signal based on a PPG signal classification technique using the plurality of optimum thresholds, wherein the device is a PPG quality checker deployed in an edge computing scenario to check the quality of the signal that is directly implemented on an edge device, wherein the PPG signal classification technique during the testing phase the plurality of optimum thresholds are used for determining the quality of the plurality of input signals at real time comprises:
• receiving the plurality of input signals from the device, wherein the plurality of input signals comprises a plurality of input datasets associated with the Photoplethysmogram (PPG) signal of a user;
• filtering the plurality of input signals to obtain a plurality of filtered input signals, wherein the plurality of input signals is filtered based on the filtering technique;
• extracting a plurality of real-time heart frequency components and a plurality of real-time breathing frequency components, from the plurality of input signals and plurality of filtered input signals based on the extraction technique;
• determining a real-time metric for the plurality of input signals, using the plurality of real-time heart frequency components and the plurality of real-time breathing frequency components for the pre-defined time interval;
• and
• determining the quality of the plurality of input signals at real time, wherein the quality of the plurality of input PPG signals is determined as one of bad signal and a good signal based on the plurality of optimum thresholds using the plurality of real-time metric, wherein classifying the input PPG signal into good and bad PPG signal is given by:
• S
• i
• =
• ∏
• ω
• j
• =
• 1
• 4
• ⁢
• ∅
• ⁡
• (
• X
• i
• ,
• j
• ,
• ω
• j
• )
• ,
• where, ∀=1,2,3,4 . . . N number of test samples and the Si is 1 for correctly classified samples, and 0 otherwise and wherein the Si is used to compute the pre-defined performance parameter F-Score using the differential evolutionary optimization technique and wherein ∅ is defined over testing instance metrics Xi and the optimal thresholds (ω), such that, ∅ (X(i,j), ωj), (∀=1,2,3,4 features in Xi) returns 1 when the given parameter Xj is below the threshold ωi;
• and 0 otherwise
• collect PPG signals by a mobile PPG application and simultaneous ground truth (GT) PPG by a PPG sensor;
• compute Heart Rate (HR) values with the GT PPG and corresponding HR values with the PPG signals collected from the mobile PPG application;
• select only good PPG signal classified by the PPG quality checker device, implemented directly on wearables or medical devices;
• and
• HR values by selecting only the good PPG signal and suppressing erroneous HR features compute;
• and
• control the HR values computation, displaying the computed HR values only for windows classified as the good PPG signal, and suppressing computation and output for the bad PPG signal, thereby reducing erroneous HR features during motion artifacts.

A computer program product comprising a non-transitory computer readable medium having a computer readable program embodied therein, wherein the computer readable program, when executed on a computing device, causes the computing device to:

• receive a plurality of raw input signals from a device, via one or more hardware processors, wherein the plurality of raw input signals comprises a plurality of data sets associated with a PPG signal of a plurality of one or more subjects, wherein the device comprises a wearable, a plurality of medical diagnostic tools of an Intensive Care Unit (ICU), and a plurality of medical grade devices, wherein the plurality of raw input signals is collected from ICU patients using the medical grade device at a sampling rate of 125 Hz or by using a pulse oximeter with a sampling rate of 60 Hz or by using a wrist sensor at 64 Hz or dataset at the ICU from an adult population at a sampling rate of 300 Hz;
• obtaining annotations of the received raw input signals of PPG segments from varied datasets in an annotator tool and the annotations correspond to marking windows of a clean PPG segment in the PPG segments considered as a ground truth;
• filtering the plurality of raw input signals to obtain a plurality of filtered signals, via the one or more hardware processors, wherein the plurality of raw input signals is with a low-pass Chebyshev filter at 3 Hz cut-off frequency;
• extracting a plurality of heart frequency components and a plurality of breathing frequency components, via the one or more hardware processors, from the plurality of raw input signals and the plurality of filtered signals, based on an extraction technique, wherein fs is a sampling rate of the plurality of raw input signals, and frequency (f) and a power (P) are frequency-power outputs from Fast Fourier Transform (FFT), respectively, wherein the plurality of heart frequency components is in the range of 0.61-2 Hz, and the plurality of breathing frequency components is in the range of 0.2-0.6 Hz;
• determining a plurality of metrices, via the one or more hardware processors, using the plurality of heart frequency components and the plurality of breathing frequency components for a pre-defined time interval, wherein the plurality of metrices includes a mean heart component (Hm), a mean breathing component (Bm), a change in the heart component (P1), and a change in the breathing component (P2) wherein,
• (a) the Hm and the Bm is an average value of the plurality of heart frequency component and a plurality of breathing frequency component for the raw input signals and the plurality of filtered signals, and,
• (b) the P1 and the P2 is the percentage change in the plurality of heart frequency component and a plurality of breathing frequency component for the raw input signals and the plurality of filtered signals, wherein the percentage change in the heart component (P1) and the percentage change in the breathing component (P2) is expressed as:
• P
• 1
• =
• 1
• ⁢
• 0
• ⁢
• 0
• *
• ❘
• "\[LeftBracketingBar]"
• H
• raw
• -
• H
• filtered
• ❘
• "\[RightBracketingBar]"
• H
• raw
• ;
• P
• 2
• =
• 1
• ⁢
• 0
• ⁢
• 0
• *
• ❘
• "\[LeftBracketingBar]"
• P
• raw
• -
• P
• filtered
• ❘
• "\[RightBracketingBar]"
• P
• raw
• ;
• wherein the plurality of metrices Hm, Bm, P1 and P2 are represented as Xi,j (j=1;
• 2;
• 3;
• 4 metrics and i=1;
• 2;
• 3;
• :::N number of instances), for each instance Xi of PPG, 4 metrics of Hm, Bm, P1 and P2 are extracted;
• classifying, via the one or more hardware processors, the plurality of metrics Hm, Bm, P1 and P2 against thresholds into a good PPG segment and a bad PPG segment;
• estimating, during a training phase, a plurality of optimum thresholds, via the one or more hardware processors, using the plurality of metrices Hm, Bm, P1 and P2 based on a differential evolutionary optimization technique;
• determining quality of a plurality of input signals at real time via one or more hardware processors, wherein the quality of the plurality of input signals is determined as one of bad signal and a good signal based on a PPG signal classification technique using the plurality of optimum thresholds, wherein the device is a PPG quality checker deployed in an edge computing scenario to check the quality of the input signal that is directly implemented on an edge device wherein the PPG signal classification technique during the testing phase the plurality of optimum thresholds are used for determining the quality of the plurality of input signals at real time comprises:
• receiving the plurality of input signals from the device, wherein the plurality of input signals comprises a plurality of input datasets associated with the Photoplethysmogram (PPG) signal of a user;
• filtering the plurality of input signals to obtain a plurality of filtered input signals, wherein the plurality of input signals is filtered based on the filtering technique;
• extracting a plurality of real-time heart frequency components and a plurality of real-time breathing frequency components, from the plurality of input signals and plurality of filtered input signals based on the extraction technique;
• determining a real-time metric for the plurality of input signals, using the plurality of real-time heart frequency components and the plurality of real-time breathing frequency components for the pre-defined time interval;
• and
• determining the quality of the plurality of input signals at real time during a testing phase, wherein the quality of the plurality of input PPG signals is determined as one of bad signal and a good signal based on the plurality of optimum thresholds using the plurality of real-time metric, wherein classifying the input PPG signal into good and bad PPG signal is given by:
• S
• i
• =
• ∏
• ω
• j
• =
• 1
• 4
• ⁢
• ∅
• ⁡
• (
• X
• i
• ,
• j
• ,
• ω
• j
• )
• ,
• where, ∀=1,2,3,4 . . . N number of test samples and the Si is 1 for correctly classified samples, and 0 otherwise and wherein the Si is used to compute the pre-defined performance parameter F-Score using the differential evolutionary optimization technique and wherein ∅ is defined over testing instance metrics Xi and the optimal thresholds (ω), such that, ∅ (X(i,j), ωj), (∀=1,2,3,4 features in Xi) returns 1 when the given parameter Xj is below the threshold ωi;
• and 0 otherwise,
• collecting PPG signals by a mobile PPG application and simultaneous ground truth (GT) PPG by a PPG sensor;
• computing Heart Rate (HR) values with the GT PPG and corresponding HR values with the PPG signals collected from the mobile PPG application;
• and
• selecting only good PPG signal classified by the PPG quality checker device, implemented directly on wearables or medical devices
• computing HR values by selecting only the good PPG signal and suppressing erroneous HR features;
• and
• controlling the HR values computation, displaying the computed HR values only for windows classified as the good PPG signal, and suppressing computation and output for the bad PPG signal, thereby reducing erroneous HR features during motion artifacts.