Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/02405—Determining heart rate variability
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
  • A61B5/7235—Details of waveform analysis
  • A61B5/7246—Details of waveform analysis using correlation, e.g. template matching or determination of similarity
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
  • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
  • A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/486—Bio-feedback

Definitions

• This invention relates to a method and device for quantifying heart rate variability coherence, more particularly but not exclusively, for a human subject.
• a healthy heart has a natural beat-to-beat variation in rate, known as Heart Rate Variability (HRV). Patterns and rhythms within this variability are important to health and well-being. Research shows that when you shift into a different emotional state, heart rhythms immediately change. Negative emotions such as anxiety and frustration show a disordered and chaotic variation. Positive emotions like tranquility shows an ordered rhythm synchronized with breathing.
• the beat to beat variation is under the direct control of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS).
• SNS sympathetic nervous system
• PNS parasympathetic nervous system
• the autonomic nervous system (ANS) comprises of both SNS and PNS, from which the system's response impacts our daily activities (e.g. your mood, your sense of touch).
• HRV Heart Rate Variability
• step (ii) may comprise obtaining an intermediate time-domain heart rate variability signal from the bio-signal; averaging the intermediate time-domain heart rate variability signal to obtain an average heart rate variability signal; and deriving the time-domain heart rate variability signal from the intermediate time-domain heart rate variability signal and the average heart rate variability signal.
• the time-domain heart rate variability signal may be derived by subtracting the intermediate time-domain heart rate variability signal by the average heart rate variability signal.
• averaging the intermediate time-domain heart rate variability may be performed over the HRV's entire time window.
• the method may further comprise segmenting the HRV's time window into a plurality of intermediate time windows with each intermediate time window having a corresponding segmented HRV signal, and averaging the corresponding segmented HRV signals to obtain the average HRV signal.
• the method may also further comprise deriving a strongest cross correlation from the correlated heart rate variability signal and a frequency corresponding to the strongest cross correlation.
• the method may further comprise calculating standard deviations of peak-to-peak of the bio-signal.
• quantifying the heart rate variability coherence may include calculating a wellness index based on the frequency corresponding to the strongest cross correlation, the percentage of the strongest cross-correlation and the standard deviation of the peak-to-peak of the bio-signal.
• step (v) may include obtaining cross correlation coefficients r xy based on the formula:
• x(i) is time series of a reference heart rate variability signal (sine wave);
• y(i) is time series of a heart rate variability signal obtained from a subject; and y is mean of the corresponding y(i) series;
• r xy is the cross-correlation coefficient of x(i) and y(i) series.
• the bio-signal from the subject may include a PPG signal or an ECG signal.
• the method may also include increasing or reducing the frequency of the sine wave by a predetermined interval.
• the predetermined interval may be 0.005 Hz, or other suitable intervals.
• a device for quantifying heart rate variability coherence of a subject comprising a processor configured to (i) obtain a bio-signal from the subject; (ii) derive a time-domain heart rate variability signal from the bio-signal; (iii) correlate the time-domain heart rate variability signal with a sine wave representing a time domain reference heart rate variability signal to obtain a correlated heart rate variability signal; and (iv) quantify the heart rate variability coherence based on the correlated heart rate variability signal; wherein the processor is further configured to (iv) adjust frequency of the sine wave; (v) perform cross-correlation between the sine wave at each of the adjusted frequencies and the heart rate variability signal to obtain the correlated heart rate variability signal. It is envisaged that features related to one aspect may be relevant to the other aspect(s).
• Figure 1 is a flow chart illustrating a method of qualifying heart rate variability coherence according to a preferred embodiment of the invention
• Figure 2 is a pictorial representation of a cross correlation step using frequency scanning method used in the method of Figure 1 ;
• Figure 3 is a graph illustrating results of the cross correlation of Figure 2;
• Figure 4a illustrates a time window of a heart rate variability signal of the flow chart of Figure 1 ;
• Figure 5a illustrates a HRV signal for "Subject 2" which has a fluctuating baseline
• Figure 5b illustrates a HRV signal for "Subject 5" which has a relatively constant baseline
• Figure 6 is a graph illustrating results of adjusting the baseline of a HRV signal based on segmenting the time window of Figures 4b and 4c;
• Figures 7a, 7b and 7c are reference tables for deriving a Zen index based on the cross correlation results of Figure 3, and other parameters;
• Figure 8 shows how the Zen index may be used in combination with another index (Vita index for fitness) to represent overall well-being of the subject.
• SNS and PNS The two branches of the ANS (SNS and PNS) usually function in tandem with each other (i.e. when one is activated, the other is suppressed). Division of SNS results in stress arousal (i.e. both positive and negative). During SNS arousal, increased heart rate and respiration, cold and pale skin, dilated pupils, raised blood pressure are expected symptoms. Division of PNS results in states of rest and relaxation. During PNS arousal, decreased heart rate and respiration, warm and flushed skin, normally reactive pupils, lowered blood pressure are expected symptoms.
• Figure 1 is a flow chart illustrating a method 100 of qualifying HRV coherence, according to a preferred embodiment.
• a bio-signal is obtained from a human subject, broadly referred to as a user of the method.
• the user places his fingertip on a measurement device such as a combination of a mobile telephone and a measurement unit as disclosed in WO 2012/099534 (PCT/SG201 1/000424), the contents of which are incorporated herein by reference, to obtain a PPG signal as the bio-signal.
• the measurement device includes a band pass filter to filter the obtained PPG signal at 104 to produce a filtered PPG signal.
• the measurement device also includes a peak detector and at 106, the peak detector detects peaks of the filtered PPG signal to produce a series of peak positions of the PPG signal and time indications corresponding to the series of peak positions.
• a processor of the measurement device derives heart rate variability (HRV) of the user from the series of peak positions and time indications at step 106 and this is illustrated as a HRV signal 1000 in Figure 2. Further, standard deviation of the series of peak positions (i.e. peak-to-peak, SDPP) is also derived and this is shown in step 1 10.
• HRV heart rate variability
• average of the HRV signal is also obtained by averaging total data points of the HRV signal.
• baseline of the HRV signal is adjusted to ground state by subtracting the HRV signal by the average HRV signal to produce a normalised HRV signal. This is to improve the accuracy of correlation which is the next step.
• cross correlation is performed between the normalised HRV signal and a sine wave 200 by frequency scanning method. This involves varying the frequency of the sine wave 200 from 0.05 Hz to 0.4 Hz with each increment of 0.005 Hz and at each interval, cross correlation with the normalised HRV signal is performed.
• the formula for deriving the cross-correlation coefficients of the x and y series is as follows:

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• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• Cardiology (AREA)
• Engineering & Computer Science (AREA)
• Medical Informatics (AREA)
• Animal Behavior & Ethology (AREA)
• Pathology (AREA)
• Physics & Mathematics (AREA)
• Biomedical Technology (AREA)
• Heart & Thoracic Surgery (AREA)
• Physiology (AREA)
• Molecular Biology (AREA)
• Surgery (AREA)
• Biophysics (AREA)
• General Health & Medical Sciences (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• Artificial Intelligence (AREA)
• Computer Vision & Pattern Recognition (AREA)
• Psychiatry (AREA)
• Signal Processing (AREA)
• Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)

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• 2013
  • 2013-08-22 US US14/423,351 patent/US20150208931A1/en not_active Abandoned
  • 2013-08-22 JP JP2015528443A patent/JP2015529513A/ja active Pending
  • 2013-08-22 AU AU2013306440A patent/AU2013306440A1/en not_active Abandoned
  • 2013-08-22 SG SG11201501208QA patent/SG11201501208QA/en unknown
  • 2013-08-22 EP EP13831347.3A patent/EP2887864A4/en not_active Withdrawn
  • 2013-08-22 WO PCT/SG2013/000362 patent/WO2014031082A1/en active Application Filing

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