WO2011110491A1 - Système non invasif et méthode pour diagnostiquer et éliminer l'hypertension de la blouse blanche et l'effet blouse blanche chez un patient - Google Patents

Système non invasif et méthode pour diagnostiquer et éliminer l'hypertension de la blouse blanche et l'effet blouse blanche chez un patient Download PDF

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WO2011110491A1
WO2011110491A1 PCT/EP2011/053294 EP2011053294W WO2011110491A1 WO 2011110491 A1 WO2011110491 A1 WO 2011110491A1 EP 2011053294 W EP2011053294 W EP 2011053294W WO 2011110491 A1 WO2011110491 A1 WO 2011110491A1
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Prior art keywords
blood pressure
patient
wch
wce
settings
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PCT/EP2011/053294
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English (en)
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Vicente Jorge Ribas Ripoll
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Sabirmedical, S.L.
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    • 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
    • A61B5/021—Measuring pressure in heart or blood vessels
    • 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
    • A61B5/026—Measuring blood flow
    • A61B5/0295—Measuring blood flow using plethysmography, i.e. measuring the variations in the volume of a body part as modified by the circulation of blood therethrough, e.g. impedance plethysmography
    • 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
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7296—Specific aspects of physiological measurement analysis for compensation of signal variation due to stress unintentionally induced in the patient, e.g. due to the stress of the medical environment or examination

Definitions

  • the present invention generally relates to a non-invasive system, and more specifically pertains to a system and a method for monitoring of a patient's blood pressure.
  • Cardiovascular function is particularly valuable and is performed on a very widespread basis. Accurate measurement of blood pressure and other physiological signals allow for careful diagnosis of medical problems. Monitoring cardiovascular functions, such as blood pressure, can allow a physician to diagnose conditions such as hypertension (increased blood pressure) which may result from processes such as aging or disease.
  • the heart functions as a pump which moves blood through the circulatory system by a regulated sequence of contractions.
  • the heart ejects blood into the aorta.
  • the blood then flows through the arteries, arterioles, and capillaries to the tissues where the blood delivers oxygen and other nutrients and removes carbon dioxide and other waste products from the tissues.
  • the blood returns to the heart and the lungs where carbon dioxide is expelled from the body and oxygen is again transported into the body.
  • the human body regulates blood pressure throughout the circulatory system to facilitate efficient delivery of blood to the tissues.
  • Blood pressure is the pressure exerted by circulating blood on the walls of blood vessels, and is one of the principal vital signs.
  • BP varies between a maximum (systolic) and a minimum (diastolic) pressure.
  • the mean BP decreases as the circulating blood moves away from the heart through arteries, has its greatest decrease in the small arteries and arterioles, and continues to decrease as the blood moves through the capillaries and back to the heart through veins.
  • the blood pressure ranges from a systolic blood pressure (SBP) 120 mmHg and a diastolic pressure (DBP) 80 mmHg.
  • SBP systolic blood pressure
  • DBP diastolic pressure
  • Figure 1 shows a typical record of the pulsations of pressure taken from by an invasive catheterization in the root of the aorta.
  • the normal systolic blood pressure (SBP) of a young adult is approximately 120 mm Hg while the diastolic blood pressure (DBP) is approximately 80 mmHg.
  • the difference between the two pressures is called the pulse pressure (PP) and under normal conditions is approximately 40 mmHg.
  • the auscultatory method uses a stethoscope and a sphygmomanometer. This comprises an inflatable cuff placed around the upper arm at roughly the same vertical height as the heart, attached to a mercury or aneroid manometer.
  • the mercury manometer measures the height of a column of mercury, giving an absolute result without need for calibration, and consequently not subject to the errors and drift of calibration which affect other methods.
  • the use of mercury manometers is often required in clinical trials and for the clinical measurement of hypertension in high risk patients.
  • a cuff of appropriate size is fitted smoothly and snugly, and then inflated manually by repeatedly squeezing a rubber bulb until the artery is completely occluded.
  • the examiner listening with the stethoscope to the brachial artery at the elbow, the examiner slowly releases the pressure in the cuff.
  • the turbulent flow creates a "whooshing" or pounding (first Korotkoff sound).
  • the pressure at which this sound is first heard is the systolic BP.
  • the cuff pressure is further released until no sound can be heard (fifth Korotkoff sound), at the diastolic arterial pressure.
  • the auscultatory method has been predominant since the beginning of BP measurements but is in some cases is being replaced by other noninvasive techniques.
  • the Oscillometry method was first demonstrated in 1876 and involves the observation of oscillations in the sphygmomanometer cuff pressure which are caused by the oscillations of blood flow, i.e. the pulse.
  • the electronic version of this method is sometimes used in long-term measurements and general practice. It uses a sphygmomanometer cuff like the auscultatory method, but with an electronic pressure sensor (transducer) to observe cuff pressure oscillations, electronics to automatically interpret them, and automatic inflation and deflation of the cuff.
  • the pressure sensor should be calibrated periodically to maintain accuracy.
  • the Oscillometric measurement requires less skill than the auscultatory technique, and may be suitable for use by untrained staff and for automated patient home monitoring.
  • the cuff is inflated to a pressure initially in excess of the systolic arterial pressure, and then reduces to below diastolic pressure over a period of about 30 seconds.
  • cuff pressure will be essentially constant. It is essential that the cuff size is correct: undersized cuffs may yield too high a pressure, whereas oversized cuffs yield too low a pressure.
  • the cuff pressure which is monitored by the pressure sensor, will vary periodically in synchrony with the cyclic expansion and contraction of the brachial artery, i.e., it will oscillate.
  • the values of systolic and diastolic pressure are computed, not actually measured from the raw data, using an algorithm; the computed results are displayed.
  • a photoplethysmograph is an optically obtained plethysmograph.
  • the photoplethysmograph is a tool that uses an emitter-receiver pair to determine blood flow.
  • a light emitting diode is used to transmit light through the skin.
  • the receiver picks up the transmitted signal, which is then analyzed with signal processing techniques.
  • the pulse wave is produced by the changes in blood volume in the arteries and capillaries. Changes in blood volume produce changes in the optical absorption of the transmitted signal.
  • the light transmitted through the tissue can be highly scattered or absorbed depending on the tissue.
  • the detector which is positioned on the surface of the skin, can detect the reflection or transmission of waves from various depths and from highly absorbing or weakly absorbing tissues. Regardless of the absorbency of the tissues and skin, it is assumed that the amount of light absorbed and/or reflected by these tissues will remain constant. With this assumption in mind, it can then be assumed that the only change in the absorption or reflection of the transmitted light will be from the increase or decrease of the blood volume in the arteries and capillaries. The measured volume change is actually an average of all of the arteries and capillaries in the space being measured. The signal that is received is dependent on the tissue type, skin type, position of the receiver and transmitter, blood volume content of the arteries and capillaries, and the properties of the sensor and receiver.
  • the output is proportional to blood flow.
  • the PPG can be regarded as a low cost technique for measuring changes in blood volume at the micro vascular (usually a finger or the lobe of the ear) applied in a non-invasive manner to the skin of a subject.
  • US patent 5,237,997 discloses a method for continuous measurement of mean arterial pressure (MAP) from the transit time of pulses in a PPG signal received from the ear lobe.
  • MAP mean arterial pressure
  • SBP and DBP are also derived from measuring the blood volume density in the ear lobe.
  • This invention requires a calibration of the values of the blood pressure by conventional methods (e.g., Oscillometric, Korotkoff).
  • US patent 5,865,755 and US patent 5,857,975 describe a method for the determination of the SBP and the DBP from ECG and PPG signals.
  • the blood pressure is calculated from the arrival times of pulses, the waveform volume and the heart rate for each pulse.
  • These patents use the time difference between the R wave of the ECG and the beginning of the PPG pulse together for the determination of the blood pressure.
  • US Patent 2006/0074322 discloses a system for measuring blood pressure without a cuff based on the principle of photoplethysmograph (PPG). Although this patent discloses a system for measuring blood pressure without a cuff, it requires calibration for each user based on the principles oscillometric and Korotkoff.
  • PPG photoplethysmograph
  • the present invention discloses a system for continuous non-invasive monitoring of blood pressure, which does not require calibration by an additional system like a sphygmomanometer.
  • WCH white coat hypertension
  • WCH should not be confused with 'white coat effect' (WCE), which represents an increase in blood pressure during the clinic visit compared to with the mean daytime blood pressure and occurs in patients with sustained or normalized hypertension, treated or untreated. Therefore, the WCE is a measure of blood pressure change, whereas WCH is a measure of blood pressure level.
  • WCE 'white coat effect'
  • Blood pressure does not remain constant over time. Not only does BP fluctuate during the pumping cycle of the heart, but it is also influenced by a wide range of factors. These factors include activity level, temperature, pain, the presence of drugs, recent eating or drinking, recent smoking and stress. Although many of these transient factors are easily controlled, such as by restriction of food intake prior to measurement, the impact of stress and anxiety which stimulates the patient's body 'fight or flight' response is not so readily managed in the WCH.
  • WCH hypertensive medication
  • the proportion of hypertensive patients with WCH is between 15% and 30%. These people may have white coat hypertension that goes unrecognized which could mean being wrongly diagnosed as having high blood pressure and receiving unnecessary treatment.
  • the WCH may be reduce with familiarity of the patient with the physician, environment, and/or technology. Foe example, it has been shown that the blood pressure readings of patients taken by a physician in a clinical environment on two different days two weeks apart tend to drop with time (James et al., The reproducibility of average ambulatory, home, and clinical pressures, Hypertension, Vol. 11, No. 6, Part 1, pp. 545-549, 1999).
  • WCH White coat hypertension
  • WCE white coat effect
  • Ambulatory blood pressure monitoring was introduced more than 40 years ago, and is now fully accepted as a clinically useful method. These days, ambulatory blood pressure systems are to be found both in hospitals and in numerous general practices all over the world. They are used to diagnose hypertension based on numerous recordings, and their advantage is that ambulatory blood pressure readings have lower variability than those taken in the physician's office. If the ambulatory blood pressure reading is normal, compared with an elevated reading in a clinic, the patient has WCH. According to experts in the field of blood pressure, the real blood pressure of a patient can only be detected by ABPM or self-monitoring, when there are no specific predisposing factors.
  • This system should be convenient and fast in operation, user friendly, and with precise measurement abilities.
  • a blood pressure module comprising a plethysmograph for measuring changes in a tissue volume in a predetermined location of said patient, and generating an electrical signal associated with said changes in said tissue volume;
  • a computing means with a processor said computing means is in communication with said blood pressure module
  • a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same by performing at least one operation selected from: (i) calculation of a set of measurement parameters from said electrical signal based on at least one auto-regressive moving average (ARMA) model; (ii) generation of a fixed length data vector comprising said set of measurement parameters and clinical parameters of said patient; (iii) storage in a communicable database said data vector and other data vectors; and, (iv) performing a classification based on a "Random Forest” algorithm by using said data vector and said database, such that the blood pressure of said patient is estimated; and,
  • ARMA auto-regressive moving average
  • a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient and to characterize the profile of said WCH and said WCE in said patient according to a diagnosis protocol;
  • the programmed executable instructions of said second storage further adapted to provide a "real" blood pressure measurement which is not biased by said WCH and WCE by eliminating said profile of said WCH and said WCE from said estimated blood pressure measurement of said patient.
  • a non-invasive system for diagnosing a WCH and WCE and in a patient comprising: (i) a blood pressure module comprising a plethysmograph; (ii) a computing means with a processor, said computing means is in communication with said blood pressure module; (iii) a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same; and, (iv) a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient and to characterize the profile of said WCH and said WCE in said patient according to a diagnosis protocol;
  • a blood pressure module comprising a plethysmograph for measuring changes in a tissue volume in a predetermined location of said patient, and generating an electrical signal associated with said changes in said tissue volume;
  • a computing means with a processor said computing means is in communication with said blood pressure module
  • a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same by performing at least one operation selected from: (i) calculation of a set of measurement parameters from said electrical signal based on at least one auto-regressive moving average (ARMA) model; (ii) generation of a fixed length data vector comprising said set of measurement parameters and clinical parameters of said patient; (iii) storage in a communicable database said data vector and other data vectors; and, (iv) performing a classification based on a "Random Forest” algorithm by using said data vector and said database, such that the blood pressure of said patient is estimated;
  • ARMA auto-regressive moving average
  • system further comprises a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient according to a diagnosis protocol, and to differentiate patients with sustained or normalized hypertension and patients with WCH and WCE.
  • PPG photoplethysmograph
  • pulse oximeter an acoustic plethysmograph
  • a mechanical plethysmograph or any combination thereof.
  • said at least one auto-regressive moving average (ARMA) model is a Teager-Kaiser operator.
  • said clinical parameters of said patient are selected from a group consisting of: sex, age, weight, height, food consumption, time of day, BMI, weight divided by age, weight divided by Heart Rate (HR), height divided by HR, HR divided by age, height divided by age, age divided by the BMI, HR divided by body mass index, or any combination thereof.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MAP mean arterial pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • a non-invasive system for diagnosing a WCH and WCE and in a patient comprising: (i) a blood pressure module comprising a plethysmograph; (ii) a computing means with a processor, said computing means is in communication with said blood pressure module; (iii) a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same; and, (iv) a second storage in communication with said computing means containing programmed executable instructions adapted to detect said WCH in said patient according to a diagnosis protocol;
  • ARMA auto-regressive moving average
  • PPG photoplethysmograph
  • pulse oximeter an acoustic plethysmograph
  • a mechanical plethysmograph or any combination thereof.
  • said at least one auto-regressive moving average (ARMA) model is a Teager-Kaiser operator.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MAP mean arterial pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • a PPG based blood pressure system for estimation of the patient's blood pressure
  • a second storage in communication with the computing means containing programmed executable instructions adapted to: (i) diagnose the WCH and said WCE in said patient; (ii) characterize the profile of the WCH and the WCE in said patient according to a diagnosis protocol; and, (iii) to provide a "real" blood pressure measurement which is not biased by the WCH and WCE by eliminating the profile of the WCH and the WCE from the estimated blood pressure measurement of the patient;
  • said estimation of said patient's blood pressure is performed via a "Random Forest” algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, the clinical parameters are received from the patient.
  • a "Random Forest” algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, the clinical parameters are received from the patient.
  • RAM auto-regressive moving average
  • the step of estimation of the blood pressure of the patient is performed via a "Random Forest” algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, the measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, the clinical parameters are received from the patient.
  • a "Random Forest” algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, the measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, the clinical parameters are received from the patient.
  • ARMA auto-regressive moving average
  • a storage in communication with said blood pressure system containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient according to a diagnosis protocol;
  • the estimation of said patient's blood pressure is performed via a "Random Forest” algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • a "Random Forest” algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • RAM auto-regressive moving average
  • a PPG based blood pressure system for estimation of said patient's blood pressure, said estimation of said patient's blood pressure being performed via a "Random Forest” algorithm adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto- regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient; and
  • ARMA auto- regressive moving average
  • a storage in communication with said blood pressure system containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient according to a diagnosis protocol;
  • the non-invasive system for diagnosing white coat hypertension (WCH) and white coat effect (WCE) in a patient system does not require calibration via an additional blood measurement technique.
  • a blood pressure module comprising a plethysmograph for measuring changes in a tissue volume in a predetermined location of said patient, and generating an electrical signal associated with said changes in said tissue volume;
  • a computing means with a processor said computing means is in communication with said blood pressure module
  • a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same by performing at least one operation selected from: (i) calculation of a set of measurement parameters from said electrical signal based on at least one auto-regressive moving average (ARMA) model; (ii) generation of a fixed length data vector comprising said set of measurement parameters and clinical parameters of said patient; (iii) storage in a communicable database said data vector and other data vectors; and, (iv) performing a classification based on a SVM algorithm by using said data vector and said database, such that the blood pressure of said patient is estimated; and,
  • ARMA auto-regressive moving average
  • a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose said WCH and said
  • the programmed executable instructions of said second storage are further adapted to provide a "real" blood pressure measurement which is not biased by said WCH and WCE by eliminating said profile of said WCH and said WCE from said estimated blood pressure measurement of said patient.
  • PPG photoplethysmograph
  • pulse oximeter an acoustic plethysmograph
  • a mechanical plethysmograph or any combination thereof.
  • said at least one auto-regressive moving average (ARMA) model is a Teager-Kaiser operator.
  • said clinical parameters of said patient are selected from a group consisting of: sex, age, weight, height, food consumption, time of day, BMI, weight divided by age, weight divided by Heart Rate (HR), height divided by HR, HR divided by age, height divided by age, age divided by the BMI, HR divided by body mass index, or any combination thereof.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MAP mean arterial pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • a non-invasive system for diagnosing a WCH and WCE and in a patient comprising: (i) a blood pressure module comprising a plethysmograph; (ii) a computing means with a processor, said computing means is in communication with said blood pressure module; (iii) a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same; and, (iv) a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient and to characterize the profile of said WCH and said WCE in said patient according to a diagnosis protocol;
  • PPG photoplethysmograph
  • pulse oximeter an acoustic plethysmograph
  • a mechanical plethysmograph or any combination thereof.
  • said at least one auto-regressive moving average (ARMA) model is a Teager-Kaiser operator.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MAP mean arterial pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • a blood pressure module comprising a plethysmograph for measuring changes in a tissue volume in a predetermined location of said patient, and generating an electrical signal associated with said changes in said tissue volume;
  • a computing means with a processor said computing means is in communication with said blood pressure module;
  • a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same by performing at least one operation selected from: (i) calculation of a set of measurement parameters from said electrical signal based on at least one auto-regressive moving average (ARMA) model; (ii) generation of a fixed length data vector comprising said set of measurement parameters and clinical parameters of said patient; (iii) storage in a communicable database said data vector and other data vectors; and, (iv) performing a classification based on a SVM algorithm by using said data vector and said database, such that the blood pressure of said patient is estimated;
  • ARMA auto-regressive moving average
  • system further comprises a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient according to a diagnosis protocol, and to differentiate patients with sustained or normalized hypertension and patients with WCH and WCE.
  • PPG photoplethysmograph
  • pulse oximeter an acoustic plethysmograph
  • a mechanical plethysmograph or any combination thereof.
  • said at least one auto-regressive moving average (ARMA) model is a Teager-Kaiser operator.
  • said clinical parameters of said patient are selected from a group consisting of: sex, age, weight, height, food consumption, time of day, BMI, weight divided by age, weight divided by Heart Rate (HR), height divided by HR, HR divided by age, height divided by age, age divided by the BMI, HR divided by body mass index, or any combination thereof.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MAP mean arterial pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • a non-invasive system for diagnosing a WCH and WCE and in a patient comprising: (i) a blood pressure module comprising a plethysmograph; (ii) a computing means with a processor, said computing means is in communication with said blood pressure module; (iii) a first storage in communication with said computing means containing programmed executable instructions configured to receive said electrical signal and to process the same; and, (iv) a second storage in communication with said computing means containing programmed executable instructions adapted to detect said WCH in said patient according to a diagnosis protocol; b. measuring changes in a tissue volume in a predetermined location of said patient, and generating an electrical signal associated with said changes in said tissue volume via said blood pressure module;
  • ARMA auto-regressive moving average
  • PPG photoplethysmograph
  • pulse oximeter an acoustic plethysmograph
  • a mechanical plethysmograph or any combination thereof.
  • said at least one auto-regressive moving average (ARMA) model is a Teager-Kaiser operator.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MAP mean arterial pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • a PPG based blood pressure system for estimation of said patient's blood pressure
  • a second storage in communication with said computing means containing programmed executable instructions adapted to: (i) diagnose said WCH and said WCE in said patient; (ii) characterize the profile of said WCH and said WCE in said patient according to a diagnosis protocol; and, (iii) to provide a "real" blood pressure measurement which is not biased by said WCH and WCE by eliminating said profile of said WCH and said WCE from said estimated blood pressure measurement of said patient;
  • the estimation of said patient's blood pressure is performed via a SVM algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto- regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • SVM algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto- regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • the step of estimation of the blood pressure of said patient is performed via a SVM algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • SVM algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters
  • said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG
  • said clinical parameters are received from said patient.
  • a PPG based blood pressure system for estimation of said patient's blood pressure
  • a storage in communication with said blood pressure system containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient according to a diagnosis protocol;
  • the estimation of said patient's blood pressure is performed via a SVM algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto- regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • SVM algorithm which is adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto- regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient.
  • a PPG based blood pressure system for estimation of said patient's blood pressure, said estimation of said patient's blood pressure being performed via a SVM algorithm adapted to receive a fixed length data vector comprising a set of measurement parameters and clinical parameters, said measurement parameters are based on at least one auto-regressive moving average (ARMA) model of the signals received from said PPG, said clinical parameters are received from said patient; and
  • ARMA auto-regressive moving average
  • a storage in communication with said blood pressure system containing programmed executable instructions adapted to diagnose said WCH and said WCE in said patient according to a diagnosis protocol;
  • the non-invasive system for diagnosing white coat hypertension (WCH) and white coat effect (WCE) in a patient system does not require calibration via an additional blood measurement technique.
  • FIG. 1 is a schematic illustration of a profile of a patient's blood pressure obtained by invasive catheterization.
  • FIG. 2 is a block diagram of the system and the method of the present invention for estimation of blood pressure.
  • FIG. 3 is a schematic illustration of a profile of a PPG signal as measured the signal of the present invention.
  • FIG. 4 is a schematic illustration is a block diagram of the pre-processing step of the method of the present invention.
  • WCH refers hereinafter to a 'white coat hypertension'. It is a phenomenon in which a patient exhibits elevated blood pressure in a clinical setting (e.g., the doctor's office) but not in other settings (e.g., at home).
  • 'WCE' refers hereinafter to a 'white coat effect'. It is a phenomenon in which patient's systolic blood pressure is at least about 20 mmHg and/or diastolic blood pressure is at least about 10 mmHg lower at home than in the doctor's office.
  • plethysmograph' is an instrument for measuring changes in volume within an organ or whole body.
  • PPG refers hereinafter to a photoplethysmograph which produces signals that are associated with changes in blood volume in the arteries and capillaries.
  • ABSPM refers hereinafter to ambulatory blood pressure monitoring.
  • SBP systolic blood pressure
  • DBP diastolic blood pressure
  • MBP refers hereinafter to the mean blood pressure of a patient.
  • PP refers hereinafter to pulse pressure which is the difference between systolic and diastolic blood pressure.
  • ARMA refers hereinafter to auto regressive moving average.
  • AR refers hereinafter to auto regressive.
  • MA refers hereinafter to moving average.
  • WCH and WCE refers hereinafter to: a diagnosed WCH and WCE at same time and in the same patient, a diagnosed WCH without diagnosed WCE at same time and in the same patient, and a diagnosed WCE without diagnosed WCH at same time and in the same patient.
  • office settings' refers hereinafter to settings which can influence on the measured blood pressure level, such as: the doctor's office, the hospital, etc. Moreover, the term “office settings' refers hereinafter to settings in which a measurement is performed by a doctor or a physician without the dependency of the measurement location.
  • other settings' refers hereinafter to settings which different than the office setting, and in which the measure blood pressure should not be influenced. Moreover, the term “other settings' refers hereinafter to settings in which a measurement is not performed by a doctor or a physician, but by somebody else (e.g., the person himself, a nurse, a relative, etc.) without the dependency of the measurement location.
  • SVM Support Vector Machine algorithm.
  • the core of the invention is to provide a system and method for blood pressure measurements without the need for calibration.
  • a system is able to diagnose white coat hypertension (WCH) and white coat effect (WCE) in said patient and to differentiate patients with sustained or normalized hypertension and patients with WCH and WCE.
  • WCH white coat hypertension
  • WCE white coat effect
  • the system of the present invention is adapted to "real" blood pressure measurement which is not biased by said WCH and WCE (in case of patient with diagnosed WCE and/or WCH).
  • the system of the present invention disclosed hereinafter is a non-invasive system for measuring blood pressure of a patient with elimination of white coat hypertension (WCH) and white coat effect (WCE).
  • WCH white coat hypertension
  • WCE white coat effect
  • a blood pressure module comprising a plethysmograph for measuring changes in a tissue volume in a predetermined location of said patient, and generating an electrical signal associated with said changes in said tissue volume.
  • a computing means with a processor.
  • the computing means is in communication with the blood pressure module.
  • a first storage in communication with the computing means containing programmed executable instructions configured to receive the electrical signal and to process the same by performing at least one operation selected from: (i) calculation of a set of measurement parameters from said electrical signal based on at least one auto-regressive moving average (ARMA) model; (ii) generation of a fixed length data vector comprising the set of measurement parameters and clinical parameters of the patient; (iii) storage in a communicable database the data vector and other data vectors; and, (iv) performing a classification based on a "Random Forest” algorithm by using the data vector and the database, such that the blood pressure of the patient is estimated.
  • ARMA auto-regressive moving average
  • a second storage in communication with the computing means containing programmed executable instructions adapted to diagnose the WCH and the WCE in the patient and to characterize the profile of the WCH and the WCE in the patient according to a diagnosis protocol.
  • the system further comprises programmed executable instructions of said second storage which are adapted to provide a "real" blood pressure measurement which is not biased by the WCH and WCE by eliminating the profile of the WCH and the WCE from the estimated blood pressure measurement of the patient.
  • the system of the present invention disclosed hereinafter is a non-invasive system for diagnosing white coat hypertension (WCH) and white coat effect (WCE) in a patient.
  • the system comprises:
  • a blood pressure module which comprises a plethysmograph for measuring changes in a tissue volume in a predetermined location of the patient, and generating an electrical signal associated with the changes in the tissue volume.
  • a computing means with a processor.
  • the computing means is in communication with the blood pressure module.
  • a first storage in communication with the computing means containing programmed executable instructions configured to receive the electrical signal and to process the same by performing at least one operation selected from: (i) calculation of a set of measurement parameters from the electrical signal based on at least one auto-regressive moving average (ARMA) model; (ii) generation of a fixed length data vector comprising the set of measurement parameters and clinical parameters of the patient; (iii) storage in a communicable database the data vector and other data vectors; and, (iv) performing a classification based on a "Random Forest” algorithm by using the data vector and the database, such that the blood pressure of the patient is estimated.
  • ARMA auto-regressive moving average
  • the system further comprises a second storage in communication with said computing means containing programmed executable instructions adapted to diagnose the WCH and the WCE in the patient according to a diagnosis protocol, and to differentiate patients with sustained or normalized hypertension and patients with WCH and WCE.
  • the terms 'white coat hypertension' (WCH) and a 'white coat effect' (WCE) in some circumstances pertain to the same meaning, and in some circumstances to a different meaning.
  • the present invention discloses a non-invasive system for diagnosing a white coat hypertension (WCH) and a white coat effect (WCE) in a patient.
  • WCH white coat hypertension
  • WCE white coat effect
  • the diagnosis of WCH and WCE are performed by the system of the present invention, following a measurement of the patient's blood pressure in a non-invasive manner. This diagnosis is performed by an analysis of the levels of said blood pressure.
  • the system of the present invention comprises the following components:
  • a blood pressure module is a sensor module which comprises a plethysmograph for measuring changes in a tissue volume in a predetermined location of said patient (e.g., finger, the lobe of the ear, etc.), and generating an electrical signal associated with the changes in the tissue volume.
  • the blood pressure module is selected from a group consisting of: a photoplethysmograph (PPG), a pulse oximeter, an acoustic plethysmograph, a mechanical plethysmograph, or any combination thereof.
  • the plethysmograph is a PPG.
  • a computing means with a processor is in communication with the blood pressure module by means of wires or by wireless means which are well known in the art.
  • a first storage in communication with the computing means containing programmed executable instructions (algorithm) configured to receive the electrical signal and to process the same.
  • a second storage in communication with the computing means containing programmed executable instructions (algorithm) adapted to detect the WCH and WCE in said patient according to a diagnosis protocol, and to differentiate patients with sustained or normalized hypertension and patients with WCH and WCE.
  • algorithm programmed executable instructions
  • a main advantage of the present invention over the prior art is not only that it is based on a non-invasive precise measurement of blood pressure, but also on the fact that the system does not require calibration with additional techniques and devices.
  • the PPG which is located within the blood pressure module is adapted to create a PPG signal which is associated with changes in blood volume in the arteries and capillaries.
  • the blood pressure module is adapted to be located on the patients' finger.
  • the system of the present invention is based on the assumption that there is a relationship between the PPG signal and the blood pressure of a patient. According to the present invention, this relationship is derived from a data extracted from the PPG signal and the statistical data of the patient himself.
  • the system of the present invention estimates the blood pressure of a patient according to an estimated decision function. Since the PPG signal is characterized by a variable length, one of the objects of the present invention is to produce a fixed length data vector for each measurement.
  • This data vector contains parameters which are derived from the PPG signal and additional parameter which are associated with the clinical information of the patient.
  • the parameters which are derived from the PPG signal might be for example: the shape of the signal (e.g., auto-regression coefficients, moving average, etc.), distance between pulses, the variance of the signal, the energy of the signal, the changes in the energy of the signal, etc.
  • the clinical information of the patient might be for example: sex, age, weight, height, health information, etc.
  • the estimated decision function of the present invention is the well known “Random Forests” algorithm. That is opposed to other machine learning and pattern recognition techniques such as: regression and decision trees (CART), 'Splines', and Neural Networks.
  • the 'Random forests' algorithm is a classifier based on the generation of a parallel set of decision trees which estimate the function of a random selection of variables. This algorithm operates in the following way: the 'Random Forests' algorithm grows many classification trees; to classify a new object from an input vector, an input vector is put down in each of the trees in the forest; each tree gives a classification, and we say the tree "votes" for that class; and, the forest chooses the classification having the most votes (over all the trees in the forest).
  • the implementation of the system of the present invention for measurement of blood pressure consists of two distinct phases.
  • the first phase is the training phase of the system, which is performed only once and therefore does not require calibration later.
  • This phase consists of obtaining a database with information on various parameters of different patients which includes their personal parameters such as: sex, weight, age, etc., along with the records of their PPG signals. This information is used for the estimation of the parameters of the decision trees which are stored in said database of the system.
  • the second phase consists of loading the information of all the trees obtained in the training phase, and recording the PPG signal of the patient at the time of the measurement along with other clinical parameters such as: sex, weight, age, etc.
  • the system processes the PPG signal of the patients, and generates a fixed length data vector. Additionally to the processed data received from said PPG signal, the data vector also comprises said other variables of said patient.
  • the PPG sensor captures the PPG signal from the measurement location in a patient (e.g., a finger, ear lobe, etc.). Said PPG signal is associated with the oxygen saturation (Sp02) signal which is received via a pulse oximeter. In other words, said PPG sensor might be also used as a pulse oximeter.
  • the PPG signal is processed and measurement parameters are extracted from the signal, and the measurement parameters are combined with other clinical parameters 11 of said patient.
  • pre-processing step 12 the extraction of measurement parameters from the PPG signal is based on a stochastic model of the physiology of the circulatory system as presented in the background of the invention.
  • step of estimation 14 a fixed length vector which comprises the measurement parameters and the clinical parameters of the patient is inputted to the estimation function which is based on the "Random Forests" algorithm.
  • basic blood pressure parameters SBP, DBP, and MAP
  • other various functions which are related to error estimation are estimated.
  • the final values of the blood pressure SBP, DBP, and MAP
  • SBP, DBP, and MAP are calculated following a correction of the basic blood pressure parameters (SBP, DBP, and MAP) via said various functions which are related to error estimation.
  • the signal in step 10 is obtained by a PPG sensor unit which is a simple, noninvasive and low cost sensor for the detection of volume changes in a tissue.
  • the PPG sensor comprises two main light elements: (/) at least one source for illumination of the tissue (e.g., the skin); (//) at least one photo detector which is able to measure small variations in light intensity associated with changes in tissue perfusion at level of detection.
  • the PPG sensor is adapted for (/) emitting a light beam to an organ of the patient; (//) detecting the reflected light beam; and, (/ ' ) converting the detected light beam into an electrical signal.
  • the PPG is normally used in non-invasive measurements and operates in the wavelength of infrared or near-infrared (NIR).
  • NIR near-infrared
  • the PPG signal comprises a physiological pulsatile waveform (AC component) attributed to changes in blood volume synchronous with each heartbeat. This component is superimposed on another component of basal low frequency (DC component) related to the respiratory rate, the activity of the central nervous system and thermoregulation. According to the signal in FIG. 3, the fundamental frequency of the AC component is around 1 Hz (depending on the cardiac rhythm).
  • AC component physiological pulsatile waveform
  • DC component basal low frequency
  • the interaction between light and biological tissues is complex and includes processes such as optical scattering, absorption, reflection, transmission and fluorescence.
  • the light of the PPG sensor is in the NIR (e.g., close to 805 nm).
  • the PPG signal has two distinct phases: the anacrotic phase, which represents the increase in the pulse, and the catacrotic phase, representing the fall of the pulse.
  • the first phase is related to the systolic phase of the blood pressure and the second phase is related to diastolic phase of the blood pressure.
  • the PPG signal of the present invention uses the oxygen saturation (Sp0 2 ) which can be obtained by the illumination of a tissue in the red and NIR wavelengths.
  • the systems which calculate the oxygen saturation are switching between two wavelengths for the determination of the oxygen saturation.
  • the oxygen saturation can be obtained by the illumination of the tissue in the red and the NIR wavelengths.
  • the amplitudes of the two wavelengths are sensitive to changes in Sp0 2 due to the difference in absorption of light in Hb0 2 and Hb for each one of the wavelengths.
  • the Sp0 2 can be obtained from the ratio between the amplitudes, and the AC and DC components of the PPG signal.
  • the light intensity (T) transmitted through tissue is commonly referred to as a DC signal and is a function of the optical properties of tissue (i.e., the absorption coefficient ⁇ ⁇ and scattering coefficient ⁇ 5 ).
  • the arterial pulse produces periodic variations in the concentrations of oxy and deoxy hemoglobin, resulting in turn in periodic variations in the absorption coefficient.
  • the PPG signal is proportional to the physiological variation of light intensity, which in turn is a function of the scattering and absorption coefficients ( ⁇ ⁇ and ⁇ 3 respectively).
  • Variations ⁇ ⁇ can be written as a linear variation of the concentrations of oxy and deoxy hemoglobin ( Ac ox and Ac deox ): ⁇ * a
  • dP is the differential change in the intensity of a light beam passing through a infinitesimal dz in with a uniform absorption coefficient 3 ⁇ 4 . Therefore, integrating over z we get the Beer-Lambert law:
  • the PPG signal which is obtained by the system of the present invention is used as input to the pre-processing step 12 of the system of the present invention whose main function is to establish a stochastic model of the circulatory function.
  • the spread of the pulse pressure should be taken into consideration throughout the analysis of the PPG signal.
  • PP pulse pressure
  • a regressive moving average (ARMA) models are used in the present invention in order to characterize the mechanism of the generation of the PP.
  • ARMA Auto regressive moving average
  • parameters which affect the shape and the propagation of the PP are related to: cardiac output, heart rate, cardiac synchrony, respiratory rate, metabolic function, etc.
  • step 20 of the pre-processing step 12 a stochastic modeling via Auto- regressive moving average (ARMA) is performed.
  • the Auto-regressive moving average (ARMA) model is a tool for understanding and, perhaps, predicting future values in this series.
  • the model consists of two parts, an auto regressive (AR) part and a moving average (MA) part.
  • the model is then referred to as the ARMA(p,q) model where p is the order of the autoregressive (AR) part and q is the order of the moving average (MA) part.
  • the PPG signal of the present invention which is PPG time series: PPG(n), PPG
  • PPGi z) j defined as the Z- transformed of the PPG signal, and:
  • the ARMA (q, p) filter in step 20 is given by:
  • a (z) and B (z) are the AR and MA components, respectively.
  • step 20 of FIG.4 a stochastic modeling via ARMA is applied on the PPG signal in the form of the filter H(z).
  • the ARMA model is using the Wold decomposition and the Levinson-Durbin recursion to generate the filter H (z) and the inverse filter 1/H(z) which is implemented on the signal in step 22 of FIG.4 Moreover, additional statistical calculation are performed in step 24 of FIG.4. The results of the statistical calculation of step 24 are stored in the fixed sized vector v().
  • step 26 of FIG.4 to model nonlinear interactions such as the PP, the present invention uses the Teager-Kaiser operator.
  • PPG pulse modulated AM-FM signal (modulated in amplitude and frequency) of the type: PPG ( t ) - a ( t ⁇ ) cos J w ( T ) dr (XX)
  • the Teague-Kaiser operator of a given signal is defined by:
  • This operator applied to the AM-FM modulated signal from equation (XX) is the instantaneous energy of the source that produces the oscillation of the PPG i.e.,
  • an AR process of order p is implemented on l [PPG(f)
  • the present invention calculates the heart rate (HR) and cardiac synchrony (i.e. heart rate variability) from the PPG signal in step 30.
  • HR heart rate
  • cardiac synchrony i.e. heart rate variability
  • the heart rate correlations which are calculated in step 30 by an autocorrelation function (with time windows between 2 seconds and 5 minutes) are applied on the signal.
  • step 32 of FIG.4 the zero crossings of the PPG signal are calculated, and later used in vector V ⁇ n ) .
  • step 34 of FIG. 4 a collection of clinical parameters related to the patient is performed. These parameters might be: Sex, age, weight, height, food consumption, time of day, BMI, Weight divided by age, Weight divided by HR, Height divided by HR, HR divided by age, Height divided by age, Age divided by the BMI , HR divided by body mass index.
  • step 20 Totally, following step 20, 22, 24, 26, 28, 30, 32, and 34, feature vector of fixed size V ( n ) is created, and the blood pressure can be estimated in step 14 by the "Random Forests" classifier.
  • the system of the present invention has an advantage over the prior art as being not requiring calibration in order to estimate the blood pressure. This is achieved via the "Random Forests" classification algorithm which is previously trained.
  • the "Random Forests” is a classifier consisting of a set of classifiers with a tree structure ⁇ /! ( '- (: , 1 . ⁇ ⁇ ⁇ wnere ®fc are random vectors which are independent and identically distributed (II D). Each vector 3 ⁇ 4 is adapted to provide a single vote for the most popular class of the input vector V(). This approach presents a clear advantage in terms of reliability compared to other classifiers which are based on a single tree and do not impose any restriction on the functional relationship between the pulse and blood pressure levels.
  • the "Random Forests” algorithm used in the present invention is generated by the growth of decision trees which are based on the random vector ⁇ such that the predictor ⁇ ( ⁇ , ⁇ ) outputs numerical values.
  • This random vector ⁇ associated with each tree provides a random distribution at each node while also providing information on the random sampling of the training base, resulting in different subsets of data for each tree.
  • the error of the "Random Forests" classifier used in the present invention is given by: PE -E 1 ⁇ 4y ( Y--h( V) ) 2 (xxiii).
  • each tree has a different generalization error and P represents the correlation between the residues identified in (XXIV). This fact implies that a lower correlation between the residues (XXIV) results in better estimates.
  • the minimum correlation is given by the random sampling process of the feature vector at each node of the tree that is trained by the system.
  • the present invention estimates the parameters of interest (SBP, DBP and MAP) as linear combinations of them.
  • Random Forests consist of a set of decision trees CART-type ( 'Classification and Regression Trees ", by its initials in English), altered to introduce systematic errors (XXV) on each one and then, through a system of 'bootstrap' a systematic variation (both random processes are modeled by the parameter 0 in the analysis of predictor ⁇ , ⁇ ) ) j e systematic error different in each realization is introduced by two mechanisms:
  • each tree is trained with a sample of type 'bootstrap' (ie a sample is taken from the input data, which leads to that part of the input data while missing and another part is repeated).
  • This effect of 'bootstrap' introduces variability, when making estimates of the average offset.
  • the overall result of these features which are part of the post-processing step 16, where the systematic error and variability of the error can be compensated quite easily more accurate than other estimators of functions (XXVII).
  • the base is a tree classifier, which decides on the basis of levels, making it robust against input distributions with 'outliers' or heterogeneous data types (such as the present invention).
  • the postprocessing step 16 of FIG. 3 is to take random samples from two 47-level node (which may also implement changes between 2 and 47) and size of a 'bootstrap' which is 100.
  • the 'bootstrap' may be between a size of 25 to a size of 500.
  • the computing means of the present invention is selected from a group consisting of: a DSP system, FPGA, microcontroller, or any combination thereof.
  • the second storage which is in communication with the computing means contains programmed executable instructions (algorithm) adapted to detect the WCH and WCE in the patient according to a diagnosis protocol.
  • This diagnostic protocol can also differentiate patients with sustained or normalized hypertension and patients with WCH and WCE.
  • the record of the aforementioned blood pressure may be analyzed by the system in order to determine whether said patient has WCH and WCE or one of them. This analysis is performed via the algorithm stored in the second storage of the system.
  • the diagnostic protocol which is stored in said second storage comprising a set of rules and threshold according to which the WCH and the WCE are diagnosed.
  • the time parameter The system may be operated for a predetermined length of time which can vary for example from minutes (e.g., 15, 30 min.) to days (e.g., 24 hours, 48 hours, etc). The length of the time parameter may influence on the precision of said diagnosis. According to the preferred embodiment of the invention, the system is operated with time parameter of about 24 hours.
  • Mode of operation The system may be operated in different modes of operation (e.g., ambulatory, continuous, discrete). For example, a continuous or ambulatory (ABMP) mode may be used when the person is sent home with the system attached to his body, and a precise measurement of blood pressure is needed (e.g., a reading which is takes every a few seconds). A discrete mode may be used when the blood pressure is measure every a few minutes or hours. According to the preferred embodiment of the invention, the system is operated in the continuous or the ambulatory (ABMP) modes.
  • ABMP continuous or ambulatory
  • a discrete mode may be used when the blood pressure is measure every a few minutes
  • Blood pressure thresholds There are different studies which are relevant to the WCH and WCE. According to these studies, there are various thresholds of blood pressure that determine the WCH and the WCE.
  • the WCH is determined when the measured blood pressure is the following: the SBP is at least about 140 mmHg and/or the DBP is at least about 90 in office settings, while at in other settings (e.g., at home) the measured blood pressure is the following: the SBP is less than about 135 mmHg and/or the DBP is less than about 85.
  • the WCE is determined when the measured blood pressure in office settings in higher than the measured blood pressure in other setting (e.g., at home) in the following measures: the SBP is higher by at least about 20 mmHg, and the DBP is higher by at least about 10 mmHg.
  • Registration of the location of measurement When the system of the present invention performs a measurement, it is highly important to register the location in which the measurement is taken, for the WCH and WCE diagnosis algorithm. Therefore, as part of the operation of the system, the location of the operation is registered.
  • the measurement location may be classified to office setting and to other settings, and the algorithm may use this classification for the diagnosis of WCH and WCE.
  • Registration of the identity of person who performs the measurement when a measurement is performed , the personal and professional identity of the person performing the measurement is registered. For example, when a doctor performs the measurement, the system is operated in office settings (e.g., doctor's office). Contrary, if the measurement is performed by the person himself, this means that the system is operated in other settings (e.g., home settings).
  • the algorithm of the second storage is further adapted to register the settings in which the system is operated.
  • the settings are selected from a group consisting of: the location measurement, the kind of person who performs the measurement, such that the settings are classified to two classes: office settings and other settings.
  • the system is adapted to diagnose WCH and WCE in the patient according to said diagnosis protocol and said settings.
  • the system may be used to detect when the WCH and/or WCE do not influence on the measured blood pressure, or at least reduced. This can be done by the algorithm of the second storage which can detect the time in which the blood pressure is reduced to a predetermined level in which the sittings in which the system is operated do not influence.
  • the system of the present invention is adapted to differentiate between patients with WCH and/or WCE ad patients with sustained or normalized hypertension with a precision of at least about 90%.
  • the algorithm of the second storage may perform statistical analysis as part of its operation to detect the WCH and the WCE in the patient.
  • the determination of the blood pressure in different settings may be performed by calculation of mean blood pressure (e.g., mean SBP, mean DBP, etc.) in a predetermined time and in a specific location.
  • the level of the mean blood pressure may comprise two levels: an upper level and a lower level of the mean blood pressure. The analysis of the algorithm of the second storage may be performed with respect to these two levels.
  • the system of the present invention may differentiate patients with sustained or normalized hypertension and patients with WCH, when the blood pressure levels are defined as following: a measurement which is performed by the person himself in which the SBP is ⁇ 140 mmHg and/or the DBP is ⁇ 90 mmHg, and a measurement which is performed by the doctor in which the SBP is > 160 mmHg and/or the DBP is > 95 mmHg.
  • the algorithm of the second storage is adapted to diagnose WCH and WCE by comparing the measured blood pressure of a patient during said office settings (e.g., for about 15 minutes), and the blood pressure of said patient before/after said visit (e.g., for about 15 minutes).
  • the diagnosis of the WCH and the WCE can be performed via other known in the art diagnosis protocols, and is not limited to the diagnosis protocol disclosed by the present invention.
  • the present invention is adapted to measure blood pressure of a patient with elimination of the WCH and the WCE.
  • the second storage which is in communication with the computing means containing programmed executable instructions (algorithm) adapted to diagnose the WCH and WCE in said patient according to a diagnosis protocol, and to provide a "real" blood pressure measurement which is not biased by said WCH and WCE by eliminating the profile of the WCH and the WCE from the estimated blood pressure measurement of the patient.
  • the measurement of blood pressure and the diagnosis of said WCH and WCE of this embodiment of the present invention are performed via the disclosed above embodiments of the present invention.
  • the programmed executable instructions (algorithm) of the second storage may estimate and characterize the profile of said WCH and said WCE.
  • the profile of said WCH and said WCE in said patient is characterized by a time-dependent amplitude of the difference between a blood pressure of said patient in office settings and a blood pressure of said patient in other settings.
  • this measured blood pressure is not the "real" blood pressure of the patient, but a biased one.
  • This biasing factor should be eliminated from the blood pressure measurement in order to provide a "real" blood pressure which is not influenced by said WCH and said WCE.
  • This biasing factor is the profile of said WCH and said WCE.
  • the profile of said WCH and WCE is estimated by the diagnosis protocol which comprises the information regarding the blood pressure signal of said patient in different settings.
  • this diagnosis protocol has a record of a patient's blood pressure in office setting (a first signal) and a record of a patient's blood pressure in other setting (a second signal), both of them are characterized by the same length in time.
  • additional signal processing e.g., interpolation
  • the profile of said WCH and WCE is estimated by other conventional techniques which can characterize the profile of said WCH and WCE, and eliminate them from the measured blood pressure, and thereby provides a "real" blood pressure.
  • the profile of said WCH and WCE is stored in the database defined by the present invention.
  • the person that activates the system of the present invention may receive two kinds of blood pressure measurement signals simultaneously: an estimated blood pressure by the system of the present invention, a "real" blood pressure in case of a WCH and WCE in a patient.
  • the person that activates the system of the present invention may activate and deactivate in any time the algorithm that provides the "real" blood pressure.
  • a "real" blood pressure is provided
  • a blood pressure measurement is provided.
  • Support vector machines are a set of related supervised learning methods used for classification and regression. In simple words, given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.
  • an SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.
  • a support vector machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space, which can be used for classification, regression or other tasks.
  • a good separation is achieved by the hyperplane that has the largest distance to the nearest training datapoints of any class (so-called functional margin), since in general the larger the margin the lower the generalization error of the classifier.
  • the classification is performed via a Support Vector Machine (SVM) algorithm, instead of the "random forests” algorithm. Therefore, all the methods of calculation and data classification as described above might be performed via the SVM algorithm. Moreover, for each application of the "random forests” algorithm, according to said other embodiments, the operations might be performed via the SVM algorithm instead.
  • SVM Support Vector Machine

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Abstract

Cette invention concerne un système non invasif qui permet de mesurer la pression sanguine d'un patient en éliminant l'hypertension de la blouse blanche et l'effet blouse blanche, ledit système comprenant : un module pression sanguine comprenant une PPG pour mesurer les variations dans un volume tissulaire en un endroit prédéterminé chez le patient, et générer un signal électrique associé auxdites variations dans le volume tissulaire ; un dispositif de calcul pourvu d'un processeur, ledit dispositif étant en communication avec le module ; un premier moyen de stockage en communication avec le dispositif contenant des instructions pour recevoir le signal électrique et le traiter par exécution d'au moins une opération choisie parmi les suivantes : calcul d'un jeu de paramètres de mesure à partir du signal électrique basé sur au moins un modèle de moyenne mobile auto-régressive (ARMA) ; (ii) génération d'un vecteur de données de longueur fixe constitué du jeu de paramètres de mesure et cliniques ; (iii) sauvegarde dans une base de données du vecteur de données et autres vecteurs de données ; et, (iv) exécution d'une régression basée sur un algorithme « Random Forest » à l'aide du vecteur de données et de la base de données, la pression sanguine étant calculée ; et un second moyen de stockage en communication avec ledit dispositif contenant des instructions pour diagnostiquer l'hypertension de la blouse blanche et l'effet blouse blanche et pour caractériser le profil de l'hypertension de la blouse blanche et de l'effet blouse blanche selon un protocole de diagnostic ; les instructions dudit second moyen de stockage étant, en outre, conçues pour fournir une mesure de pression sanguine « réelle » qui n'est pas biaisée par l'hypertension de la blouse blanche et l'effet blouse blanche par élimination du profil de l'hypertension de la blouse blanche et de l'effet blouse blanche de la mesure de pression sanguine estimée.
PCT/EP2011/053294 2010-03-09 2011-03-04 Système non invasif et méthode pour diagnostiquer et éliminer l'hypertension de la blouse blanche et l'effet blouse blanche chez un patient WO2011110491A1 (fr)

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WO2016026698A1 (fr) * 2014-08-22 2016-02-25 Koninklijke Philips N.V. Procédé et appareil de mesure de la pression artérielle à l'aide d'un signal acoustique
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WO2019131252A1 (fr) * 2017-12-27 2019-07-04 オムロンヘルスケア株式会社 Dispositif, procédé et programme de traitement d'informations
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CN114767085A (zh) * 2022-06-17 2022-07-22 广东百年医疗健康科技发展有限公司 一种血压监测方法

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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9301700B2 (en) 2012-09-27 2016-04-05 Welch Allyn, Inc. Configurable vital signs system
US11071467B2 (en) 2013-08-08 2021-07-27 Welch Allyn, Inc. Hybrid patient monitoring system
WO2016026698A1 (fr) * 2014-08-22 2016-02-25 Koninklijke Philips N.V. Procédé et appareil de mesure de la pression artérielle à l'aide d'un signal acoustique
CN106572804A (zh) * 2014-08-22 2017-04-19 皇家飞利浦有限公司 用于使用声学信号来测量血压的方法和装置
US20180132731A1 (en) * 2016-11-15 2018-05-17 Microsoft Technology Licensing, Llc Blood pressure determinations
WO2018093617A1 (fr) * 2016-11-15 2018-05-24 Microsoft Technology Licensing, Llc Déterminations de la pression sanguine
US11096595B2 (en) 2016-11-15 2021-08-24 Microsoft Technology Licensing, Llc Blood pressure determinations
WO2019131252A1 (fr) * 2017-12-27 2019-07-04 オムロンヘルスケア株式会社 Dispositif, procédé et programme de traitement d'informations
JP2019115585A (ja) * 2017-12-27 2019-07-18 オムロンヘルスケア株式会社 情報処理装置、情報処理方法及び情報処理プログラム
CN111511277A (zh) * 2017-12-27 2020-08-07 欧姆龙健康医疗事业株式会社 信息处理装置、信息处理方法和信息处理程序
CN111511277B (zh) * 2017-12-27 2023-04-18 欧姆龙健康医疗事业株式会社 信息处理装置、信息处理方法和信息处理程序
CN114767085A (zh) * 2022-06-17 2022-07-22 广东百年医疗健康科技发展有限公司 一种血压监测方法

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