EP2874539A1 - A method and system for determining the state of a person - Google Patents
A method and system for determining the state of a personInfo
- Publication number
- EP2874539A1 EP2874539A1 EP13736568.0A EP13736568A EP2874539A1 EP 2874539 A1 EP2874539 A1 EP 2874539A1 EP 13736568 A EP13736568 A EP 13736568A EP 2874539 A1 EP2874539 A1 EP 2874539A1
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- Prior art keywords
- person
- features
- determining
- ppg
- bile
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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
- A61B5/02416—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
- A61B5/48—Other medical applications
- A61B5/4854—Diagnosis based on concepts of traditional oriental medicine
-
- 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
- A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
-
- 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
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04C—ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT PUMPS
- F04C2270/00—Control; Monitoring or safety arrangements
- F04C2270/04—Force
- F04C2270/042—Force radial
- F04C2270/0421—Controlled or regulated
Definitions
- the present invention concerns a method and system for determining the state of a person; and in particular to a method and system for determining the Vietnamese state and/or state of the body and mind of a person in which features of digital pulse waveform which is representative of the pulse of a person, is used to automatically determine the state of the person.
- TTM Traditional Vietnamese Medicine
- TAM Chinese
- TAM Chinese
- TAM Ayurvedic
- BMC body-mind complex
- the Typology of a person can be of three different aspects according to Traditional Vietnamese Pulse reading: wind (Lung), bile (Tripa) and phlegm (Beken).
- Each person possesses a specific Typology at a certain age. These Typologies evolve with time, external conditions and internal conditions, i.e. young babies tend to be more phlegm, adult more bile and elderly more wind.
- the Typology of a person can change with the seasons, with the night and day cycle, breathing cycle, and/or even with the heart pulse cycle.
- Each person possesses the three Typologies in a certain ratio at any particular time. These three typologies play an important role in the TTM system of diagnosis and treatment. The tendency of a person to be of a certain type tells the traditional doctor about her physical and mental condition, which in turn is used in further diagnosis refinement.
- Pathologies reflect the fact that the three Typologies of a person are in a state of imbalance; to give a precise example, if a person is predominantly bile type but presents conditions of flu, we can say that she has developed a phlegm disorder.
- TTP Traditional Vietnamese Pulse
- the TTP can be measured sequentially with one finger at a time as well as simultaneously with the three fingers (as shown in Figure 1 a).
- the TTP is traditionally recorded with three fingers (index, middle and ring) of the doctor (see Figure 1 b), placed along the radial artery of the wrist: first on the left/right wrist for male/female and second on the right/left wrist for male/female.
- Each fingertip, index, middle and ring assess different parts of the body: upper, middle and lower respectively.
- the index is always placed toward the thumb in a flat position so that each side of the fingertip can sense the pulse wave.
- the mapping of the organs senses with the fingers and the fingers position is displayed in Figure 1 c.
- Figurel c shows that TTP reading can identify and diagnose 12 organs using the two wrists of the patient: 2(wrist)x2(finger's
- the doctor determines the typology of the patient on the basis of the characteristics of the pulse of the patient which the doctor feels through the three fingers.
- the following correspondence between the Typology and the pulse characteristics can be made as follows:
- TTP reading can only be performed by skilled doctors who simply place their fingers on some vital patient's body locations to feel and 'listen to' the pulse of the patient; the doctor then draw a conclusion as to the Typology of the patient based on characteristics of the pulse he/she has felt.
- pulse reading in this manner requires many years of training and experience. There is no means provided to date which would enable an unskilled person to perform TTP reading.
- a method for determining the state of a person comprising the steps of: sensing a pulse of the person using a
- PPG photoplethysmograph
- DPW digital pulse waveform
- the state of the person may comprise at least one of: the heart condition, breathing condition and/or relaxing condition of the person.
- the state of the person may be the Vietnamese state of the person.
- the Vietnamese state of the person may comprise a ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typologythat the person possesses.
- the classical state of the person including the heart condition, breathing condition and relaxing condition of the person and/or the Vietnamese state of the person (i.e. the Typology of the person) can be determined automatically.
- the heartcondition, breathing condition,relaxing conditions, the determined ratio of wind (Lung), bile (Tripa) and phlegm (Beken) Typology of the person may be represented either on a digital display by a set of user chosen color tones, images or other suitable visual information; or on an analogue display using mechanical means.
- Determining the heart condition, breathing condition and relaxing condition of the person cancomprise determining values each of which is representative of the heart condition, breathing condition and relaxing condition of the person. Determining a value which is representative of the heart condition of the person can be determined using a measurement of the heart rate and heart rate variability of the person. Determining a value which is representative of the breathing condition of the person can be determined using a measurement of the pulse wave envelope and Peak- to-Peak (PP) intervals, of the person. Determining a value which is representative of the relaxed condition of the person can be determined using a measurement of the heart rate variability and spectral entropy of the PP, of the person.
- PP Peak- to-Peak
- the value which is representative of the heart condition, of the breathing condition, and of the relaxed condition of the person can be displayed on a display only if the measured movement of the person is below the threshold level of movement. Moreover, the value which is representative of the heart condition, of the breathing condition, and of the relaxed condition of the person can be displayed on a display continuously or only on demand.
- the DPW is preferably generated by PPG signals which are output from the PPG sensorwhen the PPG sensor is sensing the pulse of the person.
- the one or more features may comprise at least one of, heart rate variability, spectral entropy and/or undulations level indices.
- the one or more features may comprise at least one of, the pulse wave envelope, peak-to-peak (PP) intervals, heart rate, heart rate variability indices in the frequency domain and/or spectral entropy of the PP intervals.
- the one or more features may comprise, at least one of, the mean value of PP (Rm), normalized variance of PP (Rv), normalized undulation level of the heart pulse wave (EUL), normalized spectral entropy of the heart pulse wave (ESEh), Lempel-Ziv complexity of the heart pulse wave (PW) (LZrr) and/or normalized breathing frequency (BF).
- the method may comprise the step of forming surrogates of the one or more parameters.
- the pulse of the person can be sensed at one of the locations of the person comprising at least the wrist, hand, fingers and ears of a person, and the method can comprise securing the PPG sensor around the wrist, hand, fingers or ears of a person such that the PPG sensor is arranged to sense pulse at the wrist, hand, fingers or ears of the person.
- the method can comprise the step of sensing the pulse at the left wrist, hand or fingers of the person or at the left ear of the person,such as to generate a first DPW, and sensing the pulse at the right wrist, hand or fingers of the person or at the right ear of the person, such as to generate a second DPW.
- the method can further comprise determining one or more features of each of the first and second DPW; and using the one or more features to determine the ratio of wind, bile and phlegm Typology that the person possesses
- the method can comprise the step of sensing the pulse at one of the left and right wrist, hand, fingers and ear of the person respectively; generating a corresponding DPW; determining one or more features of the corresponding DPW; and using the one or more features in combination with a set of reference features extracted from offline population average to determine the ratio of wind, bile and phlegm
- the method can furthercomprise the step of determining the difference between the one or more features of each of the first and second DPW.
- the determined difference can be used for determining the ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology.
- the method may further comprise the step of measuring the movement of the person.
- the movement of the person can bemeasured using one or more accelerometers. Preferably the movement of the person is continuously measured.
- the method cancomprise the step of comparing the measured movement of the person to a threshold level of movement, and determining one or more features of the DPW only if the measured movement of the person is below the threshold level of movement.
- the measured movement of the person can be continuously displayed on a display. Alternatively the measured movement of the person can be displayed on the display only on demand.
- the method canfurther comprise determining a quality index value which is representative of the quality of the PPG signal, and comparing the quality index value to a threshold quality index value.
- the step of determining the one or more parameters of the DPW canbe performed only if determining a quality index value is greater than the threshold quality index value.
- the method cancomprise the step of streaming one or more features of the DPW and the DPW to an operating system.
- the operating system canbe configured to use the one or more features to determine the ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology that the person possesses.
- the operating system may comprise a display which can display one or more measurements or results e.g. to display the determined ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology that the person possesses.
- the operating system may comprise the display of the heart, breathing and relaxing condition.
- the operating system may further comprise a storage means for storing data.
- the method may further comprise the step of selecting a subgroup of features from the one or more features from the determined one or more features of the DPW; and using the selecting subgroup of features, to determine the ratio of wind, bile and phlegm Typology that the person possesses.
- the method may further comprise the step of performing principal component analysis on the one or more features to extract salient information from the features and separate them for further
- the step of performing principal component analysis on the one or more features may be carried out to generate feature clusters which are representative of the wind (lung), bile (Tripa) and phlegm (Beken) Typology.Preferably the principal component analysis is performed in state space.
- the method may further comprise the step of identifying which cluster corresponds to wind (lung), bile (Tripa) and phlegm (Beken) Typology respectively.
- the Typology of the person being determined by a Vietnamese doctor in order to perform the classification of the feature clusters.
- the method may further comprise the step of separating clusters (classification) which have been determined during principal component analysis and which are representative of the wind (lung), bile (Tripa) and phlegm (Beken) typology respectively, from each other. Each cluster corresponds to either of the wind (lung), bile (Tripa) and phlegm (Beken) Typology as determined by the Vietnamese doctor.
- the method may further comprise the step of using a classifier which has been trained to map the different clusters into the Vietnamese doctor typology as determined by the Vietnamese doctor.
- the classifier may be of Neural Network, Linear Discriminant or similar architecture.
- the method may further comprise the step of processing the DPW to reduce artefacts.
- a linear filter may be applied to the DPW to reduce artefacts in the DPW.
- the method may further comprise the step of using principal component analysis on the DPW, with selection of the components which have a frequency within a predefined range.
- the method may be performed while the person is sleeping to determine a first ratio of the wind (lung), bile (Tripa) and phlegm (Beken) typology of a person, and the method may be performed while the person is awake to determine a second ratio of the wind (lung), bile (Tripa) and phlegm (Beken) Typology of a person, and the method may further comprise the step of detecting changes between the first and second determined ratios.
- the method may contains information extracted from the threeclassic conditions of heart, breathing and relaxing while the person is sleeping to determine sleep disordered breathing patterns.
- a system for determining the state of a person comprising, a PPG sensor which is configured to generate a DPW which represents a sensed pulse of the person; a means for determining one or more features of the DPW; a means for determining thestate (classic and/or Vietnamese state) of the person from the one or more parameters of the DPW.
- the means for determining the state of the person may comprises a means for determining the heart condition, breathing condition and/or relaxing condition of the person from the one or more features of the DPW.
- the means for determining the state of the person may comprises a means for determining the ratio of wind, bile and phlegm Typologies that the person possesses from the one or more features of the DPW.
- the DPW is preferably generated by PPG signals which are output from the PPG sensor.
- the PPG sensor may be configured to be worn about one of the wrist, hand,fingers and ears of a person such that the PPG sensor is arranged so sense pulse at the wrist, hand,fingers or ears of the
- the sensor may be further configured to measure the movement of the person.
- the sensor may have one or more accelerometers which are operable to measure the movement of the person.
- the system may further comprise a control module which is configured to, receive the measured movement of the person, compare the measured movement of the person to a threshold movement value, and to selectively effect the generation of the DPW using a PPG signal output from the PPG sensor only if the measured movement of the person is below the threshold movement value.
- the control module can be further configured to receive a PPG signal output from the PPG sensor, and to calculate a quality index value which is representative of the quality of the PPG signal, compare the quality index value to a threshold quality index value and to a selectively effect the generation of the DPW using the PPG signal output from the PPG sensor only if the quality index value is greater than threshold quality index value.
- the system can further comprise a streaming unit which is configured to communicate wirelessly with a processing means for display and which defines means for determining said one or more features of the DPW and wherein the processing means further defines the mean for determining the ratio of wind, bile and phlegm typology that the person possesses from the one or more features of the DPW.
- the system can further comprise an analog display unit which is capable of showing the state of the person in a holistic way by means of mechanisms.
- the system may comprise a means with is configured to perform any of the steps mentioned in the above method methods.
- Fig. 1 a shown the position of the fingers of the traditional doctor when performing pulse reading
- Fig. 1 b shows the finger which are used to perform pulse reading
- Fig. 1 c show the mapping of organs to the different locations on the wrist of the patient;
- Fig. 2 provides a block diagram of aDPW system, according to an embodiment
- Figs. 3a and 3b show clusters, which result from principal component analysis in state space, which represent the Typology of the patient;
- Fig 3a show clusters derived from measurements taken on the left and right wrists of a patient and
- Fig 3b are clusters derived from the values of left minus right wrist measurements;
- Fig.4a shows ROC curves for QNN FP (FP refers to First Pass, see below for detailed explanations) with manual feature selection
- Fig.4b shows ROC curves for QNN FP with mRMR feature selection
- Fig. 5a shows the ROC curves for QNN (SP refers to Second Pass, see below for detailed explanations) with manual feature selection
- fig. 5b shows the ROC curves for QNN SP with mRMR feature selection
- Figs.6, 7 and 8 present feature statistics for a number of patients, for each Typology.
- Fig 9 provides a flow chart which summaries the steps involved in a method according to an embodiment of the present invention.
- Figs. 10a, b, and c each illustrate a flow diagram of the steps involved in a method according to an embodiment of the present invention
- Fig. 1 1 represents a qualitative correspondence between DPW and TTP measures, according to an embodiment.
- TTP descriptive quality
- T2D mapping how to translate a descriptive quality (the TTP) into a quantitative measure using the DPW features such as rising and falling transition time, amplitude, frequency and/or time-frequency content. This is called hereafter the T2D mapping.
- An automatic method for Typology assessment using digital signal processing and classification methods requires objective measures.
- Table 2 in Fig. 1 1 represents a qualitative correspondence between DPW and TTP measures.
- Table 2 there is also provided some archetypal qualitative pulse wave forms for each TTP measure.
- the goal for using TTP measures for the Traditional Vietnamese practitioner is to classify each of the Typologies and also to tentatively describe them in a qualitative verbal way. This is important to map the Typologies into some numerical measures
- This embodiment of the sensing system of the present invention is configured for single finger position measurement at a time.
- the sensor is configured to measure at the index finger position only (i.e. the position on the wrist of the subject at which the doctor's index finger would be positioned when performing TTP reading).
- the corresponding location is the one proximal to the thumb as can be seen in Figure 1 (a). This location is particularly suited to analyze the properties related to the heart, lungs, small and large intestines.
- the sensor is positioned on the radial artery in a similar way a Vietnamese doctor would sense the pulse until the signal shows some stability as displayed on the screen of the computer running the recording software. Once an optimal position has been found, the sensor is maintained with a wrist band during the duration of the recording. The subject is then asked to avoid moving, clinching the fist and eventually talking, while breathing normally and in a relaxed way.
- the DPW Recording Instrument The measure of the pulse wave should be done preferably using a pressure sensor. The reason for this is that the qualities of the pulse wave sensed by the doctor's fingers depends on the applied pressure and corresponds to different parts of the body. In Figure 1 the fingers distal from the thumb must apply more pressure and sense deeper tissues and lower organs.
- PPG Photo-Plethysmographic
- PPG is an optical noninvasive technology allowing the assessment of information related to subcutaneous blood circulation. By illuminating a living tissue with an infrared light source, PPG obtains estimates on both arterial pulsatility and arterial blood content. PPG measurements setups consist thus of a light source and a photo-receptor.
- the main advantage of this PPG-based DPW system is its potential for high integration in a small device, comfort and low cost which makes it attractive as a portable low-cost health monitoring system.
- the placement of the sensor can be preferably on the wrist radial artery or finger tips.
- the sensor can also be placed on the ear lobe or any suitable place where the pulsatile heart pulse wave can manifest.
- a block diagram describing the DPW recording system is provided in Figure 2.
- the system comprises an optical probe 10, an electronics box 13, and a laptop 14.
- the optical probe includes a Light Emitting Diode (LED) 1 1 or any adequate source of lightand a photodiode12or any photosensitive device.
- the DPW is generated from the PPG signal using analog filters, Analogue to Digital Converters and Digital filtering to remove wandering trends and high frequency content of the PPG signal.
- DPW qualities can be described with: Rhythmicity and Stability. Rhythmicity and Stability are explained
- Heart Rate Variability HRV
- Stability is linked with amplitude and frequency fluctuations or modulation of the pulse wave. This behaviour can be related also with the frequency modulation of the pulse wave, leading to AM/FM modulation as seen exemplarily in the so-called Respiratory Sinus Arrhythmia phenomena. Local vasoconstriction and/or dilation of the vessels can also manifest in amplitude modulation. Stabilitycan be partially described using Spectral Entropy (SE) and Undulation Level (UL) indices.
- SE Spectral Entropy
- UL Undulation Level
- Heart Rate Variability HRV
- SE Heart Rate Variability
- UL Undulation Level
- DPW Rhythmicity and Stabilityqualities are further linked with quantitative measures called DPW features and are summarized in the Table 3.
- the normalization of most of the features is preferred so as to have a well-conditioned feature space when it comes to a carrying out a classification step. Details of how normalization is carried out are provided in Table 4.
- the DPW(t) signal can be coarsely separated into two
- the HPW(t) is itself modulated by the respiratory system as discussed in the Stability quality, which contains both a nervous and mechanical origin.
- the BPW(t) is solely originating from the mechanical effect of breathing: i.e. the change of pressures in the vascular system and heart chambers due to the diaphragm and chest movements.
- the signal coming out of the DPW sensor contains a large DC offset and some slow drift. Additionally, it contains movement artefacts.
- the pre-processing thus comprises first using DC offset and slow drift removal in the first instance, and second applying a gentle linear filtering to reduce the effects of the movement artefacts still keeping the main features of the pulse wave. These two steps are described hereafter.
- Table 4 Normalization Principal Component Filtering with Frequency Selection
- the f i rst pre-processing is performed with a quadratic detrending, while the second uses a Principal Component Analysis in State Space (PCA-SS) method together with an additional frequency selection (PCA Freq).
- PCA-SS Principal Component Analysis in State Space
- PCA Freq additional frequency selection
- the maximum number of components to be used is limited to be the first 8 corresponding to the largest eigenvalues of the trajectory matrix, thus reducing the amplitude of the high frequencies. It is further selected from these components those for which the spectral content has a maximum between 0.05Hz and 12Hz. This choice of the frequency band corresponds to the physiologically plausible content of the heart and respiration three first main harmonics.
- the DPW(t) is subject to movement and muscle artefacts. These artefacts appear as brief signals of high amplitude and would bias the statistics which will be performed if they are not removed. These artefacts have a very different influence on the statistics than time- amplitude-uniform noise sources. These artefacts are thus removed using digital signal processing techniques.
- a wavelet based artefact detection and reduction methods could be used. Wavelet analysis is particularly well suited for artefact detection as it can precisely localize it in the time and scale domains simultaneously.
- the artefact shows up in some scales of a wavelet transform W as a set of larger components. In order to detect these scales contaminated by the artefact, an Artefactness degree is defined as follows:
- A(s) 2 Sigmas / (max(abs(s)) - min(abs(s)) (2) where Sigmas is the standard deviation of a signal s. It is guarantee that /4 ⁇ [0,1 ] with a value closer to 0 when the artefact is large and close to 1 when the artefact is small.
- x is the vector of all samples of x(t)
- cj is the vector of wavelet coefficients at scale j extracted using the operator S.
- a. is the artefactness of c.
- Theta(s,T) is the vectorial Heaviside function with input vector s and threshold T. We have used a hard threshold because we want to leave the artefact-free part of the signal unmodified.
- DPW Qualities All features are extracted using a rectangular sliding window of 30 s with 50% overlap. Normalization of the features is carried out and outlined in table 4.
- the pulse wave signal DPI l (f) displays fluctuations both in the frequency and amplitude domains.
- the fluctuations in the frequency domain are linked with known phenomena originating from the neuro- ardiovascular close-loop architecture with exogenous inputs such as the voluntary respiration or emotions.
- the variability of the pulse wave maximum peak location is linked to the Heart Rate Variability (HRV) which is defined as the variability of the peak electrocardiogram (ECG) R-wave time position (We say linked because the pulse wave as measured at the radial artery has been filtered from its original source in the left ventricle and contain phase distortions).
- HRV Heart Rate Variability
- ECG peak electrocardiogram
- the intervals signal of the pulse wave peak location is denoted PP similarly to the RR extracted from an ECG.
- the pulse wave maxima Pare extracted using a maxima detection (hill climbing).
- the Peak-to-Peak intervals PP are then computed and outliers (intervals outside the range [0.4,2]s) are removed.
- the classic measures of PP fluctuations are defined as: 1 ) the average pulse rate Rm, 2) the variance of the pulse rate Rv, 3) the Normalized Low Frequency Power (LF: frequency range 0.04 Hz to 0.1 5 Hz), 4) the
- HF Normalized High Frequency Power
- the fundamental properties of a rhythmic signal are displayed in its frequency spectrum as analyzed using a Fourier transform.
- the pulse wave contains a rich spectrum which is essentially related to the different functions of the cardiovascular and respiratory systems. Therefore two band-pass filters are applied to extract these 2 components prior to the spectral analysis.
- a Butterworth first order filters in the 2 frequency bands are used: 1 ) for the heart related subsignal HPW(t) in [0.6,2]Hz, and 2) for the respiratory subsignal BPW(t) in [0.1 ,0.4]Hz.
- D k H Sqrt(lntegral on ⁇ [Pt xha t(w) (w - 2 pi f k H )) 2 dw]) (7)
- Pt X hat(w) is an estimation of the energy-normalized power spectral density of the artefact reduced signalxhaift): i.e.
- Ptxhat(w) Pxhat(w) / Integral on ⁇ [ P xha t(w) dw] (8)
- Stability The concept of stability comes from the fact that both the PP and pulse wave amplitude naturally fluctuates in a multi-scale or/and chaotic way.
- ESE - Integral on ⁇ [ Pt xha t(w) log(Pt xha t(w)) dw]/ ⁇ (9)
- the ESE equation (9) resemble the classic Shannon entropy formula and is a measure of disorder of a signal in the frequency domain. Typically, narrow band signals will have a small entropy as compared to broadband signals.
- Time Domain StabilityAnother way to quantify the Stability is by using the Lempel-Ziv Complexity (LZ) to a symbolized PP series.
- LZ Lempel-Ziv Complexity
- the easiest way to get a symbol out of a continuous value is to quantize it.
- a standard quantization method based on 5 bits to obtain an integer value is used as a symbol for the LZ computation.
- the reason for using the LZ for pulse wave classification is that the pulse wave exhibits pseudo-periodic behaviour in certain Typologies and also when manifesting some disorder for which the LZ measure is very well adapted.
- Amplitude Domain Stabi/ityStability in the amplitude domain is
- the pulse wave is indeed modulated in amplitude due to various factors such as respiration, the autonomous nervous system or heart pacemaker
- Ehatx(t) EMD[ abs(Hilbert(x(t)))] (12) where Hilbertis the Hilbert transform operator and EMD is the denoising and detrending EMD operation. In order to avoid the complexity of the Hilbert transform, we can instead use a wavelet transform.
- the pulse wave is sensed and recorded on both wrists of the subject.
- left and right wrist shows different TTP characteristics is also found true in our DPW features.
- three sets of features are identified: 1 ) on the left hand, 2) on the right hand and 3) on the difference between the left and the right hand.
- Feature selection is a necessary step in order to reduce the input space dimension of the classifier and improve its robustness and
- Step 1 Features Selection using mRMR
- the first category there is for instance correlation based methods, principal and independent component analysis; while in the second, also called wrappers and embedded methods , support vector machines, naive-Bayes, decision trees and neural networks can be found.
- the second category is usually more time and resource consuming than the first.
- the first category is data- driven, fast and essentially based around a notion of separation of categories and thus a distance. The most common distance is the
- mRMR minimum Redundancy Maximum Relevance Feature Selection
- Step 2 Features Selection using Principal Component Analysisln order to further ease the work of the classifier, performed a Principal Component Analysis in State Space (PCA-SS) to the feature retained in Step 1.
- PCA-SS Principal Component Analysis in State Space
- FIG. 3(a) shows Feature Clusters after PCA-SS on the Left and Right hands and Figure 3(b) shows Left minus Right hand. These clusters have been obtained during a manual feature selection procedure as will be explained.
- feedforward neural networks which can handle uncertain inputs and have a very flexible structure of hidden nonlinear layer in the form of a superimposition of sigmoidal functions with flexible amplitude, slope and shift.
- the hidden layer can focus or relax its data representation by concentrating or spreading around regions of certainties or uncertainties of the feature space more like a quantum wave function localize or spread out around certain or uncertain states bearing some resemblance to quantum systems and networks.
- the nonlinear hidden functions have a multi-sigmoidal shape which gives rise to a soft staircase shape.
- the hidden layer sigmoid functions are here referred to as Quantum Jumps.
- the training algorithm of the QNN adapt both the linear input weights, the nonlinear output functions and all the parameters of the hidden layers.
- a gradient descend has been used.
- the stopping criteria for the training is based on a threshold on the normalized mean square error (NMSE).
- NMSE normalized mean square error
- the initial conditions are chosen at random in the input space and the training is iterated on batch until the minimum training NMSE reaches the threshold.
- forty surrogate PCA-SS forty surrogate PCA-SS
- the trained network is then validated using additional independent surrogated data. These surrogates are then used to compute the Receiver Operating Curves of the QNN in a Monte Carlo simulation.
- the ROC curve indicates the performance of the classifier in terms of True Positives
- the confusion matrix is also computed from these Monte Carlo simulations.
- the confusion matrix C of the QNN is given a
- the diagonal entries C 1 (2) are related to the percent of samples which are correctly classified in the respective Class 1 (2).
- C 2( D is the percentage of samples from Class 1 (2) which are classified as Class 2(1 ).
- C 2( D is the percentage of samples from Class 1 (2) which are classified as Class 2(1 ).
- QNN FP QNN First Pass
- the hidden layers are unbiased sigmoid functions of saturating value 1 and middle slope of 1 .
- the output layer has the same characteristics. By assumption Class 1 is assigned to Lung, while Class 2 is assigned tothe union of Tripa and Beken: (Tripa - Beken).
- the diagonal entries in C fP signifies that 90% ⁇ 2% of the samples are correctly classified in the Lung Class while and 75% ⁇ 2% are correctly classified in (Tripa - Beken).
- the off-diagonal entries in C fP have the is the percentage of samples from Lung which are miss classified as belonging to (Tripa - Beken), while ' is just the opposite.
- ROC curves are shown in Figure 4(a) and (b).
- Figure 4(a) shown curves which result from manual feature selection
- figure 4(b) show curves which result from mRMR feature selection.
- the transparent coloredarea in the ROC curve are drawn as the average ROC plus/minus the standard deviation computed from the Monte Carlo simulation. It can see that the QNN FP performs well under 10% False Positive for Lung and has difficulties increasing the True Positive. For (Tripa - Beken) the True
- mRMR Feature Se/ert/on Using the mRMR feature selection as presented the following confusion matrix can be obtained: Note that the feature clusters used in this mRMR are not those displayed in Figures 3(a) and (b) which are those used in the manual feature selection. The ROC curves are shown in Figure 4(b). The curves show a lower performance than with the manual feature selection, with still a similar performance as revealed by C fP . Following the separation of Lung from Tripa and Beken, next the separation of Tripa and Beken is performed.
- QNN Second Pass QNN SP .
- the hidden layers are unbiased sigmoid functions of saturating value 1 and middle slope of 1.
- the output layer has the same characteristics. By assumption Class 1 is assigned to Tripa, while Class 2 is assigned to Beken.
- the diagonal entries in C signifies that 94% ⁇ 2% of the samples are correctly classified in the Tripa Class while and 71 % ⁇ 2% are correctly classified in Beken.
- the off-diagonal entries in C fP have the following
- ROC curves are shown in Figure5.
- Figure 5(a) shows the ROC curves for QNN SP with manual feature selection
- figure 5(b) shows the ROC curves for QNN SP with mRMR feature selection.
- the ROC curve for Beken shows a good performance
- the one for Tripa shows a medium performance with 80% True Positives at the cost of 20% False Positives.
- mRMR Feature the mRMR procedure, we obtain the following confusion matrix:
- Fig 9 provides a flow chart which summaries the steps involved in a method according to an embodiment of the present invention.
- the first step involves taking measurements of the patient's pulse using a PPG sensor so as to obtain a digital pulse wave (140).
- a digital pulse wave is then generated using analog to digital converter circuits and filtering (141 ).
- features are extracted from the DPW and include the mean value ofintervalPP(Rm), normalised variance of PP(Rv), normalised enhanced undulation level of a heat pulse wave (EUL), normalised enhanced spectral entropy of a heat pulse wave (ESEh), Lempel- Ziv complexity of the persons heart pulse wave PW (LZrr) andnormalised breathing frequency (BF).
- HeartRate Variability indices in the frequency domain (LF and HF) the variance of the PPintervals, the breathing
- principal component analysis is performed on the selected features so as tobetter condition theinput space for further classification (144).
- Figs. 10a,b, and c each illustrate a flow diagram of the steps
- a photoplethysmograph (PPG) sensor 1 50 which is configured to be worn on the wrist of a patient is first secured to the wrist of the patient.
- the PPG sensor can take any other form that the one illustrated in Fig. 10 that is suitable for measuring the pulse at the wrist, hands, fingers orears of the person.
- the PPG sensor can take the form of a behind-the-ear, in-the-ear, etc., hearing aid types.
- PPG sensor 1 50 senses a pulse (161 ) of the patient and outputs a PPG signal which contains information regarding the pulse of the subject (151 ).
- the movement of the subject is measured (1 52) using one or more accelerometers (not shown) which are integral to the body
- PPG photoplethysmograph
- the movement of thesubject is continuously measured.
- the measured movement is compared to a threshold movement value (Ta) (1 58).
- the PPG signals output from thephotoplethysmograph (PPG) sensor 1 50 are only passed to the next stage in the process if the measured movement is less than the threshold movement value (Ta)).
- the measured movement of the person may also be passed (1 59) to be output on a holistic display (160).
- An indicator of the movement of the subject could be displayed on the display unit (160).
- Ta threshold
- the PPG signals output from the photoplethysmograph (PPG) sensor 1 50 are processed to determine their quality.
- a quality index value (Ql) (153) which is representative of the quality of the PPG signals is determined; and the quality index value (Ql) (1 53).
- the quality index value (Ql) is compared to a threshold quality index value (Tqi) (154). It will be understood that the quality index value could be displayed on a holistic display 160.
- the PPG signals output from thephotoplethysmograph (PPG) sensor 1 50 are passed for further processing only if the quality index value (Ql) is greater than the threshold quality index value (Tqi) (1 55). Further processing of the PPG signals will include generating a digital pulse waveform which is representative of the pulse of the patient; determining one or more features from the digital pulse waveform (the one or more features may include features such as pulse wave envelope, heart PP intervals, heart rate, heart rate variability indices in the frequency domain, breathing frequency and spectral entropy of the PP intervals). It will be understood that any of these parameters could be displayed on the holistic display 160.
- Further steps include using the one or more features to determine the heart condition, breathing condition and relaxing condition of the patient and displaying these values which are representative of these conditions on the holistic display. It will be understood that the further processing of the PPG signals may be carried out on an operating system and that the method may include the step of streaming PPG signals output from
- PPG photoplethysmograph
- the steps involves in the method illustrate in Fig 10b are similar to those of Fig 10a and like steps are awarded the same reference numbers.
- the one or more features are used to determine the ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology that the person possesses, and displaying the ratios on a holistic display 172.
- the holistic display can comprise a mechanical-based mechanism including hands,discs or the like, or an electronic display.
- the processing of the PPG signals to, generate a digital pulse waveform which is representative of the pulse of the patient, determining one or more features from the digital pulse waveform (the one or more parameters may include features such as pulse wave envelope, heart PP intervals, heart rate, heart rate variability indices in the frequency domain and spectral entropy of the PP) are carried out on an operating system 170 which includes a holistic display 172which is located remotely.
- the operating system is operable to determine the ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology that the person possesses and to display the ratio on the holistic display 172.
- Fig 10a the method illustrated in Fig 10a is used when it is desired to determine and display the heart condition, breathing condition and relaxing condition of the body and mind of a patient
- Fig 10b the method illustrated in Fig 10b is used when it is desired to determine and display theratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology that the person possesses.
- the method illustrated in Fig 10c is used to monitor a patient while sleeping.
- the method is used to determine and display theratio of wind (lung), bile (Tripa) and phlegm (Beken) typology that the person possesses while sleeping and also to determine and display theheart condition, breathing condition and relaxing condition of the body and mind of a patient.
- the movement of the patient is still measured using accelerometers, as in the method of Fig 10a, however the measured movement is not compared to a threshold movement value. Furthermore neither is a quality index, representing the quality of the PPS signals, determined and the PPG signals output from
- thephotoplethysmograph (PPG) sensor 1 50 are passed for further processing regardless of their quality.
- PPG photoplethysmograph
- a threshold movement value and threshold quality index could be used in this method in a similar fashion to the manner in which they are used in the methods of Fig 10a and Fig 10 b.
- Fig 10c has many of the same features as the methods illustrated in Figs. 10a and 10b and like features are awarded the same reference numbers.
- the PPG signals output from thephotoplethysmograph (PPG) sensor 150 are streamed (181 ) to an operating system 180 for further processing.
- the operating system 180 is configured touse the PPG signal to generate a digital pulse waveform which is representative of the pulse of the patient; determine one or more features from the digital pulse waveform (the one or more features may include parameters such as pulse wave envelope, heart PP intervals, heart rate, heart rate variability indices in the frequency domain and spectral entropy of the PP); and to determine the heart condition, breathing condition and relaxing condition of the patient, and the ratio of wind (lung), bile (Tripa) and phlegm (Beken) typology that the person possesses, using the one or more features.
- the operating system 180 is further used to display values which are representative of theheart condition, breathing condition and relaxing condition of the patient and to display the ratio of wind (lung), bile (Tripa) and phlegm (Beken) Typology that the person possesses, on a holistic display 182.
- any of the one or more features could be displayed on the holistic display 182.Thus the method illustrated in Fig 10c is employed to monitor the patient when the patient is sleeping, and heart condition, breathing condition, relaxing condition and the Typology of the patient is determined and displayed using this method.
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