Assessment of bilateral photoplethysmography for lower limb peripheral vascular occlusive disease using color relation analysis classifier

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 0 3 ( 2 0 1 1 ) 121–131

journal homepage: www.intl.elsevierhealth.com/journals/cmpb

Assessment of bilateral photoplethysmography for lower limb peripheral vascular occlusive disease using color relation analysis classifier Chia-Hung Lin ∗ Department of Electrical Engineering, Kao-Yuan University, No. 1821, Jhongshan Rd., Lujhu Township, Kaohsiung County 821, Taiwan

a r t i c l e

i n f o

a b s t r a c t

Article history:

This paper proposes the assessment of bilateral photoplethysmography (PPG) for lower limb

Received 15 July 2009

peripheral vascular occlusive disease (PVOD) using a color relation analysis (CRA) classifier.

Received in revised form

PPG signals are non-invasively recorded from the right and left sides at the big toe sites.

4 April 2010

With the time-domain technique, the right-to-left side difference is studied by comparing

Accepted 25 June 2010

the subject’s PPG data. The absolute bilateral differences construct various diminishing and damping patterns. These difference patterns in amplitude and shape distortion relate to

Keywords:

the grades of PVOD, including the normal condition, lower-grade disease, and higher-grade

Photoplethysmography (PPG)

disease. A CRA classifier is used to recognize the various patterns for PVOD assessment.

Peripheral vascular occlusive

Its concept is derived from the HSV color model and uses the hue, saturation, and value to

disease (PVOD)

depict the disease grades using the natural primary colors of red, green, and blue. PPG signals

Color relation analysis (CRA)

are obtained from 21 subjects aged 24–65 years using an optical measurement technique.

HSV color model

The proposed CRA classifier is tested using the physiological measurements, and the tests reveal its practicality for monitoring PPG signals. © 2010 Elsevier Ireland Ltd. All rights reserved.

1.

Introduction

PPG is an optical measurement technique that can be used non-invasively to detect blood volume changes in the microvascular bed of tissue. A PPG waveform comprised of alternating current (AC) and direct current (DC) components reveals physiological information, such as cardiac synchronous changes in the blood volume with each heartbeat, thermoregulation, respiration, and vasomotor activity [1,2]. Currently, optical technology, such as light emitting diodes (LEDs) and photo-transistors, have improved their size, sensitivity, and reliability. The technique is compact, low cost, non-invasive, and has a portable design for helping clinical instrument designs. It requires light sources (red or near infrared wavelength) to illuminate the skin, and a photo-

∗

detector to measurement the variations in light intensity with changes in blood volume. These variations have also been used in computer-based digital signal processing (DSP) and pulse waveform analysis. PPG pulse measurement has applied multi-channel data measurement at many different patients’ sites, such as the ear lobes, index fingers, thumbs, and big toes. Clinical applications include physiological monitoring, vascular assessment, and autonomic function in medical devices, including blood oxygen saturation, heart rate, blood pressure, and vascular diagnosis [3–5]. Therefore, the optical technique provides a promising solution for clinical patient monitoring. In the literatures, PPG pulses have been used in assessing cardiovascular diseases and can also provide valuable information about peripheral circulation. They can be acquired from the tissue of the ears, fingers, and toes. However, patient age and the body site affect the PPG characteristics. Many

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researches have examined this through the time-domain and frequency-domain techniques on the subjects. Literatures [6,7] show the age-related changes in PPG shapes at individual finger or toe sites through frequency analysis to verify a general reduction in the harmonic components of the pulse in older subjects. Literature [5], normalized ear, finger, and toe pulse shapes are computed, and the right–left pulse differences in shape relate to the oldest subjects. The overall age-related changes in multi-site PPG pulse shape characteristics have been quantified. Age-matched normal ranges can be considered to evaluate pulses from patients with possible vascular diseases. Literatures [8–11] have also quantified pulse transit timing changes with ages at various body sites. Many features have been examined, including PPG rise time, pulse transit time (PTT), amplitude, shape, and width/height ratio. These features can be provided to detect vascular disease, because peripheral pulse gradually becomes damped, delayed, and diminished. Literatures [12,13] have employed frequency analysis to estimate the resistance-compliance changes with disease. These reports reveal the correlation between changes and disease grades. Bilateral differences have been applied for preliminary PVOD assessment using ABPI (ankle-to-brachial pressure index, ABPI) classification [14,15]. This index can be divided into two classes, including ABPI ≥ 0.9 for normal condition (Nor), and ABPI < 0.9 for lower-grade (LG) and higher-grade (HG) diseases. The gold standard (63 healthy subjects and 48 vascular patients) has been established for bilateral PPG and PVOD assessment in earlier works [5,13,16,17]. However, an ABPI examination takes about 10 min, therefore limiting its use for routine vascular monitoring. It also limits the real-time applications, including physiological monitoring and automatic diagnosis function. Therefore, the absolute bilateral differences (the right-to-left side) of PPG signals are quantified for different grades in the time-domain, including healthy subjects, lower-grade (LG) and higher-grade (HG) subjects. Color relation analysis (CRA) is a flexible pattern mechanism to develop an automatic analytical tool without any complicated procedure, optimization technique, parameter adjustment, and inference scheme. CRA-based classifier is used to recognize the various bilateral difference patterns for PVOD assessment. Twenty-one subjects will be examined using the proposed classifier in the next section. It will show a promising method for implementation in the portable medical monitor.

2.

Problem description and motivation

PVOD is a widespread vascular disease and is also associated with an increased risk of coronary artery disease and stroke. PPG pulses for PVOD assessment can be described in various features, including timing, amplitudes and shape characteristics to represent transit delay increase, amplitude reduction, and distortion increase. Multi-site measurement techniques were used to study the age-related changes in the characteristics of PPG pulse shape at the ear, finger, and toe sites. The largest changes with age are seen at the ear and finger sites for the systolic rising edge region, and the finger site for the dicrotic notch region. The smallest changes with age are for

the toe site [5]. Therefore, the right-to-left side differences are studied by comparing the patient’s big toe data with the normative pulse ranges. Transit time is characterized in terms of timing in seconds, such as the transit time from ECG Rpeak to pulse foot (PTTf ) and to pulse peak (PTTp ) and the foot-to-peak rise time (RT), as shown in Fig. 1. The foot-topeak amplitudes (AMPs) are normalized between zero and one. The normative ranges of PTTf , PTTp , RT, and AMP are defined by their expected changes with the PVOD. In clinical investigations, the transit time and shape distortion increase with disease severity, and calibrated amplitude decreases in vascular diseases. The shape distortion is attributed to changes in pressure wave reflection and changes in the resistance properties of the peripheral arteries [7,12,13]. The AMP reductions are attributed to blood flow decrease in the vascular bed [16,18]. The above statements provide promising results and valuable information about PVOD detection. Therefore, bilateral difference measurements are proposed to simultaneously acquire PPG pulses from the right and left big toes. This is an important index for providing information to prevent leg pain, arteriosclerosis, and arterial disease. PPG pulses are synchronously induced at the right and left sites. When PVOD gradually becomes severe, the PPG pulses could become delayed, damped, and diminished with disease. Locating each ECG R-peak as a time reference, the pulse peak can be found within a certain window duration, including the systolic rising edge and dicrotic notch region (about 200 ms) as shown in Fig. 1 [13,16]. The timing characteristics (PTTf , PTTp , and RT), amplitude, and shape of the PPG pulse could be captured and analyzed for the bilateral legs. The rising edge of the pulse produce with systole, and the falling and notch edge can be seen with diastole in healthy compliant arteries. The rising edge is an interesting phase where blood pressure, combined volume and flow changes in arteries appear [14,15,19]. Also, peripheral resistance increases or peripheral is vascular occlusive, and the pulse wave change in shape and transit time will be prolonged. Then CRA performs the recognition tasks for PVOD assessment in computer-based signal processing and analysis, including three grades: normal condition, lower-grade and higher-grade disease. The concept of the proposed CRA classifier is derived from the HSV color model, which attempts to describe perceptual color relationships for grades of disease. HSV is simple transformation of device-dependent RGB, and stands for hue (H), saturation (S), and value (V) as shown in Fig. 2 [20,21]. Its model is commonly used in computer graphics applications, with H depicting a three-dimensional conical formation of the color wheel. The S is represented by the distance from the center of a circular cross-section of the cone, and V is adjusted with the brightness bar between black and white. H can be presented as an angle point, and the values of 0◦ , 120◦ , 240◦ stand for the color red, green, and blue, respectively. For pattern recognition, CRA is applied to develop a classifier for PVOD assessment. It has a flexible pattern mechanism with add-in and delete-off features without parameter adjustment, and does not demand strict statistical methods and inference rules. In this study, H is employed to identify the grade of disease, and S and V are the confidence index for the recognition results. These two parameters could enhance the reliability of the diagnostic results. For twenty-one subjects, the tests

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Fig. 1 – ECG signal and PPG pulse landmark recognition.

Fig. 2 – HSV color space model.

will show computational efficiency and accurate recognition of PGG signals.

3. Mathematical design-color relation analysis The research indicates the time-domain and frequencydomain features contain valuable diagnostic information for PVOD assessment. The bilateral difference determines the normative ranges of PPG pulse characteristics at the big toes, and compared patient pulse data to assess the grade of disease according to the differences in pulse timing, amplitude,

and shape. Cross-correlation [4,5,10,11], frequency analysis [6,7,18,22], artificial neural network (ANN) [23,24,19], and wavelet analysis [25] have proposed a decision method. Frequency or wavelet analysis has to choose and determine the parameters. The transformation processes can be regarded as a filter with suitable frequency bandwidths for feature extraction at specific parameters. However, the PPG pulse characteristics could be difficult to describe by mathematical transformation. The ANN method can allow non-linear classification, and a pulse shape classifier has been developed for lower limb arterial disease detection with a larger group of subjects. The local minimum problem, slow learning speed, design determination, and the weight of interferences between different patterns are the major drawbacks. Pattern recognition is a field of research studying the operation systems that can classify patterns in data. Its fields include discriminated analysis, feature extraction, error estimation, cluster analysis, and syntactical pattern recognition. Important application areas are image analysis, character recognition, speech analysis, man and machine diagnostics, and person identification. Pattern recognition aims to classify data/patterns based on behavioral characteristics, or on statistical information extracted from the patterns. These patterns to be classified are usually groups of measurements or observations, defining finite points in a suitable multi-dimensional space. Artificial intelligence methods are concerned with the classification or description of observations. Based on similarity and dissimilarity, relational measurement is a method for determining the relationship between the reference pattern and other comparative patterns. Color relation analysis (CRA) is conducted to

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classify patterns. Assume a reference pattern ˚r (0) = [ϕ1 (0), ϕ2 (0), ϕ3 (0), . . . , ϕi (0), . . . , ϕn (0)], and K comparative patterns ˚c (k) = [ϕ1 (k), ϕ2 (k), ϕ3 (k), . . . , ϕi (k), . . . , ϕn (k)], k = 1, 2, 3, . . . , K, can be represented as [26,27] ˚r (0) = [ ϕ1 (0)

⎡

⎤

ϕ2 (0)

⎡

˚c (1) ϕ1 (1) ⎢ ˚c (2) ⎥ ⎢ ϕ1 (2) ⎢ . ⎥ ⎢ . ⎢ . ⎥ ⎢ . ⎢ . ⎥ ⎢ . ⎢ ⎥=⎢ ⎢ ˚c (k) ⎥ ⎢ ϕ1 (k) ⎢ . ⎥ ⎢ . ⎣ .. ⎦ ⎣ .. ˚c (K) ϕ1 (K)

··· ϕ2 (1) ϕ2 (2) .. . ϕ2 (k) .. . ϕ2 (K)

ϕi (0) ··· ··· .. . ··· .. . ···

··· ϕi (1) ϕi (2) .. . ϕi (k) .. . ϕi (K)

ϕn (0) ] ··· ··· .. . ··· .. . ···

(1)

⎤

ϕn (1) ϕn (2) ⎥ .. ⎥ ⎥ . ⎥ ⎥ ϕn (k) ⎥ .. ⎥ ⎦ . ϕn (K)

(2)

Fig. 3 – Euclidean distances versus gray grades.

where n is the dimensional space in one pattern; K is the number of comparative patterns. Compute the absolute deviation of the reference pattern ˚r (0) and k comparative pattern ˚c (k) by ϕi (k) = |ϕi (0) − ϕi (k)|

(3)

Nor, LG, and HG for PVOD assessment, and can be represented as  = [Nor (1), Nor (2), . . . , Nor (t), . . . , Nor (NNor )|LG (1), LG (2), . . . , Nor (t), . . . , LG (NLG )|HG (1), HG (2), . . . , Nor (t), . . . , HG (NHG )]. Compute the average grade for each class, as

Compute the index ED(k), as

 n  2 ED(k) =

(ϕi (k))

Nor ave

=

(4)

i=1

LG ave

where ED(k) is the Euclidean distance (ED) between two patterns ˚r (0) and ˚c (k). If pattern ˚r (0) is similar to any comparative pattern ˚c (k), the index ED(k) will be small values. These indexes are used in CRA to measure the relationship between the reference pattern and comparative patterns. This concept can be used for analyzing pattern relations. In this study, a mathematical method is designed to assess peripheral vascular occlusive diseases. According to the anklebrachial pressure index (ABPI) vascular assessment, three agreement classes are used to assess the conditions for bilateral leg comparisons, including “normal condition (Nor)”, “lower-grade disease (LG)”, and “higher-grade disease (HG)” [13]. Overall indexes ED(k), k = 1, 2, 3, . . . , K, are converted to gray grade (k) by non-linear transformation, as (k) =  exp[−ED(k)]

NNor

(5)

where  is the recognition coefficient with interval (0, ∞). Eq. (5) is used to enhance the contrast in indexes ED(k). Intensity adjustment is a technique for mapping an original intensity value to a new specific range. The range of the gray grade (k) is in the interval [0, ], as shown in Fig. 3. The coefficient  is selected to 1 to make the gray grades more distinguishable,  = 5 is chosen in this study. Gray grade (k) satisfies the following four properties: normal interval (k) ∈ [0, ], dual symmetry, wholeness, and approachability [27,28]. Gray grade (k) can be used to measure the degree of similarity between the reference and comparative patterns when ED(k) = 0 represents the largest similarity. No matter how large ED(k) is, the range of the gray grade is in the interval [0, ]. These gray grades can be separated into three classes

t=1

HG = ave

(6)

NNor

NLG =

Nor (t)

t=1

LG (t)

(7)

NLG

NHG t=1

HG (t)

(8)

NHG

where NNor , NLG , NHG are the number of class Nor, class LG, and class HG, respectively. Then find the minimum and maximum average grades, as

Nor LG HG , ave , ave min = min ave



Nor LG HG , ave , ave max = max ave

(9)



(10)

/ max . Convert three average grades to primary where min = color grades r (red), g (green), and b (blue), as

r=

Nor Nor max − ave max − ave = max − min 

(11)

g=

LG max − ave 

(12)

b=

HG max − ave 

(13)

According to the HSV color model, CRA are defined mathematically by transformations between the RGB color space and the HSV color space, as shown in Fig. 2. Grades r, g, b ∈ [0, 1] are the red, green, and blue coordinates in the RGB color space [21,22]. Find the hue angle h ∈ [0◦ , 360◦ ] for the HSV color space, and it

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can be calculated as

h=

⎧    ◦ × g − b + 360◦ mod 360◦ , Nor ⎪ 60 if max = ave ⎪ ⎪  ⎪ ⎪ ⎨   60◦ ×

b−r

+ 120◦ ,

if max = LG

ave  ⎪ ⎪ ⎪ r − g ⎪ ⎪ HG ⎩ 60◦ × + 240◦ , if max = ave

(14)



Find saturation s and value v, which are defined as v = max s=1−

min v

(15) (min = / max , max = / 0)

(16)

The value of h is generally normalized to lie between 0◦ and 360◦ , and hue h has no geometric meaning when min = max and saturation s is zero. For PVOD assessment, parameter h is used to identify the three classes, which are the red-series color for “Nor”, green-series color for “LG”, and blue-series color for “HG”, respectively. Parameter s is used to judge the possibility. If its value is greater than 0.5 and approaches 1.0, we have high confidence to confirm the possible class.

LED converts electrical energy into light energy and has a narrow single bandwidth. Then, the photo-detector converts light energy into an electrical current and connects to a transimpedance amplifier and filter circuits. High-pass Butterworth filters are used to remove and reduce the size of the DC component. A high-pass filter can enable the AC component to be boosted to a nominal 1 V peak-to-peak level [11]. This PPG instrument has a long operating life and rapidly acquires the PPG signals. PPG signals were collected from 21 subjects in the hospital (Tainan, Taiwan). These subjects aged 24–65 years were divided into three groups, including Nor, LG, and HG, as shown in Table 1. In PPG measurement, signals may be disturbed by posture, relaxation level, room temperature, and other environment factors. Each subject was asked to lie supine on a couch in a temperature-controlled room (25 ± 1 ◦ C). With bilateral difference measurement, two PPG probes were placed at the right and left big toe, respectively. The PPG signals were captured at a sampling rate of 1 kHz for 15 min, and NI DAQ card (National Instruments DAQ Card, 16 channels, 1.25 ms/s) is an analog-to-digital (A/D) converter between the optical measurement system and a computer.

4.2.

4.

PPG features extraction

4.1.

Physiological measurement

An optical measurement technique [11,20] is used to acquire PPG signals operating in reflectance mode, consisting of light sources, photo-detector, trans-impedance amplifier, filter circuits, amplifiers, and interface, as shown in Fig. 4. The light source and the photo-detector are positioned side by side, the so-called reflection mode. The light is directed down into the skin and is backscattered from the skin adjacent to the photodetector, and allows measurement of virtually any skin area [19]. A choice of wavelength used in the light source is 940 nm (near infrared, spectral bandwidth: 45 nm, forward voltage: 1.2 V), in which there are large differences in the extinction coefficients of deoxyhaemoglobin and oxyhaemoglobin [29].

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Bilateral difference patterns

Vascular occlusive and resistance gradually increase, the PPG pulse will change in shape and transit time will be prolonged, as seen in the interval between dash-lines in Fig. 5(a). PPG pulses are asynchronously at the right and left sites. Locating each pulse peak as a reference, the pulse peak can be found within a certain window duration, including the systolic rising edge and dicrotic notch region (about 200 ms). With the center on the pulse peak, the resampling process performs to obtain n sampling data around the pulse peak (n/2 points before and n/2 points after). The larger value of n provides better accuracy at the expense of more computation time. Therefore, 50 sampling data are chosen in this study, as shown in Fig. 5(c). The bilateral difference can be represented as ϕi (0) = |xRi − xLi |,

i = 1, 2, 3, . . . , n

Fig. 4 – Block diagram of data capture and signal processing procedure.

(17)

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Table 1 – Related Data for twenty-one subjects. Grade 1 2 3

Agreement class

The number of subject

Normal condition (Nor) Lower-grade disease (LG) Higher-grade disease (HG)

9 8 4

where xRi and xLi are the sampling data from the right and left PPG signals, respectively. The difference pattern can be calculated along all n sampling points, as ˚r = [ϕ1 (0), ϕ2 (0), ϕ3 (0), . . . , ϕi (0), . . . , ϕn (0)]. Fig. 5(c) shows the various difference patterns for Nor, LG, and HG. The normalized maximum height ranges of difference patterns are 0.00–0.10, 0.10–0.25, and >0.25, respectively. The amplitude of damping patterns increases with disease severity. According to theses patterns, we can systematically create comparative patterns with Eq. (2). Various damping patterns are obtained for each subject by averaging the bilateral differences from 10 consecutive good-quality PPG pulses at least two times [5]. The number of comparative patterns equals the 42-set data (K = 42), as shown in Table 1. These patterns can be divided into three groups, and the numbers of patterns from

The number of comparative patterns 18 16 8

the same groups are 18-set (NNor = 18), 16-set (NLG = 16), and 8-set (NHG = 8) data, respectively. CRA is used to identify the grade of the disease, and the possible grade can be presented as an angle point h by Eq. (14). The Matlab colormap function is used to display the results in the HSV color space. The values of s and v are in the interval [0, 1]. In the HSV color space, with v at maximum, parameter s will approach 1 as Eqs. (15) and (16). A threshold value 0.5 is designed for parameter s to separate positive (P) and negative (N). The saturation s is between 0 and 1, where a value close to 1 means agreement to a certain grade for “positive (P)”, and a value close to 0 means “negative (N)”. Given a assessment result defined by the (h, s, v) values in the HSV space, with h as Eq. (14), and with s and v varying between 0 and 1, a corresponding (r, g, b) triplet in RGB space can be calculated as [20,21]

Fig. 5 – (a) Bilateral PPG pulses at the right and left great toes. (b) Bilateral PPG features in the systolic rising edge and dicrotic notch edge. (c) Difference patterns for normal condition (Nor), lower-grade (LG) disease, and higher-grade (HG) disease.

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hc =

h

PTTp = 19.6 ms, and RT = 14.6 ms fall into the expected normal range 0.3–7.4 ms, 0.4–22.3 ms, and 1.3–15.6 ms, respectively. By comparing the bilateral differences, the timing parameters PTTf and PTTp , and RT increase with disease severity. Therefore, three parameters could be used to estimate the PVOD grades. According to standard ranges, two subjects could confirm the LG and HG diseases from the preliminary assessment. Despite ABPI measurement can predict death rate and cardiovascular diseases in high-risk patients [13]. Actually, it is difficult and non-objective for evaluating lower limb PVOD only referring to ABPI. Techniques like arterial Doppler [30] and magnetic resonance angiography [31] are also valuable in clinical examinations. As shown in Table 2, the value of ABPI was obtained from one of patients, but it was not the only parameter for setting the rules of classification. The major rule of classification for the 3 groups is based on the comments of professional physicians, supported by many examinations, in addition to the ABPI. The measurement takes several times to examine and has limitation for routine screening in a primary care setting. For bilateral-timing differences, each ECG R-peak was used as the timing reference for obtaining bilateral-timing parameters. However, timing reference might be disturbed by heart-rate variation and asynchronous measurement in parameters extraction. A significant diagnostic tool should be low-cost, non-invasive, quick, and simplicity technique, thus, CRA-based classifier could offer a significant automatic diagnostic tool for asymptomatic patients to prevent amputation and gangrene in the lower limb, as detailed next section.

(18)

6

where hc might be a suitable parameter for use in computer graphic application and human user interfaces. The colors begin with red-series, passes through green-series, blue-series, and return to red. The colormap function will used to display the angle hc as colors in the Matlab workspace.

5.

Experimental results and discussion

The proposed CRA-based classifier was developed on a PC Pentium-IV 3.0 GHz with 480MB RAM and Matlab software. Matlab workspace (MathWorks Inc.) is a well-known signaland image-processing tool, and is used to examine the prototype algorithm. The performance of the proposed method was tested for diagnostic accuracy on the data set, including normal condition, LG, and HG groups. The number of each group is 9, 8, and 4, respectively, and the ages ranged from 24 to 65 years.

5.1.

Preliminary PVOD assessment

In clinical applications, the ABPI is calculated using the highest of the right and left ankle systolic blood pressure divided by the highest of the right and left arm brachial systolic blood pressure [14,15]. The PPG analysis extracted timing parameters using the bilateral differences, such as PTTf , PTTp , and RT. Table 2 shows the preliminary PVOD assessment in ABPI and the ranges of bilateral-timing differences for three groups. In our selected cases, the APBI indexes are ABPI ≥ 0.9 for healthy subjects and diabetic subjects with LG disease, ABPI < 0.9 for diabetic subjects with HG disease. The severe subjects (ABPI < 0.5) had poor wound healing and had gangrene in the lower limb. Clinical examinations were confirmed by professional physicians. For example, a healthy subject, average bilateral differences PTTf = 5.0 ms,

5.2.

PVOD assessment with CRA classifier

To examine the proposed CRA-based classifier in the operating mode, the above-mentioned subjects are also conducted to test using the physiological measurements. For example, eleven PPG signals for three subjects are shown in Fig. 6. Because the pulse peak is very distinct, it can be found within

Table 2 – The bilateral differences in ankle-brachial pressure index (ABPI) and parameters PTTf , PTTp , and RT. Parameter

Subject category Assessment grade ABPI ≥ 0.9

PTTf (ms) Mean Min–Max PTTp (ms) Mean Min–Max RT (ms) Mean Min–Max

Testing subject ABPI < 0.9

ABPIR : 1.1391 ABPIL : 1.1382

ABPIR : 0.8941 ABPIL : 1.0817

ABPIR : 1.0422 ABPIL : 0.8945

Healthy subject

LG subject

HG subject

Normal

LG diabetic patients

HG diabetic patients

2.58 0.3–7.4

9.2 5.1–23.7

29.2 23.6–34.8

5.0

23.6

23.6

7.3 0.4–22.3

22.6 14.3–56.5

52 46.2–57.8

19.6

33.3

57.8

6.84 1.3–15.6

14.6 3.4–32.3

23.4 11.5–35.3

14.6

32.8

35.3

Note: The values of PTTf , PTTp , and RT are absolute values. (1) PTTf = |PTTRf − PTTLf |, (2) PTTp = |PTTRp − PTTLp |, (3) RT = |RTR − RTL |, where suffix words R and L are defined right and left legs, (4) these investigations were confirmed by a professional physician.

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Fig. 6 – The great toe PPG signals and difference patterns for the normal and PVOD subjects. (a) The great toe PPG signals from healthy subject and difference patterns with normalized maximum height range less than 0.1. (b) The great toe PPG signals from LG subject and difference patterns with normalized maximum height range between 0.1 and 0.25. (c) The great toe PPG signals from HG subject and difference patterns with normalized maximum height range greater than 0.25.

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function displays the hue angles as a red-series (average hue angle = 58.96◦ ), green-series colors (average hue angle = 24.36◦ ), and blue-series colors (average hue angle = 40.37◦ ), respectively. The saturation s is a confidence degree to assess the diagnostic results. The average saturation values (confidence degrees) are 0.9592, 0.8552, and 0.9698, where values greater than 0.5 means agreement to a certain grade for “Positive (P)”. Through the experimental tests, the CRA-based classifier promises results with high accuracy and 100% sensitivities for three groups. In the observation, the second and third subjects were diabetic patients with LG and HG disease. If their treatments are not adequately controlled, serious complications include cardiovascular disease and nerve damage. The poor wound healing can lead to gangrene and also to amputation especially in the lower limbs. Therefore, the proposed method appears to estimate the risk grades for further clinical diagnosis.

5.3.

Fig. 7 – Detection results for healthy subject and PVOD subject. (a) Detection results of healthy subject. (b) Detection results of LG subject (diabetic patient). (c) Detection results of HG subject (diabetic patient).

the movable window with each shift in time. Centering on the pulse peak, 50 resampling data can be obtained around the pulse peak. Then the bilateral difference can be calculated as seen in Fig. 6(a)–(c). Using 100 PPG signals (about 1.5 min long), the diagnostic results for three subjects are shown in Fig. 7(a)–(c). Matlab colormap function is used to convert the hue angles hc to the RGB colors. These results confirm the major grades are “Nor”, “LG”, and “HG”, and the colormap

Performance comparison

Table 3 compares the performances of the proposed method and the traditional ANN classifier. The network design of traditional ANN is 50-27-3, including 50 input nodes, 27 hidden nodes, and 3 output nodes. Outputs represent three grades defined as ONor , OLG , and OHG , respectively. Each one lies between the minimum and maximum value. The maximum one indicates the possible grade. For the same testing data, the results show the proposed method does better than an ANN classifier. ANN has some limitations including very slow learning process (back-propagation learning algorithm), iteration requirement for updating network weights, parameters assignment (initial network weight, learning rate, Convergent Condition), and the need to determine the network design, such as the number of hidden layers and hidden nodes. There are two general conditions to terminate the training stage: (1) the objective function (OF) is less than a pre-specified value or (2) the number of iterations achieves the maximum number [27]. Tuning the parameters can improve the detection accuracy by using the optimum method. However, as the number of training data increases, the training process, reliability, and classification efficiency become the main problems. The CRA uses straightforward mathematical operation to process numerical computation, and expandable or reducible patterns without parameter adjustment and assignment. It is an adaptive pattern mechanism, and the database is continually formed for comparative patterns added or deleted to the current matrix of comparative patterns. It takes about 1.6093 s to recognize 100 PPG signals. The proposed method requires less parameter assignment, and needs no iteration process to adjust the parameters. Only the recognition coefficient needs to choose, and is selected to 1 to make the difference more distinguishable. The outcomes of the proposed classifier are better than an ANN classifier. To develop an automatic diagnostic tool, the proposed method can be further integrated in telemedicine and portable non-invasive devices. It can be used to acquire patient’s information in home healthcare, remote non-clinical environments, and elderly communities.

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Table 3 – The elated data of CRA classifier and traditional ANN classifier. Task

Method CRA classifier

Network architecture

No

Database Activation function Learning algorithm Parameter assignment

Comparative patterns Eq. (5) No Minor Recognition coefficient (0, ∞) No

Parameter adjustment Adaptation capability

Result display

Traditional ANN classifier According to input–output data and experience formulas Note (1), (2), (3) Input–output pairs training data Sigmoid activation function [28] Back-propagation learning algorithm Major Whole network weights and learning rate (0, 1) Training iteration OF < 10−3 or Itr < 500 Moderate Retrain with new training patterns Numerical data

Good Expandable or reducible patterns Colors and numerical data Nor

Training patterns

HG

Nor

LG

HG

Average hc = 58.96◦ Average s = 0.9592

Average hc = 24.36◦ Average s = 0.8552

Average hc = 40.37◦ Average s = 0.9698

Average ONor = 0.8868

Average OLG = 0.8562

Average OHG = 0.8987

Note: (1) NH = (NI + NO )1/2 ; (2) NH = (NI + NO )/2; (3) NH = (NI + NO ). NH : the number of hidden node; NI : the number of input node; NO : the number of output node. (4) Itr: the maximum allowable number [27].

6.

Conclusion

A CRA-based classifier has been proposed to detect PPG signals for lower limb PVOD assessment. Optical technology is used for non-invasive measurement operating in reflectance mode at the skin surface. Its device is employed to acquire PPG signals at the right and left big toes. The bilateral features, the so-called bilateral difference patterns, can be calculated with the right and left PPG signals. These patterns in amplitude and distortion shape relate to the disease grades, and the amplitude of difference patterns increases with disease severity. The proposed method uses these distinctive features to estimate the grades of PVOD. It has a flexible pattern mechanism with add-in and delete-off features without adjusting many parameters. The results can be presented in colors as red, green, and blue in computer graphics application. The saturation parameter is a confidence degree used to confirm the possible grade. For real life applications, an optical technology and telemedicine have been commonly used in biomedical engineering. Its advantages of low-cost, non-invasive, and short design-cycle become essential to develop a bio-signal monitor. The proposed method and optical-based measurement provide a promising way of further implementing the portable bio-monitor and telemedicine.

Acknowledgments The author would like to thank Dr. Yi-Chun Du, the Institute of Biomedical Engineering, National Cheng-Kung University,

and professional physician Dr. Chian-Ming Lee for providing his valuable suggestion and their help on experiments.

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