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 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 4  |  Issue : 1  |  Page : 35-45

Pulse wave analysis for cardiovascular disease diagnosis


Department of Applied Computer Science, The University of Winnipeg, Winnipeg, Canada

Date of Web Publication18-May-2018

Correspondence Address:
Simon Xin Meng Liao
Department of Applied Computer Science, The University of Winnipeg, 515 Portage Avenue, Winnipeg
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/digm.digm_2_18

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  Abstract 


Background: In this research, the pulse wave data of 274 subjects from both the patient and control groups are evaluated and analyzed. Based on the pulse wave analysis of those subjects, a study of diagnosing cardiovascular diseases is conducted. Methods and Results: By investigating the correlation between the cardiac indices Reverse Shoulder Index (RSI) and Ratio of Distance for patients with cardiovascular diseases from different age and gender groups, several common and important observations are reported. By carrying out case studies, we have verified some of our findings with several patient cases. Conclusion: In this research, pulse wave analysis is applied for the study of cardiovascular diseases with some important observations. We expect that our discoveries in this research can eventually help the end-users in cardiovascular diseases diagnosis.

Keywords: Cardiovascular diseases, pulse wave analysis, reverse shoulder index, subendocardial viability ratio


How to cite this article:
Xia J, Meng Liao SX. Pulse wave analysis for cardiovascular disease diagnosis. Digit Med 2018;4:35-45

How to cite this URL:
Xia J, Meng Liao SX. Pulse wave analysis for cardiovascular disease diagnosis. Digit Med [serial online] 2018 [cited 2018 Sep 19];4:35-45. Available from: http://www.digitmedicine.com/text.asp?2018/4/1/35/232706




  Introduction Top


Examining someone's pulse from their wrist has been a main diagnosis method in China since about 2,500 years ago. In the 3rd century, Mai Jing, a Chinese book that categorized pulses into 24 types was the first literature about the pulse.[1] The foundation of modern pulse wave analysis was established by Frederick Akbar Mohamed in 1870.[2] He also contributed to the development of arterial pulse wave measurement devices.[3] Since the 17th century, physicians and scientist have made great efforts on understanding the significance of pulse waves, which can be measured non-invasively, and their relationships with ages, genders, and various kinds of diseases, especially cardiovascular disease.

In this research, we have evaluated and analyzed the pulse waves of 274 subjects from both the patient and control groups. With the information acquired from the original pulse wave signal data, we have conducted a study of diagnosing cardiovascular diseases from pulse wave analysis. Some common and important observations of the correlation between the cardiac indices obtained from pulse waves and cardiovascular diseases are reported. Finally, by carrying out case studies, we have also verified some of our findings in this research with several patient cases.


  Materials Top


Device

In this research, all pulse waves were collected and measured using a noninvasive infrared pulse sensor, HKG-07C, as shown in [Figure 1]. The device is made by Anhui Huake Electronic Technical Research Institute in China. The finger clip with an infrared sensor is shown on the left, and the USB interface to a computer is on the right in [Figure 1].
Figure 1: HKG-07C infrared pulse sensor. Original courtesy of Anhui Huake Electronic Technical Research Institute, China

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The infrared sensor was applied to the right index finger, and the device used the infrared transmission to measure arterial pressure in the finger. During the process, to keep the diameter of the artery constant, the index finger was firmly clipped by the device as illustrated in [Figure 2].
Figure 2: Index finger is firmly clipped by the device

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The volume changes in arterial pressure would be detected by the infrared sensor. Meanwhile, the device outputs synchronization pulse signals to a computer.[4] The sample rate is 200 Hz. The device is ultraportable and can fit both children and adults. [Figure 3] shows a sample of a pulse wave recorded by an HKG-07C device.
Figure 3: A sample of pulse wave recorded by the infrared pulse sensor HKG-07C

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Subjects

Pulse waves of 274 participants were recorded between August 2008 and August 2010.[5] Among these participants, 169 of them were traced in the Department of Cardiology at Shandong Provincial Hospital in China, and are classified as the patient group. The remaining 105 participants were collected randomly outside the hospital within the same period and are assigned to the control group.

The general information of those two groups is shown in [Table 1]. It includes the numbers of participants, mean age, mean weight, and mean height. The mean age of the patient group is approximately 8 years older than that of the control group. Note that, the age range of the patient group is from 7 to 96 years old. For the control group, ages are ranged from 8 to 92. The average weight and height of both groups are similar.
Table 1: General information of participants in both groups

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Parameters used in pulse wave analysis

Since pulse wave analysis is a technique that consists of accurately recording blood pressure pulse waves and analyzing its different components,[6] some key parameters, such as the systolic peak, reflected point, and the dicrotic notch will need to be identified first.

[Figure 4] is a sample of a pulse wave recorded from an aorta.[7] It shows the main parameters used in pulse wave analysis. Systolic pressure is the peak of the pulse wave, and diastolic pressure is its trough. Pulse pressure (PP) represents the changes between systolic and diastolic pressure. Augmentation pressure is the increased pressure due to the earliness of backward wave, which is added to the forward wave. Dicrotic notch represents the end of the ejection period, also known as the end of systole.
Figure 4: Aortic pulse wave with its main parameters

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Among all of the parameters, the reflection point, which is also called the inflection point, is regarded as the most important element that characterizes systolic pulse waves during ejection. It is the point where forward and backward waves meet. It is “shoulder-shaped” when it appears on the pulse wave.[8]

Dynamic curve fitting

The purpose of applying dynamic curve fitting on pulse wave signals is to express them with functions. Then, mathematical calculations can help us find important elements in the signals for further analysis.

Dynamic curve fitting is an iterative converging process to find the best solution. It starts with a guess at the coefficients and keeps modifying the coefficients until the best fit is reached.[9] Among all of our experimental tasks carried out, we found that the following sum of sine functions with eight terms provide the optimal results.[10]



Where a, b, and c are coefficients for determining the best fit of the original pulse wave signal in the iterative process.

[Figure 5]a shows the fitting curve that connects the pulse wave points into a continuous pulse wave. [Figure 5]b illustrates the residuals between the fitting curve and the original data. The root mean square error (RMSE) of the curve is 0.007302. The average RMSE of all fittings curves recorded in this research is <0.01.
Figure 5: Sub-figure (a) shows the fitting curve of the original data, and (b) illustrates the residuals between the fitting curve and the original data

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  Methods Top


Systolic phase analysis

Systolic phase analysis focuses on the reflected point. In this section, we will calculate the reverse shoulder index (RSI), which is a parameter providing an indication of the incidence of reflected waves on the total PP.[8] We will also evaluate the ratio of distance, which is derived by dividing the distance between the reflection point and systolic peak by the length of the whole pulse wave. Since the pulse wave data are collected from the peripheral artery, the methodology addressed in O'rourke [11] is applied.

Calculation of reverse shoulder index and ratio of the distance

As illustrated in [Figure 6], RSI is generated by dividing the pressure at the second shoulder minus the pressure at the wave foot, P2, by the pressure at the first shoulder minus the pressure at the foot of wave, P1:
Figure 6: Definition of reverse shoulder index

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Since the heart rates of different participants are diverse, the lengths of the pulse wave of each participant are uneven. To be more precise, the ratio of distance is taken into account:



Where d and D are shown in [Figure 7].
Figure 7: Ratio of distance

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Experimental results of applying systolic phase analysis

In the patient group, two shoulders are successfully evaluated for 127 out of the total of 169 participants. Among these 127 participants, 106 participants are regarded as Type 1, whose first shoulder is a systolic peak and the second shoulder is a reflected point, while 21 participants are labeled as Type 2, whose first shoulder occurs before the systolic peak. For the control group, two shoulders are detected for 78 out of the 105 participants, whereas 70 participants are classified into Type 1, and the remaining 8 are regarded as Type 2.

[Table 2] shows the average values of RSI and ratio of the distance of the patient group for both types.
Table 2: The average values of reverse shoulder index and radio of distance for the patient group of both types

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The results show that RSI and ratio of distance are correlated with the ages of the participants. [Table 3] shows the RSI and ratio of distance of the participants belonging to Type 1 in the patient group, whose ages range from 50 to 90. Since the number of participants aged from 0 to 49 is small and the distribution of ages is uneven, they are not taken into consideration and leaving a total of 88 participants in this category.
Table 3: Reverse shoulder index and ratio of distance related to age (Type 1) in the patient group

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From [Table 3], we can observe that when ages increase, RSIs increase, while ratios of the distance of the participants decrease. The higher the RSI value, the larger the absolute amplitude between two shoulders is. A higher ratio of distance implies a longer distance between two shoulders.

We have compared the two parameters from females and males of both groups in this research. The results illustrated in [Table 4] indicate that RSI of females is higher than that of males for both groups and types. This result is consistent with the conclusion in Salvi.[8] While the range and mean of the different age groups are close, RSI of females goes up to 77.33 and 118.33 in different types of the patient group. For the control group, the values reach 72.94 and 121.81. Although ratios of distance are close in two genders of Type 1 in each group, females have a higher value in Type 2, which indicates that reflected points for females occur earlier.
Table 4: Reverse shoulder index and ratio of distance related to gender

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In addition to age and gender, RSI and ratio of distance are related to some cardiovascular diseases as well, such as hypertension, coronary heart disease (CHD), and angina pectoris.

[Table 5] reveals RSI and ratio of the distance of the participants of Type 1 who have a history of hypertension. Hypertension complainers are divided into two groups as follows: participants with hypertension for <10 years, and participants having hypertension for 10–20 years. The RSI of both groups is higher than the average value (74.91) of the patient group, as recorded in [Table 2]. Furthermore, ratios of distance are lower than the average (12.88). In addition, for the participants who have a longer history of hypertension, the value of RSI is higher while the ratio of distance is lower.
Table 5: Reverse shoulder index and ratio of distance for participants of Type 1 have history of hypertension

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We also found that RSI and ratio of distance are related to CHD. Seventy-five out of the 127 participants in Type 1 have CHD, and among those 75 participants, more than 62% of them also have hypertension. [Table 6] illustrates the values of RSI and ratio of distance obtained from participants with different diseases. These participants are classified into three groups. The first group stands for participants who have neither CHD nor hypertension. The second group includes participants who have CHD. The third group contains the participants with both CHD and hypertension.
Table 6: Reverse shoulder index and ratio of distance related to coronary heart disease and hypertension

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  • Group 1: Participants with neither CHD nor hypertension
  • Group 2: Participants with CHD
  • Group 3: Participants with both CHD and hypertension.


The prevalence of angina pectoris is high among all of the subjects. About 50% of evaluated patients, 64 out of 127, complained of angina pectoris. [Table 7] shows the RSI and ratio of the distance of angina pectoris patients. Compared with the values illustrated in [Table 6], it can be observed that the participants with angina pectoris have higher RSI and a lower ratio of distance than those having both CHD and hypertension. Moreover, referred to [Table 2], the values of RSI of both types are 5% higher than the average. This indicates that cardiovascular function of the participants with angina pectoris is considerably worse.
Table 7: Reverse shoulder index and ratio of distance related to angina pectoris

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Subendocardial viability ratio

Calculation of subendocardial viability ratio

Subendocardial viability ratio (SEVR) represents the ratio between diastolic pressure time index (DPTI) and systolic pressure time index (SPTI),



which is also known as tension time index.

[Figure 8] illustrates the parameters used in the definition of SEVR. DPTI stands for the area under the diastolic pressure, obtained by multiplying the mean diastolic pressure by the diastolic time (DT). SPTI is the area under the systolic portion of the pulse wave, calculated by multiplying the mean systolic pressure by the left ventricular ejection time.[11] Those two areas are divided by the dicrotic notch.
Figure 8: Parameters used in the definition of subendocardial viability ratio

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Several studies have confirmed that SPTI is directly correlated with myocardial oxygen consumption, and DPTI indicates potential subendocardial blood supply.[12],[13],[14] Based on pulse wave analysis, SEVR is considered to be the ratio between myocardial oxygen demand and supply.[8]

Experimental results of applying subendocardial viability ratio

Among all of the 169 participants in the patient group, the dicrotic notches are successfully located in 153 participants. In the control group, 88 out of 105 participants have detectable dicrotic notches, while SEVR, SPTI, and DPTI are calculated.

The means of SEVR, DPTI, and SPTI in both groups are shown in [Table 8]. We can observe that both SPTI and DPTI of the patient group are larger than those of the control group, while the SEVR of the patient group is smaller. We conclude that a lower SEVR stands for a worse cardiac function.
Table 8: The average subendocardial viability ratio, Systolic Pressure Time Index and diastolic pressure time index for patient and control groups

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We have evaluated SEVR values among different age groups in this research. [Table 9] shows the SEVR values of participants in the patient group, whose ages range from 7 to 96. We observe that the SEVR values decrease when ages increase. The highest age range has the smallest SEVR, which is 13.6% lower than the mean SEVR of the patient group. Since SEVR represents the ratio between myocardial oxygen demand and supply, it is obvious that myocardial oxygen supply degenerates as people age.
Table 9: Subendocardial viability ratio related to age in the patient group

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Gender is taken into consideration as well. [Table 10] illustrates the mean SEVR values of females and males for both patient and control groups. Although the ranges of ages are relatively equal for both females and males, the mean SEVR values of females are smaller than those of males. Compared with [Table 8], the SEVR values of female participants are lower than the mean SEVR of each group, while those of males are higher.
Table 10: Subendocardial viability ratio related to gender in patient and control group

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Among 153 participants in the patient group, there are 46 participants having hypertension for years, 117 participants having CHD, 72 participants with angina pectoris, and 4 participants suffering from cardiac failure. This is obviously a confirmation that the SEVR is helpful in diagnosing patients with angina pectoris, cardiac failure, and hypertension.[11]

[Table 11] reveals the average SEVR, SPTI, and DPTI values for four different diseases in the patient group. The participants with CHD and angina pectoris have similar SEVR, SPTI, and DPTI values. The participants in hypertension group, however, have a smaller SEVR value, which is 11.2 less than the average, 0.4978, of the patient group.
Table 11: The average Subendocardial viability ratio, systolic pressure time index, and diastolic pressure time index of subjects having particular cardiovascular diseases

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For the cardiac failure group, although there are only four participants, their SEVR, SPTI, and DPTI values are considerably lower. The SEVR values of all four participants are <0.4, ranged from 0.34 to 0.39, while the mean SEVR value is about 33% lower than that of the patient group. Not only do these parameters vary significantly from those of the other groups but also the pulse waves of cardiac failure patients are distinct as well. [Figure 9] shows a sample pulse wave recorded from a cardiac failure patient. Its dicrotic notch can be hardly recognized when compared to [Figure 10], which illustrates the pulse wave of a 35-year-old male without cardiac failure from the control group. It can be concluded that the myocardial oxygen consumption of cardiac failure patient is lower, while the oxygen supply is inadequate.[15]
Figure 9: Pulse wave of subject who has cardiac failure

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Figure 10: Pulse wave of a 35-year-old male in the control group

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  Experimental Results Top


Cardiovascular disease is the leading cause of death worldwide,[16] and has impacted not only industrialized countries but also developing countries. It accounted for approximately 30% of all deaths in 2010.[17] In this section, several major cardiovascular diseases will be discussed through pulse wave analysis. To explore some common factors for these particular diseases, the parameters mentioned in Section 2 and Section 3 will be applied to patients having the same diseases.

Hypertension

Hypertension is regarded as one of the most common causes of cardiovascular disease. There are an estimated 1 billion individuals with hypertension and approximately 7.1 million deaths per year may be attributable to hypertension.[18]

[Figure 11] illustrates the pulse wave of a 66-year-old male patient, Patient 1, who had a history of hypertension for 2 years. [Figure 12] shows the pulse wave of Patent 2, a 66-year-old male patient with a history of hypertension for 15 years.
Figure 11: Pulse wave of Patient 1 with hypertension for 2 years

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Figure 12: Pulse wave of Patient 2 with hypertension for 15 years

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[Table 12] shows that the RSI value of Patient 2 is 26.5% higher than that of Patient 1, and Patient 2 has a ratio of distance 25.5% lower than that of Patient 1. This indicates that RSI increases and the ratio of distance decrease the longer a patient has hypertension. As recorded in [Table 11], the average SEVR value of the control group is 0.5044, which is higher than the SEVR values of both patients. It is verified that the SEVR values of patients having hypertension are lower than those of the participants in the control group.
Table 12: Information and cardiac index of two patients with hypertension

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In both [Figure 11] and [Figure 12], the dicrotic notches are visible. The areas under the systolic portion (SPTI) of patients with hypertension are much larger than the areas under diastolic pressure (DPTI). Therefore, the SEVR value, which is the ratio between myocardial oxygen demand and supply, of each patient is smaller than that of the average value in the control group. In this case, the supply of myocardial oxygen of these two patients is insufficient compared to the participants in the control group.

Coronary heart disease

CHD, also known as arteriosclerotic heart disease or coronary artery disease,[19] occurs when fat or cholesterol builds up along the inner walls of the arteries of the heart, which narrows or blocks the arteries and reduces blood flow to the heart.[20] CHD is a large cause of death worldwide, causing 7,249,000 deaths in 2008 (12.7% of total global mortality).[21]

[Table 13] shows the general information and cardiac parameters of three patients with different health conditions. Patient 3 did not have CHD, Patient 4 did, and Patient 5 had both CHD and hypertension. Their pulse waves are shown in [Figure 13], [Figure 14], [Figure 15], respectively.
Table 13: Information and cardiac indexes for three patients with different health conditions

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Figure 13: Pulse wave of Patient 3 who has neither coronary heart disease nor hypertension

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Figure 14: Pulse wave of Patient 4 who has coronary heart disease but not hypertension

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Figure 15: Pulse wave of Patient 5 who has coronary heart disease and hypertension

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The dicrotic notch is only visible in [Figure 14], which makes it different from the other two figures. Although Patient 3 and 5 have different health conditions, their pulse waves are similar. Both of them have a narrow systolic phase, and their dicrotic notches are not obvious. Evaluating their health conditions by these figures is not accurate in this case; although their cardiac indices are indeed distinct from each other.

As shown in [Table 13], these three patients are all females of Type 1 with similar ages. RSI of Patient 3 is 13.5% lower than that of Patient 4, and Patient 5 has the highest RSI. However, Patient 5 has the lowest ratio of distance, 7.5% lower than that of Patient 4 and 40.9% lower than that of Patient 3. As revealed in [Table 6], patients with CHD have higher RSI and a lower ratio of distance than patients who do not have the disease. Moreover, patients with both CHD and hypertension have the highest RSI and lowest ratio of distance.[22] Meanwhile, Patient 5 has the lowest SEVR among all three. The average SEVR of CHD patients is 0.4873, as shown in [Table 11], which is close to that of Patient 4, 0.4818.

Cardiac failure

Cardiac failure occurs when the heart is unable to pump adequate blood flow to other organs to keep up the demands of the body.[23] The prevalence of cardiac failure is between 2% and 3% in developed countries and rises sharply for 70–80-year-old people. As the population ages, the overall prevalence of cardiac failure is increasing.[24] In this research, the patients with cardiac failure are in the age range of 75–86.

The pulse wave shown in [Figure 16] is from an 86-year-old female with cardiac failure. She also has CHD and has had hypertension for more than 10 years. Her cardiac function is rated grade IV on the New York Heart Association Functional Classification. Her cardiac indices were:
Figure 16: Pulse wave of patient with cardiac failure

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  • RSI, ratio of distance, Type: Cannot be detected
  • SEVR: 0.3777
  • SPTI: 5370.2
  • DPTI: 2023.7.


Her RSI cannot be calculated since two shoulders during the systolic phase are undetectable. The same situation happened among other patients with cardiac failure in this research.

According to [Table 11], the mean SEVR of cardiac failure patients is 0.3742, the mean SPTI is 5907.4, and the mean DPTI is 2140.1. They are all close to the values of this patient. Note that, her SEVR value is 31.8% lower than the mean SEVR of the patient group, and SPTI and DPTI are 46.9% and 48.1% lower than the average values, respectively. Referring to the definitions of SEVR, DPTI, and SPTI in Equation (4) and [Figure 8], it indicates that cardiac failure patients have a low subendocardial blood supply and myocardial oxygen consumption. Even the ratio between myocardial oxygen demand and supply is low.

By observing the pulse waves shown in [Figure 16], we can see that their systolic PP and PP are different. Moreover, the pulse rate of this patient is rapid and unsteady. Related to medical records, patients having cardiac heart failure are always accompanied by CHD and hypertension.

Angina pectoris

Angina pectoris is also known as chest pain or discomfort. It occurs when there is decreased blood flow to the heart, impairing the delivery of oxygen and nutrients to the heart muscle.[25] Angina pectoris usually causes irritating pressure, fullness, squeezing or pain in the chest, but people may also feel discomfort in the neck, jaw, shoulder, or arm.[26] Furthermore, it usually is a symptom of CHD.[25]

A 69-year-old male patient had angina pectoris for years, and his pulse wave is shown in [Figure 17]. His cardiac indices were as follows:
Figure 17: Pulse wave of a 69-year-old male patient with angina pectoris

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  • RSI: 85.58
  • The ratio of distance: 11.00
  • Type: 1
  • SEVR: 0.4266
  • SPTI: 8365.2
  • DPTI: 3568.3.


As illustrated in [Table 7], the mean RSI of angina pectoris patients of Type 1 is 79.17. The RSI value of this patient is higher than the mean and is also higher than the average value of the patient group of Type 1. His ratio of distance is close to the mean value of angina pectoris patients. The SEVR, SPTI, and DPTI values of this patient are below the average values of the patient group, which is in agreement with the results concluded in Section 3.2.

Angina pectoris is caused by increased oxygen demand from myocardia or by decreased supply to the myocardia.[25] As mentioned in Section 3.1, SPTI is correlated with myocardial oxygen consumption, while DPTI indicates potential subendocardial blood supply. In this case, angina pectoris is due to the lower supply of myocardial oxygen since the DPTI is 18% lower than the average value of the patient group.


  Discussion Top


In this research, the pulse waves of 274 participants from both the patient and control groups are evaluated and analyzed. Several important points are acquired from the original pulse wave signal data and used in cardiac indices calculation. Then, pulse wave analysis is applied to the study of cardiovascular diseases. Referring to different positions of the first and second shoulders of pulse wave signals, two types of waveforms are classified, and the RSI and the ratio of distance are calculated. By investigating the correlation between those two cardiac indices and ages, genders, and cardiovascular diseases, several common and important observations are reported. Finally, by carrying out case studies, we have verified some of our findings in this research with several patient cases. We expect our discoveries in this research can eventually help the end-users in cardiovascular diseases diagnosis.

Declaration of patient consent

The authors certify that they have obtained all appropriate patient consent forms. In the form the patient(s) has/have given his/her/their consent for his/her/their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity, but anonymity cannot be guaranteed.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr., et al. Seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure. Hypertension 2003;42:1206-52.  Back to cited text no. 18
    
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Lung National Heart and Blood Institute. Explore angina. 2011. Available from: http://www.heart.org/HEARTORG/Conditions/HeartAttack/SymptomsDiagnosisofHeartAttack/Angina-Chest-Pain_UCM_450308. [Last accessed on 2018 Apr 12].  Back to cited text no. 25
    
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15], [Figure 16], [Figure 17]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11], [Table 12], [Table 13]



 

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