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Choose Store. In this series. Skip this list. In general, illness and risk factors will have more impact on SI. This makes SI a perceptible indication in diagnosing arterial stiffness. SI can be affected by the cardiac condition as we described before. The adjusted SI can only rectify influence of heart rate in a certain level. Other abnormal cardiac conditions, such as heart failure, will disturb the pulse wave form in different ways.

A basic judgment of cardiac condition will make SI more catholicity.

1. Introduction

Computing the integral of Equation 4 :. Cardiac Output is highly correlated to age, weight, and systolic blood pressure. It shows the working status of the heart while SI shows the degree of arterial stiffness. We can also find that many subjects in patients group haveabnormal Cardiac Output CO: 4. But there is no significant correlation between SI and CO.

Therefore, CO is a good complement of SI for analyzing cardiovascular condition. The calculation based on the points with special meanings is very sensitive in the detection of risks. It uses simple algorithm to achieve the balance of performance and accuracy. The pulse is produced by the cooperation of heart, blood vessel, micro circulation and other parties.

The more information included the more accurate classification we can get. This research used some sample wave forms to represent the different categories. Pulse wave is relatively stable under the testing condition: subject setting in a quite environment and keeping calm. The pulse wave analysis result is highly repeatable in this condition.

There are several classification system for the pulse wave. This classification focus on the notch of the wave form which is considered as the indicator of arterial stiffness. Bates evaluates continues wave forms to include other possible diseases. He gave detail description of the pulse wave and discussed the cause of each pulse wave type. Possible diseases were also provided in his research. Possible diseases which can be diagnosed based on the different types of cardiovascular pulse shapes Bates In order to get more precise information from the wave form, researchers take the traditional pulse diagnosis as the reference and mapping the characters of pulse diagnosis with the pattern of wave form.

It can be used to detect certain cardiovascular risk as well as the classification.

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For example, acute anterior myocardial infarction will have a sharp systolic component and very small diastolic component which suggests poor blood supply. Fourier Transform and Wavelet Transform have been used to perform the basic analysis on the pulse waveform. Fourier transform is a basic and important transform for linear analysis which usually transfers the signal from time domain signal based on time to frequency domain the transform depends on frequency.

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  • The Fourier theory states that any continuous signals or time serial data can be expressed as overlay of sine waves with different frequencies. This process can help signal analysis because the sine wave is well understood and treated as simple function in both mathematics and physics. Fourier transform calculates the frequency, amplitude, and phase based on this theory.

    Significant features could be detected by Fourier transform from similar time series data with big differences in frequency domain.

    Breaking Waves

    The Fourier transform can be treated as a special calculus formula that expresses the qualified function into sine basis functions. The function with the lowest frequency is called the fundamental. It has the same repetition rate of the periodic signal under evaluation. The frequency of other functions is integer times of the fundamental frequency. Inverse Fourier transform can be used to recover the time series signal after the analysis on frequency domain is done.

    With comparing the original waveform and transform data, some special features can be detected in the frequency domain. The regular waveform from a normal subject has data nearly U shape distributed in the frequency domain. Lower frequencies and higher frequencies get bigger values and the slop goes smoothly from negative to positive. The data become inconspicuous for frequencies between10 and Hz.

    All the Breaking Waves

    The higher values in time domain will result in the higher value in frequency domain. Patients with old myocardial infarction often have obtuse systolic component and weak diastolic component due to the abnormal cardiac function. The diastolic component has a round top and much wider than the normal waveform. This feature will generate a local maximum value at around 7Hz and Hz. This feature can be used to detect the cardiac function diseases which cause slow change rate at pulse waveform.

    In a group of selected testing data 50 normal waveform and 50 typical waveforms for old myocardial infarction , 48 testing data has local maximum at around 7Hz and 52 has smooth U shape distribution at frequency domain. Some possible reasons for mistake in the test:. The shape of diastolic component is important to arterial stiffness analysis.

    The features for arterial stiffness can not be derived directly from the Fourier transform. Arrhythmia cause the second pulse arrives in advanced while the first pulse waveform is not complete yet. FFT shows that multi local maximum values appear at both higher frequency end and lower frequency end.

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    Arrhythmia is a common abnormal electrical activity in cardiovascular system. The heart rate might go too fast or too slow which will cause the waveforms change shape among continuous pulses. This feature can be captured in both time domain and frequency domain. The basic feature in time domain is time variance among continuous pulses exceeding the average level. The incomplete waveforms and merged waveforms often result in the pulse detection fails which is also a sign of arrhythmia. Eight typical arrhythmia waveforms have been identified from testing data and the patients do have arrhythmia history on file.

    Wavelet transform is well known for localized variations of power analysis. It uses the time and frequency domains together to describe the variability. Wavelet functions are localized in space while Fourier sine and cosine functions are not. The algorithm can extract information from many kinds of data including audio and images especially in geophysics fields. Wavelet provides multi-resolution analysis to the source data that make the result more adequate for feature detection. Diastolic component can be easily detected by value variance among adjacent points. It has significant impact on slope changes of continuous values.

    It also generates additional peak values at Wavelet transform result. Since pulse data is two dimensional time serial data, the mining techniques for time serial data can be applied on it. The waveforms can be categorized based on the similarity between testing waveform and well classified sample waveforms. Because the waveforms have same structure: taller systolic component with lower diastolic component following, the similarity calculation can achieve high accuracy.

    It can be measured by the total distance of corresponding points between sample waveform and testing waveform warping. One of the most fundamental concepts in the nonlinear pattern recognition is that of 'time-warping' a reference to an input pattern so as to register the two patterns in time. The DTW proposed by Sakoe and Chiba is one of the most versatile algorithms in speech recognition.

    Figure shows the basic idea about the time warping. The majority application for DTW was speak recognition in the early research period.

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    Sakoe It achieve higher recognition rate with lower cost than most other algorithms. Medical data has been analyzed with DTW recently. ECG is one of the most common signals in health care environment, so most researches focus on ECG signal analysis. DTW was applied to ECG segmentation first since segmenting the ECG automatically is the foundation for abnormal conduction detection and all analysis tasks.

    Vullings The purpose of the time warping is to provide a mapping between the time indices i and j such that a time registration between the waveforms is obtained. TimeSeriesPoint can hold an array of double values which means the algorithm can process signals from multiple sensors or leads. The number of signals is defined as the dimensions of the time series data. The get function will return the value for a specific signal based on the input dimension. There are also some utility methods to return the data array, hash the value, or check the equivalence to other TimeSeriesPoint.

    TimeSeries is a collection of TimeSeriesPoints. A list of labels and a list of time reading are provided for the time series data to mark the time and special points. Label and time reading can be retrieved for each point by the method getLabel int n and getTimeAtNthPoint int n. Method getMeasurement int pointIndex, int valueIndex is provided to find the value of specific signal at the given time point. The above pulse wave was taken from a male patient at department of cardiology.

    He had a history of myocardial infarction for 8 years and came to the clinic again for angina pectoris. The waveform is a typical one with poor cardiac function. The systolic part is very sharp and narrow that suggests very low Cardiac Output. The diastolic component is lost since the weak pulse. Blood vessel condition is not measurable because the cardiac function is in an accurate stage. The above pulse wave is collected from a patient with old myocardial infarction and degenerative valvular disease.

    He has chest distress and ictal thoracalgia for eighteen years. Gasping happened for the recent 6 months and the pain increased in intensity for the last 3 months. The patient also has mitral regurgitation and tricuspid regurgitation that make him difficult to finish some daily activities. The waveform has regular shape with diastolic component. Dark waves background with halftone effect freepik 75 4. Astronaut in spacesuit standing on surfboard and surfing in milky way stars vectorpocket Floating shapes Harryarts k Sea waves collection freepik k Abstract waves design kjpargeter 17k Abstract wave logos freepik 68k Blue wavy shapes on transparent background m.

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