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NON INVASIVE DIAGNOSIS OF CARDIAC ARRHYTHMIA, CORONARY ARTERY DISEASE THROUGH FINGERTIP PHOTOPLETHYSMOGRAPHYAuthor: Neeraj Paradkar Date: 2017-06-10 Report no: IIIT/TH/2017/72 Advisor:Shubhajit Roy Chowdhury AbstractHealthy heart is a key for healthy life. Unfortunately in many cases the heart function deteriorates over a person’s lifetime, often with tragic consequences. A class of diseases involving heart functionality is termed cardiovascular disease. Among a number of such cardiovascular diseases, cardiac arrhythmia and coronary artery disease (CAD) are two of the most common and potentially life threatening diseases and are the our work is focussed around them. A primary reason for a high casualty figures caused by these diseases can be attributed to the fact that the diagnosis is not done on time. Often, the symptoms are noticeable only at a stage where the person needs immediate medical attention. Accurate diagnosis can only be done at specialized healthcare facilities by trained personnel. The diagnosis is also expensive. This limits its accessibility for major portion of population especially in rural areas. State of the art diagnosis for coronary artery disease typically involves an invasive procedure. Though this procedure is conclusive, it is still painful and risky. Assessing these challenges, there is a need for a widely accessible, inexpensive and preferably non invasive alternative. While looking for alternatives, electrocardiography or ECG has advantages of being accessible, non-invasive, and inexpensive. It is used for diagnosing cardiac arrhythmia, and as a screening test for coronary artery disease. However this screening is inconclusive and other techniques are recommended. Another non invasive technique, phonocardiography or PCG suffers from complex design and sensitive equipment. Which brings us to yet another well known technique, most commonly used for pulse oximetry, the photoplethysmography (PPG). Photoplethysmography can be defined as study of blood volume changes through light. It is non invasive, inexpensive and can easily be made portable thus making an ideal candidate for further investigation. PPG is also very suitable for use in wearable devices technology and such devices are used for ambulatory monitoring. Having all the desired characteristics, PPG is further investigated towards diagnosing cardiac diseases. As PPG as a technique already exists and is widely used, this work is aimed at developing techniques to analyse the PPG data towards possible diagnosis of cardiac diseases. Phtoplethysmography has its own challenges. It has high sensitivity and susceptibility to motion. This is particularly concerning for detecting arrhythmia and coronary artery disease alike as it wrongly affects heart beat detection as well as degrades the natural characteristics of the signal. First contribution of this work is a technique to estimate heart rate in presence of motion induced artifcats using fuzzy entropy of the signal. Once accurate heart rate is estimated, the input signal is tested for possible cardiac arrhythmias such as tachycardia, bradycardia, asystole, ventricular tachycardia and ventricular fibrillation. The fuzzy entropy based method poses some implementation limitations as it is computationally inefficient and requires initial user input. Another approach is developed to overcome these limitations. A quality index is assigned to each pulse. The artifact portion is removed using an appropriate threshold. The remaining clean portion of the signal is used for arrhythmia detection. This technique is faster and more accurate than fuzzy entropy based technique and much more useful for arrhythmia detection. Another important contribution of this work is to explore PPG for detecting coronary artery disease. This involves analysing the morphology of the PPG signal and its second derivative. Characteristic points are obtained from the PPG waveform and its second derivative. Temporal position of these characteristic points is used as a distinguishing feature to classify between healthy subjects and CAD patients. This analysis is carried out using invasive arterial blood pressure (ABP) signal as well. Supervised classification using support vector machine is employed for classifying healthy subjects and CAD patients. It is observed that higher classification accuracy was obtained using PPG signal compared to ABP. Despite certain limitations, we present proof to suggest that PPG can be effective in detecting certain cardiac arrhythmias and coronary artery disease. Full thesis: pdf Centre for VLSI and Embeded Systems Technology |
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