Preeclampsia is a disorder that arises during pregnancy which results in maternal death during delivery, fetal death in the womb and growth retention in infants based on the degree of severity and duration of onset. The markers for identification are found to be Strength of uterine muscles, Decreased velocity & volume of Uterine Artery, Poor placentation, Deficient in Remodeling of Spiral artery. The proposed work emphasis on extraction of features of EHG signal and Ultrasound Image of normal and suspected preeclampsia patients at second trimester to identify the condition at the earliest. The ability of the Uterine Muscle can be identified by recording of electrical activity of the uterus, by a device called Electrohysterograph that uses surface bipolar electrodes placed at various points in the lower abdominal of pregnant women that can indicate the possibility of Preterm labor. The anatomy of Uterus, an indicator of weaker cervix and anatomy of kidney can be analyzed by abdominal Ultrasound Imaging. Based on the combined analysis of images and signal along with the support of biochemical tests it is possible to identify the disease at the start of second trimester.

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Medical abnormalities in human body are often reflected by raise in temperature at various areas in the body. With the requirement of reliable non-invasive on the increase Infrared Thermal Image is an effective aiding in monitoring and diagnosing medical abnormalities. Existing research has applied Infrared Thermal Image effectively for various medical conditions like breast cancer screening, diabetes and peripheral vascular disorder, Risk Assessment and Treatment Monitoring. Thermal Image cameras are capable of capturing the body temperature variations, these temperature variations can lead to significant diagnosis in several areas ranging from simple flu caused by influenza virus to several conditions like diabetes, eye syndrome and thyroid to name a few. Heat distribution captured from Infrared Thermal Image by thermal cameras like Forward Looking Infrared Imaging (FLIR) with a sensitivity range of 0.10C and wide temperature ranging from - 100C to +1000C can produce good thermal images. This research suggests a non-expensive and non-obtrusive diagnostic procedure which utilizes thermal imaging for unexplored areas of applying thermal imaging and the possibility of extracting thermal variations with RGB images. To achieve the objective various image processing techniques like image preprocessing, selecting the Region of Interest (ROI), extraction by region segmentation, selective feature extraction and finally suitable classification of the relevant application selection are adopted. Results of the proposed method for detecting abnormality have been validated based on the temperature map histogram comparison from thermal image.

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High cholesterol, High blood pressure, Diabetes, Depression, Obesity, Smoking, Poor diet, alcohol consumption, and no exercise are literally the major causes which have taken the life of many people in the world. All the parameters effect is the major slow points for sudden cardiac death (SCD). As per the surveys conducted there are 1 in 4 deaths caused due to a heart attack in the U.S alone. Ventricular tachycardia (VT) is the deadly arrhythmias which can lead to SCD. Prediction of SCD using ECG signal derivative is a popular area of research. There are many papers published in this research. The recent development of a new algorithm on this topic helps to further research. In this work, we perform the overview of the ECG signal which is a way of measuring heartbeat rate and other features. Feature extraction of content areas in ECG and Classification algorithms for VF. We would see technique and methods based on ECG signal derivative by research in order to detect and predict SCD.