In the current study, we found the relationship between blood pressure (BP) and pulse transit time (PTT). After measuring ECG and photoplethysmogram, the PTT was calculated from the acquired signals. Blood pressure ( BP) , the pressure exerted by circulating blood upon the walls of blood vessels, is an important physiological parameter and can provide some information forpersonal healthcare. Pulse transit time is the time taken for the arterial pulse Pressure wave to travel from the aortic valve to a peripheral site. It is usually measured from the R wave on the electrocardiogram to a photoplethysmography signal. PTT is inversely proportional to blood pressure.The mean error are 0.29 mmHg for SBP and 0.1mmHg for DBP.These results are satisfied with the regulation of ANSI/AAMI for certification of sphygmomanometer that real measurement error valueshould be within the mean error of ±5mmHg

Electrical activity of the heart is called as electrocardiogram i.e. ECG. Arrhythmias are among the most common ECG abnormalities. ECGs provide lots f information about heart abnormalities. The diagnosis depends upon the physician and it varies from physician to physician and also depends upon the experience of the physician. Previously many techniques were tried for analysis and automisation of the analysis. This paper describes the use of MATLAB based artificial neural network tools for ECG analysis for finding out whether the ECG is normal or abnormal and if it is abnormal, what is the abnormality. There are various arrhythmia like Ventricular premature beats, asystole, couplet, bigeminy, fusion beats etc. To classify this, various weighted neural networks were tried with different algorithms. They were provided training inputs from the standard MIT-BIH Arrhythmia database and tested by providing unknown patient data from the same database. The results obtained with different networks and different algorithms are compared, it is found that to identify whether the ECG beat is normal or abnormal, cascade forward back network algorithm has shown 99.9 % correct classification. These results are compared with previous neural network techniques and found that method proposed in this paper gives best results.

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MIT-BIH Database is the standard ECG database which is used universally for ECG analysis purpose. MIT-BIH database for normal sinus rhythm is sampled at 128 Hz and the data is available at uniform intervals of 7.8125 ms. To use this data for analysis purpose with various techniques like artificial neural networks, correlation techniques etc., it is required to have samples at desired intervals. Hence this paper proposes an image processing method to convert the samples at desired intervals, so that the MIT-BIH database can be used widely and universally.