Early Disease Detection by Extracting Features of Biomedical Signals

Elderly people face a lot of health problems in day to day life due to old age and so many reasons. Therefore a regular health check-up is needed for them which is much more expensive and cannot be afforded by many people. Again the diagnosis is much more complicated to understand and in many cases there is a chance of mistreatment. There is another chance of delay in the detection of disease and late treatment causing risk in their lives. So, the disease should be detected in the early stage for lower cost and lower risk in life. The present work is related to the different physiological parameters of a human being that are to be measured to accurately diagnose the related disease. Though there are numerous physiological parameters, this work emphasizes on some of the most common physiological parameters such as blood pressure, heart rate and ECG which are of primary importance to elderly people. Accurate measurement and analysis of these parameters can lead to diagnose of several lethal disease. In this work, the method of measurement and analysis of these physiological parameters are described. The simulation, processing and analyses of these signals are also done in the work. The prime objective of the research work is to analyze and extract the features of ECG signal and blood pressure signal for early diagnosis of life threatening diseases

Development of a measurement procedure for the assessment of carotid blood pressure by means of Laser Doppler Vibrometry

L'ipertensione è uno dei principali fattori di rischio per numerose patologie, quali infarto del miocardio, insufficienza cardiaca e renale, ictus, e rappresenta la principale causa di morte al Mondo. Risulta, pertanto, fondamentale il monitoraggio della pressione arteriosa nell'ambito della prevenzione dell'insorgere di gravi patologie. Lo scopo del presente lavoro è di validare una procedura di misura per la determinazione della pressione arteriosa carotidea mediante la tecnica della vibrometria Laser Doppler (LDV). Essa è una tecnica di misura senza contatto ad elevata sensibilità, in grado di rilevare le vibrazioni della pelle legate all'attività cardiovascolare. Nel presente lavoro, il segnale LDV è stato acquisito su 28 soggetti sani ed è stato calibrato per mezzo di un opportuno modello matematico esponenziale per ottenere la forma d'onda di pressione carotidea a partire dallo spostamento del vaso sanguigno. I risultati ottenuti sono stati confrontati con due tecniche di riferimento, la sfigmomanometria e la tonometria arteriosa. La pressione sistolica ottenuta dal segnale LDV calibrato ha mostrato una deviazione percentuale inferiore del 4% e del 8 % rispetto a quella ottenuta tramite cuffia sfigmomanometrica e tonometria rispettivamente. L'integrazione del segnale e l'applicazione di un modello di calibrazione sono state considerate quali significative fonti di incertezza, e si è stimata un'incertezza complessiva di circa il 15 % nella misura della pressione sistolica. Dal segnale LDV sono stati determinati altri significativi parametri emodinamici quali il tempo di eiezione del ventricolo sinistro e la rigidità arteriosa. In conclusione, la tecnica di misura proposta mostra buona correlazione con i metodi di misura di riferimento, benchè vadano prese in considerazione alcune criticità quali l'individuazione del punto di misura, la presenza di artefatti da movimento e di fenomeni di riflessione non legati all'impulso pressorio oggetto di studio.High blood pressure is a great risk factor for several physiological diseases, i.e. myocardial infarction, heart failure, stroke, renal failure. Therefore, blood pressure measurement is a fundamental aspect of health monitoring. The aim of the present work is to validate a measurement procedure for the assessment of carotid blood pressure by means of Laser Doppler Vibrometry (LDV). LDV is a non-contact technique able to detect the skin vibrations due to the cardiovascular activity. In this study, LDV signal was acquired from 28 healthy participants and it was calibrated by means of an exponential mathematical model in order to obtain the carotid pressure waveform from the displacement of the vessel. The results have been compared with two standard techniques for the assessment of blood pressure, sphygmomanometric method and arterial applanation tonometry. The systolic peak of the calibrated waveform from LDV showed an average percentage deviation inferior to 10 % from the one assessed by means of reference techniques. The accuracy of the present measurement technique is discussed, considering the signal integration and the application of the calibration model as significant contributions to the total amount of uncertainty. An average percentage uncertainty of around 15 % has been obtained in the measure of carotid systolic pressure. Moreover, other hemodynamic significant parameters, such as arterial stiffness and Left Ventricular Ejection Time, have been derived from LDV data, showing good correlation with the measures of the reference methods. In conclusion, the proposed measurement technique, for the assessment of carotid blood pressure, shows good agreement with the reference techniques. Overall, some critical issues must be considered, such as the correct localization of the measurement point, the presence of movement artifacts and reflection phenomena not related to the pressure pulse in the investigated vessel

A noninvasive and cuffless method for the measurements of blood pressure.

Chan Ka Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2002.Includes bibliographical references.Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives --- p.1Chapter 1.2 --- Definitions --- p.2Chapter 1.2.1 --- Definition of blood pressure --- p.2Chapter 1.2.2 --- Definition of hypertension --- p.3Chapter 1.3 --- Problems related to hypertension --- p.4Chapter 1.4 --- The importance of measuring blood pressure --- p.4Chapter 1.4.1 --- Self-measurement of blood pressure --- p.5Chapter 1.4.2 --- Ambulatory blood pressure measurement --- p.5Chapter 1.5 --- Review of blood pressure measurement techniques --- p.7Chapter 1.5.1 --- The invasive method --- p.7Chapter 1.5.2 --- Noninvasive methods --- p.8Chapter 1.6 --- Review of currently available blood pressure meters --- p.15Chapter 1.7 --- Prevalence of hypertension --- p.19Chapter 1.7.1 --- Hong Kong --- p.19Chapter 1.7.2 --- Worldwide --- p.20Chapter 1.8 --- The market for blood pressure meters --- p.21Chapter 1.9 --- Organization of the thesis --- p.22References --- p.24Chapter Chapter 2 --- Measurement of the ECG-PPG interval --- p.30Chapter 2.1 --- Introduction --- p.30Chapter 2.1.1 --- Pulse transit time (PTT) --- p.30Chapter 2.1.2 --- Electrocardiogram (ECG) --- p.36Chapter 2.1.2.1 --- Measurement of the ECG signal --- p.37Chapter 2.1.3 --- Photoplethysmography (PPG) --- p.38Chapter 2.1.3.1 --- Measurement of the PPG signal --- p.41Chapter 2.1.4 --- Measurement of blood pressure by ECG-PPG interval --- p.43Chapter 2.2 --- Source of errors for measurement of the ECG-PPG interval --- p.44Chapter 2.2.1 --- Effects of variability of ECG-PPG intervals --- p.44Chapter 2.2.2 --- Effects of bending the arm --- p.49Chapter 2.2.3 --- Effects of an external force --- p.54Chapter 2.3 --- Conclusion --- p.60References --- p.62Chapter Chapter 3 --- Cuffless and Noninvasive Measurement of Blood Pressure --- p.68Chapter 3.1 --- Introduction --- p.68Chapter 3.2 --- Effects of subject-dependent calibration --- p.74Chapter 3.3 --- Effects of different time intervals --- p.81Chapter 3.4 --- The impact of using different Q-P intervals --- p.96Chapter 3.5 --- Real-time measurement of blood pressure --- p.104Chapter 3.6 --- Conclusion --- p.108References --- p.110Chapter Chapter 4 --- Motion Artifact Reduction from PPG Recordings in Ambulatory Blood Pressure Measurement --- p.114Chapter 4.1 --- Introduction --- p.114Chapter 4.2 --- Previous works --- p.115Chapter 4.3 --- Theory --- p.116Chapter 4.3.1 --- The adaptive filter --- p.117Chapter 4.3.2 --- Variation of step-size parameters --- p.119Chapter 4.3.3 --- Effects of filter length --- p.120Chapter 4.4 --- Experiment --- p.121Chapter 4.5 --- Results --- p.123Chapter 4.6 --- Discussion --- p.131Chapter 4.7 --- Conclusion --- p.133References --- p.135Chapter Chapter 5 --- Measurement of Blood Pressure using the PPG signal --- p.138Chapter 5.1 --- Introduction --- p.138Chapter 5.2 --- Theory --- p.138Chapter 5.3 --- Experiment --- p.142Chapter 5.3.1 --- Multiple linear regression (MLR) --- p.142Chapter 5.3.2 --- Artificial neural networks (ANNs) --- p.146Chapter 5.3.3 --- Results --- p.149Chapter 5.3.4 --- Discussion --- p.152Chapter 5.4 --- The implementation of the Q-P interval --- p.153Chapter 5.4.1 --- Results --- p.154Chapter 5.4.2 --- Discussion --- p.156Chapter 5.5 --- Conclusion --- p.157References --- p.158Chapter Chapter 6 --- Conclusion and Future Studies --- p.160Chapter 6.1 --- Major contributions --- p.160Chapter 6.2 --- Future studies --- p.162References --- p.165Appendix I --- p.16

Advances in non-invasive blood pressure measurement techniques

Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.</p