Nivedita Daimiwal

Continuous cuffless blood pressure monitoring based on PTT

Blood pressure (BP) is one of the important vital signs that need to be monitored for personal healthcare. This paper describes the method developed by the authors to measure systolic blood pressure from pulse transit time (PTT) 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. This method does not require an air cuff and only a minimal inconvenience of attaching electrodes and LED/photo detector sensors on a subject. Twenty three healthy subjects (age 18–60 yrs) were studied. Blood pressure measurement is carried out using pulse transit time and is compared with sphygmomanometry, the reference standard and the oscillometric based automatic BP measuring machine. The results show that the standard deviation of their differences was around 3 mmHg. The developed pulse transit time based method can be used as a noninvasive and cuffless alternative to the conventional occluding-cuff approaches for long-term and continuous monitoring of blood pressure.

Respiratory rate, heart rate and continuous measurement of BP using PPG

Measurement of blood volumetric changes in human body by photoplethysmographic sensors is used in present study. Objective is to measured different parameters that are heart rate, respiratory rate, BP. PPG signal is acquired by PPG sensor, microcontroller and RS 232. The acquired PPG signal is displayed in MATLAB. Frequency domain analysis of PPG signal shows a two peaks first at around 0.25 to 0.35 Hz and second at around 1 to 1.5 Hz. FFT at 1Hz relates to 60 BPM and FFT at 0.25 Hz relates to 15 respiratory cycles per minute. For BP Measurement, the pulse height of PPG is proportional to the difference between the systolic and the diastolic pressure in the arteries. The standard blood pressure monitoring instrument is used to calculate correlation coefficient. The arterial blood pressure is calculated based on these coefficients. PPG signal is used to detect blood pressure pulsations in a finger and achieved an accuracy of (0.8 ± 7) mmHg and (0.9 ± 6) mmHg for systolic and diastolic pressure, respectively. The developed PPG based method can be used as a noninvasive alternative to the conventional occluding-cuff approaches for long-term and continuous monitoring of blood pressure, heart rate and respiratory rate.

Wireless Transfusion Supervision and analysis using embedded system

Infusion pumps have a widespread use in the health care and Intensive Care Unit (ICU) of each hospital. The use of infusion pumps is advocated over manual flow control system on the basis of assuring precise and accurate delivery of prescribed fluid volumes over a specified time and to help in better nursing management. Transfusion speed of the pump must be changed as per the patients need and alarm indication for various abnormalities must be given. Manually setting all parameters is very time-consuming and a mistake could mean a high risk for the patients. Thus there is an obvious need of a system that can take care of this from central room. The developed system is a network which consists of a host computer (PC), transfusion monitors and interconnection mechanisms. The host computer manages the basic data of patients, transfusion information as well as controls the transfusion monitors through a wireless network to regulate the transfusion speed and give an alarm at the abnormality. A Graphical User Interface (GUI) is developed in Visual Basic (VB), which analyses the infusion history. The communication between the PC and the infusion pump occurs via a wireless network using ZigBee. The history report is generated in MS Excel format, which can be referred by the Physician. The developed system can be efficiently used for remote supervision of transfusion monitors in the ICU by hospital staff.

Comparative analysis of LDR and OPT 101 detectors in reflectance type PPG sensor

Detection of blood volume change in the skin by using Photoplethysmogram (PPG) sensor is based on the principle that hemoglobin in the blood absorbs infrared light than the other tissue. Favorable optical window is around 990 nm range. The reflectance type photoplethysmographic sensor is designed using two different detectors. Objective is to compare the response of PPG sensor by using Light Dependent resistors (LDR) and OPT101 as a detector. Signal is recorded by placing the sensor on a finger tip for wavelength ranging from visible to near IR (400 to 1000 nm) range. It is observed that the PPG signal captured using LDR is around 660 nm wavelength but for the OPT101 response is 500 nm to 1000 nm. That is OPT 101 can be used to capture PPG in visible and infrared region. Brain mapping using optical sensor OPT101 is preferable for the measurement of blood volume and blood flow. For source of 660 nm, LDR or OPT 101 can be used to detect the peripheral pulse. LDR is not suitable for the measurement in infrared range.

Acquisition of PPG signal for diagnosis of parameters related to heart

Optical sensors that use photons as sensing elements are increasingly becoming important and relevant in the field of non-invasive diagnostics. The reason is that they are simple in construction, easy to use and relatively inexpensive in comparison with tools such as EEG, MRI and FMRI that can be used for research purposes without much investment. Among the various optical sensors available, the photoplethysmographic sensors that are capable of measuring the blood volumetric changes in subcutaneous vessels are used in present study. Objective is to diagnose different parameters like heart rate, blood pressure, respiratory rate, with the help of reflectance type PPG sensor. Real time PPG signal is captured from the sensor and with the help of microcontroller and RS 232 serial communication, the real time acquisition of PPG signal is observed on MATLAB. Detailed analysis of frequency spectrum of PPG signal shows a cardiac peak around 1Hz corresponding to 60 pulsations a minute and respiratory peak around 0.25 Hz corresponding to 15 inspiration/expiration cycles per minute. After the analysis of PPG signal, all the parameters are calculated and with the help of graphic user interface (GUI), all the parameters can simultaneously be seen on GUI window.

Multimodal medical image fusion under nonsubsampled contourlet transform domain

The Paper presents the multi modal medical image fusion technique based on discrete non subsamples contour let transform and pixel level fusion rule. The fusion criteria is to minimize different errors between the fused image and the input images. With respect to the medical diagnosis, the edges and outlines of the interested objects is more important than other information. Therefore how to preserve the edge like features is worthy of investing for medical image fusion. As we know the image with higher contrast contains more edge like features. In term of this view, the project proposed a new medical fusion scheme based on discrete contour let transformation, which is useful to provide more details about edges at curves. This transformation will decompose the image into finer and coarser details and finest details will be decomposed into different resolution in different orientation. The pixel and decision level fusion rule will be applied selected for low frequency and high frequency and in these rule we are following Image Averaging, Gabor filter bank and Gradient based fusion algorithm. The fused contourlet coefficients are reconstructed by inverse NS contour lettransformation. The visual experiments and quantitative assessments demonstrate the effectiveness of this method compared to present image fusion schemes, especially for medical diagnosis. The goal of image fusion is to obtain useful complementary information from CT/MRI multimodality images. Image quality metrics can be found out by satisfactory entropy,better correlation coefficient, PSNR (Peak Signal to Noise Ratio) and less MSE (Mean Square Error).

Analysis of features for myocardial infarction and healthy patients based on wavelet

An electrocardiogram (ECG) is the recording of the electrical activity of the heart. For different pathologies, different changes are observed in a normal ECG signal. In this paper, the features of 12 lead ECG signals are analyzed using wavelet decomposition and eigen space analysis for the detection. Wavelet decomposition distributes the diagnostic information present in the ECG signal amongst different sub-bands. It is observed that changes in the ECG signal for myocardial infarction patients are reflected in the statistical parameters (mean, variance, standard deviation and entropy) and wavelet energies of the wavelet coefficients of each sub-band that are calculated after decomposition with the mother wavelet and also in the eigen values calculated from covariance matrices obtained from subband matrices in the eigen space. Therefore, the statistical parameters along with wavelet energies and the eigen values can be used as training features for classification of the ECG signals into those belonging to that of healthy control (HC) and myocardial infarction (MI) patients. The 12 lead ECG signals of both healthy control (HC) and myocardial infarction (MI) are obtained from the PTB Diagnostic ECG Database.

Energy Distribution and Coherence-Based Changes in Normal and Epileptic Electroencephalogram

In endeavor toward better understanding of brain functions, the analysis of information transfer between the various brain lobes plays a crucial role. Electroencephalogram (EEG) is an electrical brain signal in microvolts, which provides unique and important information about the brain dynamics. Frequency of EEG signal lies between 0 and 100 Hz. In epileptic or seizure related studies, decomposition of EEG signal into various frequency sub-bands such as α, β, \( \delta \), \( \theta \), and γ is essential. EEG plays a key role in diagnosis of neurological disorders such as epilepsy. In this paper, we explore decomposition of EEG by db18 wavelet, power spectral density, coherence, energy distribution, and empirical cumulative distribution function of EEGs. This work was carried out to study the changes in the normal and epileptic EEGs with respect to PSD, coherence, energy, and ECDF to check the suitability of these parameters as an input to the classifiers. The methodology was applied mainly to three groups consisting of male and females between the age group of 01–107 years: (1) healthy subjects (normal), (2) subjects with focal seizures, (3) subjects with generalized seizures. The work was carried out on the signals obtained from real subjects to study the EEG-based brain connectivity analysis. It was observed that PSD and coherence study related to the sub-bands reveal more accurate information than the study of complete EEG with or without the seizures.

Welch’s Power Spectral Density of Cranial PPG Signal Using AVR ATmega 8535 Microcontroller

Functional near-infrared spectroscopy is an optical non-invasive technique for measurement of neural activity and hemodynamic response and has a potential for brain mapping. In this work, we aimed to develop a system to capture the cranial photoplethysmogram (CPPG) using IR source (860 nm) and detector (OPT 101). AC excitation for the IR source in the range of 1−2 MHz plays a major role in the CPPG sensor. Brain functional activity in prefrontal lobe is detected by placing the sensor on the forehead. The CPPG signal is captured with eyes open (EO) and eye blinking (EB) activity for various emotions (happy and sad) on prefrontal lobe. Data acquisition is done using AVR ATmega 8535 microcontroller at a sampling rate of 500 Hz. The data are acquired from subjects in the age group of 20−60 years. Using Daubechies 9, six-level wavelet decomposition of CPPG signal is performed, and spectral analysis of each level is done using Welch’s method. From the spectral analysis, it is found the centre frequency of A6 is at 1.953 Hz. The higher frequency part of the signal is found in D4 scale and the centre frequency is 17.58 Hz. All other scales D3−D1 show predominantly the noise part of the signal. Statistical features of A6 and D6 decomposition levels are important for analysis and classification of activity.

Statistical Analysis of Derivatives of Cranial Photoplethysmogram in Young Adults

Every day risk of cardiovascular diseases is increasing in young adults. Now researchers are working on study related to a single bio-signal for prediction of maximum physiological parameters. One of such a bio-signal is photoplethysmogram (PPG). Non-invasive measurement of blood volume change is carried out by using PPG. PPG captured from a cranial site is known as cranial photoplethysmogram (CPPG). Most of the time various bio-signals acquired from the brain are used to study only the brain-related disorders. Near-infrared spectroscopy-based sensor used to record CPPG from frontal region can be used to predict heart rate, oxygen saturation, blood pressure, cardiac output and respiration rate. This paper explains the design specifications of sensor used and study of various time and amplitude indices of differentiated CPPG signal. Authors have studied two levels of differentiation of CPPG obtained by applying MATLAB-based algorithm. Features obtained from differentiated CPPG signal are compared with the standard available values to check the feasibility of this brain signal in the prediction of vascular health. The study was carried out on 19 healthy subjects aged between 19 and 30 years. Our results showed that the optical brain signal used to study hemodynamic changes in brain can also be used for the prediction of vascular health.