Revati Shriram

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.

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.

Coherence Analysis of EEG Signal Using Power Spectral Density

Coherence analysis can be detect for coordination of EEG rhythms between brain areas. It could not reflect the dynamic properties changing with time. Coherence analysis is a method developed on base of classic coherence analysis and signals joint time-frequency representations in recent years. It was used to extract transient characteristics of interactions among brain areas. It describes the temporal, spatial and frequency relationships of brain activities. This paper discuss the frequency-varying coherence of EEG (Electroencephalogram) to study the coordination mechanism of brain. The power spectral density is the frequency-varying method to study the coordination mechanism of brain areas. The Welch method and the periodogram method are the two methods to study the power spectral density. The results showed that appropriate methods were effective tools for EEG to study the coordination between brain areas.

Dental biometrics for human identification based on dental work and image properties in Periapical radiographs

The main objective of forensic dentistry is to identify people based on their dental records, mainly as radiograph images. In this paper we attempt to present dental biometric system for preprocessing, segmentation and matching of dental image. Dental biometric system is used in forensic science, in which we need to match Postmortem (PM) radiographs with antemortem (AM) radiographs to determine the identity of the person associated with the PM image. We use a semi-automatic method to extract area, histogram features and contour of teeth from the AM and PM radiographs. We present results of extraction of tooth and dental work shape. These features are used in matching of two radiographs and based on this matching, individuals can be identified. This paper presents matching of two radiographs based on histogram properties, area of tooth and dental work. Along with these three matching techniques one more technique is used which is based on control point selection. Initial experimental results tested on small database indicate that matching of teeth based on area and contour is feasible method for human identification.

Performance Parameter Based Comparison of the Slantlet Transform and Discrete Cosine Transform (DCT) for Steganography in Biomedical Signals

In this paper, we present patient’s information hiding using the Slantlet Transform and Discrete Cosine Transform (DCT). DCT transforms the signal from spatial domain to frequency domain. It can separate the image into high-, middle- and low-frequency components. In DCT-based technique, insertion of secret information in carrier depends on the DCT coefficients. The Slantlet Transform is known as Orthogonal Discrete Wavelet Transform (ODWT). It separates 1-D signal in two sub-bands, LL and HH. It divides 2-D signal into four sub-bands, HH, HL, LH and LL and the secret information is embedded in these sub-bands. At first, DCT is used for data hiding. For that, 1-D ECG signal and some biomedical images (2-D) are used as cover signal. The secret information is embedded in lower DCT coefficients. Similarly, Slantlet transform is used for hiding patient’s data in same biomedical signals ECG and biomedical images, respectively. Original signals and embedded signals are compared. To analyse the performance of the transforms used for data hiding, Mean Squared Error (MSE), Normalised Root Mean Squared Error (NRMSE) and Peak Signal to Noise Ratio (PSNR) are calculated. MSE between original and embedded signals is less.

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.

Robust Estimation of Brain Functional Connectivity from Functional Magnetic Resonance Imaging Using Power, Cross-Correlation and Cross-Coherence

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive method for investigating the structure and function of the brain. Using fMRI, brain functions and areas responsible for the particular activities are investigated. The objective of the image processing methods using fMRI is to investigate the functional connectivity. To localize mental functions of specific brain regions and to identify the brain regions, those are activated simultaneously. Correlation and cross-coherence of the time series of the pixels are used for the detection of functional connectivity in fMRI images for the different motor movements (upper and lower limb movement and finger tapping action). The methodology was applied to three groups (six subjects) consisting aged between 10 and 75 years: (1) Normal and healthy subject performing finger tapping actions, (2) brain tumour patient performing lower limb movement (LL), and (3) brain tumour patient performing upper limb movements (UL). The threshold applied for the cross-correlation is 5000. Similarly, the threshold applied for cross-coherence and power parameters is in the range of (0.6–0.9). The algorithm implemented is found to be non-destructive, and there is no loss of temporal or spatial data. The result shows that for the normal subject, functionally connected pixels are more as compared to the brain tumour patients.

Real time text detection and recognition on hand held objects to assist blind people

This paper presents camera based system which will help blind person for reading text patterns printed on hand held objects. This is the framework to assist visually impaired persons to read text patterns and convert it into the audio output. To obtain the object from the background and extract the text pattern from that object, the system first proposes the method that will capture the image from the camera and object region is detected. The text which are maximally stable are detected using Maximally Stable External Regions (MSER) feature. A novel algorithm is evaluated on variety of scenes. The detected text is compared with the template and converted into the speech output. The text patterns are localized and binarized using Optical Character Recognition (OCR). The recognized text is converted to an audio output. The speech output is given to the blind user. Experimental results shows the analysis of MSER and OCR for different text patterns. MSER shows that it is robust algorithm for the text detection. Therefore, this paper deals with analysis of detection and recognition of different text patterns on different objects.