Shreyasi Datta

Blood pressure estimation from photoplethysmogram using latent parameters

Non-invasive cuff-less Blood Pressure (BP) estimation from Photoplethysmogram (PPG) is a well known challenge in the field of affordable healthcare. This paper presents a set of improvements over an existing method that estimates BP using 2-element Windkessel model from PPG signal. A noisy PPG corpus is collected using fingertip pulse oximeter, from two different locations in India. Exhaustive pre-processing techniques, such as filtering, baseline and topline correction are performed on the noisy PPG signals, followed by the selection of consistent cycles. Subsequently, the most relevant PPG features and demographic features are selected through Maximal Information Coefficient (MIC) score for learning the latent parameters controlling BP. Experimental results reveal that overall error in estimating BP lies within 10% of a commercially available digital BP monitoring device. Also, use of alternative latent parameters that incorporate the variation in cardiac output, shows a better trend following for abnormally low and high BP.

Analyzing elementary cognitive tasks with Bloom's taxonomy using low cost commercial EEG device

Cognitive load primarily depends on how an individual perceives, assimilates and responds to an external stimulus. We intend to create Electroencephalogram (EEG) models for the cognitive skills defined in the Blooms taxonomy using low cost, commercial EEG devices. This could be applied in educational psychology to provide individual assistance according to ones learning style and abilities. The major challenge in using low resolution EEG device lies in signal analysis with reduced number of channels. In this paper, we present the signature of EEG signals for such low cost devices using three basic tasks namely, number matching; finding characters in text; finding hidden patterns and figures. These tasks map with understand, remember and analyze sub categories of Blooms taxonomy. Different brain regions are activated while performing the above tasks. However, the EEG signals observed on the scalp are the manifestation of the combined effects of various brain regions. The cleaned EEG signals are analyzed using unsupervised clustering of features obtained from different frequency bands. A study is performed on 10 subjects using 14 lead Emotiv neuroheadset, so that one can get further insights on how an individual perceives certain cognitive tasks.

Novel Statistical Post Processing to Improve Blood Pressure Estimation from Smartphone Photoplethysmogram

Blood pressure (BP) is considered to be an important biomarker for cardiac risk estimation. This paper deals with a non-conventional way of estimating BP using smartphone captured Photoplethysmogram (PPG) that enables unobtrusive health monitoring at home for possible alert generation. We have proposed a set of features that are independent to the inbuilt sensor of the capturing device. It is also observed that, BP estimated from a typical smartphone PPG signal fluctuates in successive cardiac cycles due to poor signal quality compared to a medical grade device. Hence, a novel post processing block is introduced, that rejects data depending on the BP distribution over all cardiac cycles in a session. Finally, Half Range Mode is used as a statistical average for the accepted sessions. This post processing methodology outperforms standard statistical averages in providing a better representative BP per session. The methodology yields mean absolute errors of 7.4% and 9.1% for predicting systolic and diastolic pressure respectively when validated over a dataset with a wide variation of BP.

Non Invasive Detection of Coronary Artery Disease Using PCG and PPG

Coronary Artery Disease (CAD) kills more than a million of people every year. However, there is no significant marker for identifying CAD patients unobtrusively. In this paper, we propose a methodology for non invasive screening of CAD patients from heart sound analysis. Instead of segregating the diastolic heart sound as mentioned in prior arts, the proposed methodology extracts spectral features from the entire phonocardiogram (PCG) signal, broken into small overlapping windows. Support vector machine (SVM) is used for classification. Our methodology produces 80% classification accuracy on a dataset of 25 subjects, containing PCG data of both cardiac an non cardiac patients as well as healthy subjects. Results also reveal that a simple transfer function can be formed to identify the CAD patients if photoplethysmogram (PPG) signal is available simultaneously along with PCG.

Artifact Removal from EEG Signals Recorded Using Low Resolution Emotiv Device

Electroencephalogram (EEG) signals are of very low amplitude and are easily contaminated by different types of noises like environmental and of non-cerebral in nature. Thus signal pre-processing is a major challenge while dealing with applications involving EEG signals. The scenario becomes much more complex while using commercially available, low resolution devices as they have fewer electrodes. In this paper, we have applied some of the widely used signal processing techniques to get rid of eye blink and noise related artifacts from EEG signals recorded using a low cost wireless device from Emotiv. Investigations reveal that clustering based eye blink detection method and the skewness based noise detection method give the best detection accuracy. As an example use-case, we show how selective filtering of the EEG signals in the blink regions and removal of noisy windows can help in improving the discrimination power between the two types of color Stroop stimulus based cognitive load analysis. Thus with appropriate signal pre-processing techniques, these low resolution devices can be successfully used to differentiate between different levels of mental workload, which in turn makes these devices useful for non-medical Brain Computer Interface (BCI) applications requiring mass deployment.

Sensor Agnostic Photoplethysmogram Signal Quality Assessment using Morphological Analysis

In this article, we propose a method to assess the clinical usability of fingertip Photoplethysmogram (PPG) waveform, collected from medical grade oximeter (train data) and smartphone (test data). We introduce a set of novel Signal Quality Indices (SQIs) to represent the noise characteristics of the PPG waveform. The SQIs are presented to a random forest classifier to discriminate between clean and noisy signals. The proposed method was evaluated on datasets annotated by four experts, resulting into a sensitivity and specificity of (92 ± 4.7 %, 95 ± 3 %) and (82.6 ± 4.6 %, 95.4 ± 3.1 %) on train and test data respectively. Further we applied the proposed method on PPG waveform of clinically proven control and disease population of Coronary Artery Disease (CAD), which resulted into (77 %, 77 %) of sensitivity and specificity respectively.