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Novel approach for calibration-free cuffless BP estimation; a potential tool for local BP measurement and hypertension screening.

A reliable system for cuffless BP measurement and local estimation of arterial wall properties.

Image segmentation is a primary task in many medical applications. Recently, many deep networks derived from U-Net has been extensively used in various medical image segmentation tasks. However, in most of the cases, networks similar to U-net produce coarse and non-smooth segmentations with lots of discontinuities. To improve and refine the performance of U-Net like networks, we propose the use of parallel decoders which along with performing the mask predictions also perform contour prediction and distance map estimation. The contour and distance map aid in ensuring smoothness in the segmentation predictions. To facilitate joint training of three tasks, we propose a novel architecture called Psi-Net with a single encoder and three parallel decoders (thus having a shape of Ψ), one decoder to learn the segmentation mask prediction and other two decoders to learn the auxiliary tasks of contour detection and distance map estimation. The learning of these auxiliary tasks helps in capturing the shape and the boundary information. We also propose a new joint loss function for the proposed architecture. The loss function consists of a weighted combination of Negative Log Likelihood and Mean Square Error loss. We have used two publicly available datasets: 1) Origa dataset for the task of optic cup and disc segmentation and 2) Endovis segment dataset for the task of polyp segmentation to evaluate our model. We have conducted extensive experiments using our network to show our model gives better results in terms of segmentation, boundary and shape metrics.

Measurement of arterial distensibility is very important in cardiovascular diagnosis for early detection of coronary heart disease and possible prediction of future cardiac events. Conventionally, B-mode ultrasound imaging systems have been used along with expensive vessel wall tracking systems for estimation of arterial distension and calculation of various estimates of compliance. We present a simple instrument for noninvasive in vivo evaluation of arterial compliance using a single element ultrasound transducer. The measurement methodology is initially validated using a proof of concept pilot experiment using a commercial ultrasound pulser-receiver. A prototype system is then developed around a PXI chassis using LABVIEW software. The virtual instrument employs a dynamic threshold algorithm to identify the artery walls and then utilizes a correlation based tracking technique to estimate arterial distension. The end-diastolic echo signals are averaged to reduce error in the automated diameter measurement process. The instrument allows automated measurement of the various measures of arterial compliance with minimal operator intervention. The performance of the virtual instrument was first analyzed using simulated data sets to establish the maximum measurement accuracy achievable under different input signal to noise ratio (SNR) levels. The system could measure distension with accuracy better than 10 μm for positive SNR. The measurement error in diameter was less than 1%. The system was then thoroughly evaluated by the experiments conducted on phantom models of the carotid artery and the accuracy and resolution were found to meet the requirements of the application. Measurements performed on human volunteers indicate that the instrument can measure arterial distension with a precision better than 5%. The end-diastolic arterial diameter can be measured with a precision better than 2% and an accuracy of 1%. The measurement system could lead to the development of small, portable, and inexpensive equipment for estimation of arterial compliance suitable in mass screening of “at risk” patients. The automated compliance measurement algorithm implemented in the instrument requires minimal operator input. The instrument could pave the way for dedicated systems for arterial compliance evaluation targeted at the general medical practitioner who has little or no expertise in vascular ultrasonography.

An ultra-low power ECG platform for continuous and minimally intrusive monitoring for systems with minimal processing capabilities, is presented in this paper. The platform is capable of detecting abnormalities in the ECG signal by extracting and analyzing features related to various cardiac trends. The platform is built to continuously operate on any of the 12 leads and the presented work includes a single lead implementation that works on lead I or II. A single lead, wearable ECG patch that can detect rhythm based arrhythmias and continuously monitor beat-to-beat heart rate and respiratory rate has been developed. In addition, the device stores raw ECG waveform locally and is designed to run for 10 days on a single charge. The ECG patch works in conjunction with a front end device or tablet and updates the results on the tablet interface. Upon detection of an abnormality or an arrhythmia the device switches to an ECG visualization mode enabling manual analysis on the acquired signal. The front end device also functions as a gateway for remote monitoring. The functionality and processing capabilities of the platform along with the validation tests carried out in a controlled setting are presented.

Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is seldom used outside the intensive care ward due to the large size and cost of the monitoring system. While Electrocardiogram (ECG) based respiration extraction is a validated approach, its adoption is limited by access to a suitable continuous ECG monitor. Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring. Research in this domain, however, has been predominantly focussed on estimating respiration rate from PPG. In this work, a novel end-to-end deep learning network called RespNet is proposed to perform the task of extracting the respiration signal from a given input PPG as opposed to extracting respiration rate. The proposed network was trained and tested on two different datasets utilizing different modalities of reference respiration signal recordings. Also, the similarity and performance of the proposed network against two conventional signal processing approaches for extracting respiration signal were studied. The proposed method was tested on two independent datasets with a Mean Squared Error of 0.262 and 0.145. The cross-correlation coefficient of the respective datasets were found to be 0.933 and 0.931. The reported errors and similarity was found to be better than conventional approaches. The proposed approach would aid clinicians to provide comprehensive evaluation of sleeprelated respiratory conditions and chronic respiratory ailments while being comfortable and inexpensive for the patient.

Objective: Work stress is identified as the 'health epidemic of 21st century' by WHO because, when left unchecked, it wreaks havoc on human mind and body by accelerating the onset and progression of several health disorders. Hence, the evolution of strategies for early detection of mental stress is pivotal. The study presented here is one step towards the goal of developing a physiological parameter based psychological stress detection scheme which can further be incorporated into a wearable vital signs monitor. Approach: A group of 34 subjects (14 females and 20 males, age: 21.4 ± 1.7 years; mean ± SD) volunteered to participate in a pilot laboratory intervention that emulated real-life job stress scenarios by incorporating stress factors like mental workload, time pressure, performance pressure and social evaluative threat. Electrodermal Activity (EDA), Electrocardiogram (ECG), and Skin Temperature (ST) were monitored throughout the experiment to capture sympathetic activation during stress. Stress response elicitation was validated using salivary cortisol levels. A total of 61 features were extracted from these signals and four classifiers were investigated regarding their ability to detect 'stress' using single and multimodal schemes. A fusion framework that combined the benefits of feature fusion and decision fusion was employed to generate classifier ensembles for multimodal stress detection schemes. As the generated datasets exhibited a class imbalance issue, three separate schemes for class imbalance rectification viz., undersampling, oversampling and SMOTE were investigated concerning their ability to yield the best classification performance. While ECG based performance analysis was restricted to data segments of 300 s duration to conform to international guidelines for short-term HRV analysis, non-overlapping EDA and ST data segments of durations 300 s, 180 s, 60 s, and 30 s were examined to determine the optimum data length that can generate best results. Main Results: EDA gave a superior performance for 60 s windows while ST performed best with data segments of duration 30 s. A comparative study was performed with 25%, 50%, 75% and 90% overlapping data segments as well. However, overlapping did not enhance the performance of the classifiers significantly.While EDA emerged as the best single modality, the highest stress recognition accuracy of 97.13% was yielded by a combination of EDA and ST with data segments of 60 s duration. Furthermore, the differential effect of 'physical' and 'psychological' stressors on EDA and ST was analyzed. Significance: The results clearly suggest that these physiological parameters can not only reliably detect psychological stress but can also discriminate it from physical stress.