Somdeb Majumdar

Artifacts mitigation in ambulatory ECG telemetry

In remote monitoring applications of vital signs including ECG, it is extremely important to ensure that the diagnostic integrity of the signals is not compromised due to the presence of sensing artifacts and channel errors. It is also important for the platform to be extremely power efficient in order to facilitate wearable sensors with user friendly form factors. We present a novel, low power application layer solution that is agnostic to wireless protocols and mitigates artifacts due to packet losses in Body Area Networks (BANs). In our previous work, we presented initial results based on this approach and demonstrated that greater than 99% beat detection accuracy can be achieved even at a packet loss rate as high as 20%. Our contributions in this work include validation of the above on a platform with an ultra low power wearable single lead ECG pendant. We present details of implementation and then extend the platform to mitigate ECG sensing artifacts including power line interference and baseline wandering. The proposed approach enables us to offload most of the complex processing from sensor nodes to the receiver node with better a battery budget, for improved sensor life. Finally, present a qualitative and quantitative assessment of the system.

Diagnostic grade wireless ECG monitoring

In remote monitoring of Electrocardiogram (ECG), it is very important to ensure that the diagnostic integrity of signals is not compromised by sensing artifacts and channel errors. It is also important for the sensors to be extremely power efficient to enable wearable form factors and long battery life. We present an application of Compressive Sensing (CS) as an error mitigation scheme at the application layer for wearable, wireless sensors in diagnostic grade remote monitoring of ECG. In our previous work, we described an approach to mitigate errors due to packet losses by projecting ECG data to a random space and recovering a faithful representation using sparse reconstruction methods. Our contributions in this work are twofold. First, we present an efficient hardware implementation of random projection at the sensor. Second, we validate the diagnostic integrity of the reconstructed ECG after packet loss mitigation. We validate our approach on MIT and AHA databases comprising more than 250,000 normal and abnormal beats using EC57 protocols adopted by the Food and Drug Administration (FDA). We show that sensitivity and positive predictivity of a state-of-the-art ECG arrhythmia classifier is essentially invariant under CS based packet loss mitigation for both normal and abnormal beats even at high packet loss rates. In contrast, the performance degrades significantly in the absence of any error mitigation scheme, particularly for abnormal beats such as Ventricular Ectopic Beats (VEB).

Low complexity sensors for body area networks

In this work, we present signal processing approaches to offload complexity from resource constrained sensor nodes to gateway/receiver nodes with better power, memory and CPU budgets. We consider the resources in commercially available cell phone platforms to fill the role of both gateway and receiver nodes in emerging Body Sensor Networks, with applications in healthcare. We leverage Compressed Sensing (CS), wherein signals can be reconstructed fairly accurately with high probability from significantly fewer measurements than that suggested by the Nyquist-Shannon sampling rate, albeit with additional complexity at the receiver. This enables receiver nodes with better resource budgets to leverage computationally intensive signal processing algorithms in lieu of on-board processing at the sensor node. We show that aliasing can be avoided at the sensor by trading analog domain complexity for a modest increase in digital domain complexity with synthetic examples and real-time pulse oximeter implementation. We describe ways to leverage receiver resources for mitigating packet losses and sensing artifacts and present experimental results with ECG. Finally, we motivate multi-sensor fusion at the receiver and show that CS paradigm can be used to reduce sensor complexity with sloppy clock management schemes.

A closed-loop system for artifact mitigation in ambulatory electrocardiogram monitoring

Motion artifacts interfere with electrocardiogram (ECG) detection and information processing. In this paper, we present an independent component analysis based technique to mitigate these signal artifacts. We propose a new statistical measure to enable an automatic identification and removal of independent components, which correspond to the sources of noise. For the first time, we also present a signal-dependent closed-loop system for the quality assessment of the denoised ECG. In one experiment, noisy data is obtained by the addition of calibrated amounts of noise from the MIT-BIH NST database to the AHA ECG database. Arrhythmia classification based on a state-of-the-art algorithm with the direct use of noisy data thus obtained shows sensitivity and positive predictivity values of 87.7% and 90.0%, respectively, at an input signal SNR of -9 dB. Detection with the use of ECG data denoised by the proposed approach exhibits significant improvement in the performance of the classifier with the corresponding results being 96.5% and 99.1%, respectively. In a related lab trial, we demonstrate a reduction in RMS error of instantaneous heart rate estimates from 47.2% to 7.0% with the use of 56 minutes of denoised ECG from four physically active subjects. To validate our experiments, we develop a closed-loop, ambulatory ECG monitoring platform, which consumes 2.17 mW of power and delivers a data rate of 33 kbps over a dedicated UWB link.

Blood oxygen estimation from compressively sensed photoplethysmograph

In this work, we consider low power, wearable pulse oximeter sensors for ambulatory, remote vital signs monitoring applications. It is extremely important for such sensors to maintain clinical accuracy and yet provide power savings to enable non-intrusive, long lasting sensors. Our contributions in this work include sub-Nyquist, random sampling of evanescent red and infra red (IR) photoplethysmograph (PPG) signals in real time under the Compressed Sensing (CS) paradigm. We describe the real time platform and demonstrate that the SpO2 accuracy is not compromised due to aliasing of ambient light artifacts, even when average number of measurements is much below that of Nyquist rate. We briefly discuss the various modules contributing to overall power consumption of a wireless pulse oximeter sensor and show that 10x reductions in LED power and radio power are possible, without sacrificing the SpO2 accuracy.