Iñaki Romero

Ultra-low-power wearable biopotential sensor nodes

This paper discusses ultra-low-power wireless sensor nodes intended for wearable biopotential monitoring. Specific attention is given to mixed-signal design approaches and their impact on the overall system power dissipation. Examples of trade-offs in power dissipation between analog front-ends and digital signal processing are also given. It is shown how signal filtering can further reduce the internal power consumption of a node. Such power saving approaches are indispensable as real-life tests of custom wireless ECG patches reveal the need for artifact detection and correction. The power consumption of such additional features has to come from power savings elsewhere in the system as the overall power budget cannot increase.

Robust beat detector for ambulatory cardiac monitoring

Robust beat detection under noisy conditions is required in order to obtain a correct clinical interpretation of the ECG in ambulatory settings. This paper describes the evaluation and optimization of a beat detection algorithm that is robust against high levels of noise. An evaluation protocol is defined in order to study four different characteristics of the algorithm: non-rhythmic patterns, different levels of SNR, exact peak detection and different levels of physical activity. This protocol is based on the MIT/BIH arrhythmia database and additional ECG recordings obtained under different levels of physical activity measured by 2-axis accelerometers. The optimized algorithm obtained a Se=99.65% and +P=99.79% on the MIT/BIH arrhythmia database while keeping a good performance on ECGs with high levels of activity (overall of Se=99.86%, +P=99.91%). In addition, this method was optimized to work in real time, for future implementation in a Wireless ECG sensor based on a microprocessor.

Low-power robust beat detection in ambulatory cardiac monitoring

With new advances in ambulatory monitoring new challenges appear due to degradation in signal quality and limitations in hardware requirements. Existing signal analysis methods should be re-evaluated in order to adapt to the restrictive requirements of these new applications. With this motivation, we chose a robust beat detection algorithm and optimized it further to be running in an embedded platform within a cardiac monitoring sensor node. The algorithm was designed in floating point in Matlab and evaluated in order to study its performance under a wide range of conditions. The initial PC version of the algorithm obtained a good performance under a wide variety of conditions (Se = 99.65% and + P = 99.79% on the MIT/BIH arrhythmia database and Se = 99.88%, + P = 99.93% on our own database with ambulatory data). In this study, the algorithm is adapted and further optimized to work in real time on an embedded digital processor, while keeping this performance without degradation. The run-time memory usage of the application was of 150 KB with an execution time of 1.5 million cycles and an average power consumption of 494 ¿W for an ECG of 3 seconds length and sampling frequency of 198 Hz. The algorithm implementation in a general purpose processor will put significant limits on the performance in terms of power consumption. We propose possible specifications for an application-optimized processor for more efficient ECG analysis.

Motion artifact reduction in ambulatory ECG monitoring: an integrated system approach

Recent advances in low-power micro-electronics are revolutionizing ECG monitoring. Wearable patches now allow comfortable monitoring over several days. Achieving reliable and high integrity recording however remains a challenge, especially under daily-life activities. In this paper we present a system approach to motion artifact reduction in ambulatory recordings. A custom ultra-low-power ECG analog front-end read-out for simultaneous measurement of ECG and electrode-tissue impedance, from the same electrode, is reported. Integrating this front-end, we describe a wireless patch for the monitoring of 3-lead ECG, electrode electrical artifact and 3D-acceleration. Beyond ECG monitoring, this wireless patch provides the additional necessary data to filter out motion artifact. Two algorithm methods are tested. The first method applies ICA for de-noising multi-lead ECG recordings. The second method is an adaptive filter that uses skin/electrode impedance as the measurement of noise. Algorithms, circuits and system provide a platform for reliable ECG monitoring on-the-move.

Ultra low power wireless ECG system with beat detection and real time impedance measurement

A wireless ECG monitoring system is presented that is able to perform high-quality ECG signal acquisition, beat detection, and real time monitoring of skin-electrode impedance which can be used to monitor the presence of motion artefacts. The whole system consumes only 170μW while performing local beat detection. The beat detection algorithm was verified against the MIT-BIH arrhythmia database and obtains a median sensitivity of 99.74% and positive predictivity of 99.87%. The system was validated using applied signals at varying signal to noise ratio as well as on human volunteers.

Ultra low power application specific instruction-set processor design for a cardiac beat detector algorithm

High efficiency and low power consumption are among the main topics in embedded systems today. For complex applications, off-the-shelf processor cores might not provide the desired goals in terms of power consumption. By optimizing the processor for the application, one can improve the computing power by introducing special purpose hardware units. In this paper, we present a case study with a possible design methodology for an ultra low power application specific instruction-set processor. A cardiac beat detector algorithm based on the Continuous Wavelet Transform is implemented in the C language. This application is further optimized using several software power optimization techniques. The resulting application is mapped on a basic processor architecture provided by Target Compiler Technologies, and the processor is further optimized for ultra low power consumption by applying application specific hardware, and by using several hardware optimization techniques. The optimized processor is compared with the unoptimized version, resulting in a 55% reduction in power consumption. The reduction in the total execution cycle count is 81%. Power gating, and dynamic voltage and frequency scaling, are investigated for further power optimization. For a given case, the reduction in the already optimized power consumption is estimated to be 62% and 35%, respectively.

Precordial electrode placement for optimal ECG monitoring: Implications for ambulatory monitor devices and event recorders

INTRODUCTION Detection of QRS complexes, P-waves and atrial fibrillation f-waves in electrocardiographic (ECG) signals is critical for the correct diagnosis of arrhythmias. We aimed to find the best bipolar lead (BL) with the highest signal amplitude and shortest inter-electrode spacing. METHODS ECG signals (120 seconds) were recorded in 36 patients with 16 precordial electrodes placed in a standardized pattern. An average signal was analysed for each of 120 possible BLs obtained by calculating the difference between pairs of unipolar leads. Peak-to-peak amplitudes of QRS waves (50ms around R-peak) and P waves (270-70ms before R-peak) were calculated. For patients with atrial fibrillation, power of the fibrillatory (f) wave was used instead. Maximum values at each distance were considered and differentiation analysis was performed based on incremental changes (amplitude to distance). RESULTS There was a significant correlation between distance and QRS-amplitude (r=0.78, p<0.001), P-wave amplitude (r=0.60, p<0.01) and f-wave power (r=0.79, p<0.001). The range of values was: QRS-amplitude 0.7-2.33mV, P-wave amplitude 0.07-0.18mV, and f-wave power 0.55-2.12mV(2)/s. The maximum value for the shortest distance was on a heart-aligned axis over the left ventricle for the QRS complex (1.9mV at 8.7cm) and over the atria for the P-wave (0.98mV) and f-waves (1.45mV(2)/s at 8cm, respectively). CONCLUSION There is a strong positive correlation between electrode distance and ECG signal-amplitude. Distance of 8cm on a heart-aligned axis and over the relevant heart-chamber provides the highest signal amplitude for the shortest distance. These findings are essential for the design and use of ambulatory monitoring devices.

Comparative study of algorithms for ECG segmentation

Abstract Accurate automatic identification of fiducial points within an ECG is required for the automatic interpretation of this signal. Several methods exist in the literature for automatic ECG segmentation. These algorithms are based on different methodologies and often evaluated with different datasets and protocols, which makes their performance challenging to compare. For this study, nine segmentation algorithms were selected from the literature and evaluated with the same protocol in order to study their performance. One hundred signals from the PhysioNet’s QT database were used for this evaluation. Results showed that one of the algorithms based in the discrete wavelet transform achieved sensitivity of 100% when detecting the onset and offset of the QRS complex. An algorithm using the Multi-scale Morphological Derivate achieved sensitivities of 99.81%, 98.17% and 99.56% when detecting the peak, onset and offset respectively of the P-wave. When segmenting the T-wave, an algorithm based on the Phasor transform had a good performance with sensitivities of 97.78%, 97.81% and 95.43% when detecting the peak, onset and offset, respectively. Additionally, probabilistic methods such as Hidden Markov Models had good results due to the fact that they can learn from real signals and adapt to specific conditions. However, these techniques are often computationally more complex and require training. This study could help in selecting optimal algorithms for ECG segmentation when implementing a system for automatic ECG interpretation.

Robust heart rhythm calculation and respiration rate estimation in ambulatory ECG monitoring

Heart rhythm and respiration rate are two vital signs that are of interest for ambulatory monitoring. However, noise due to activity in ambulatory monitoring complicates the ECG interpretation. This paper describes a set of algorithms to robustly monitor a subjects heart rhythm and respiration rate in an ambulatory environment. To ensure robustness against motion artifacts, we have developed an algorithm for motion artifact reduction based on independent component analysis with a multi-lead ECG system. From the robust heart-rate calculations, we use an ECG derived respiration (EDR) algorithm based on heart-rate variability (HRV) to estimate the respiration rate.