Inaki Romero

Bio: Inaki Romero is an academic researcher from IMEC. The author has contributed to research in topics: Beat detection & Adaptive filter. The author has an hindex of 15, co-authored 27 publications receiving 582 citations. Previous affiliations of Inaki Romero include German National Metrology Institute & Mayo Clinic.

Novel Measure of Local Impedance Predicts Catheter–Tissue Contact and Lesion Formation

Abstract: Background: Coupling between the ablation catheter and myocardium is critical to resistively heat tissue with radiofrequency ablation. The objective of this study was to evaluate whether a novel local impedance (LI) measurement on an ablation catheter identifies catheter–tissue coupling and is predictive of lesion formation. Methods and Results: LI was studied in explanted hearts (n=10 swine) and in vivo (n=10; 50–70 kg swine) using an investigational electroanatomic mapping system that measures impedance from an ablation catheter with mini-electrodes incorporated in the distal electrode (Rhythmia and IntellaNav MiFi OI, Boston Scientific). Explanted tissue was placed in a warmed (37 °C) saline bath mounted on a scale, and LI was measured 15 mm away from tissue to 5 mm of catheter–tissue compression at multiple catheter angles. Lesions were created with 31 and 50 W for 5 to 45 seconds (n=90). During in vivo evaluation of LI, measurements of myocardium (n=90) and blood pool (n=30) were guided by intracardiac ultrasound while operators were blinded to LI data. Lesions were created with 31 and 50 W for 45 seconds in the ventricles (n=72). LI of myocardium (119.7 Ω) was significantly greater than that of blood pool (67.6 Ω; P R 2 =0.75 versus R 2 =0.54) and generator impedance drop ( R 2 =0.82 versus R 2 =0.58). Steam pops displayed a significantly higher starting LI and larger ΔLI compared with successful radiofrequency applications ( P Conclusions: LI recorded from miniature electrodes provides a valuable measure of catheter–tissue coupling, and ΔLI is predictive of lesion formation during radiofrequency ablation.

Ultra-low-power wearable biopotential sensor nodes

Abstract: 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

Abstract: 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.

PCA and ICA applied to noise reduction in multi-lead ECG

Abstract: The performance of PCA and ICA in the context of cleaning noisy ECGs in ambulatory conditions was investigated. With this aim, ECGs with artificial motion artifacts were generated by combining clean 8-channel ECGs with 8-channel noise signals at SNR values ranging from 10 down to −10 dB. For each SNR, 600 different simulated ECGs of 10-second length were selected. 8-channel PCA and ICA were applied and then inverted after selecting a subset of components. In order to evaluate the performance of PCA and ICA algorithms, the output of a beat detection algorithm was applied to both the output signal after PCA/ICA filtering and compared to the detections in the signal before filtering. Applying both PCA and ICA and retaining the optimal component subset, yielded sensitivity (Se) of 100% for all SNR values studied. In terms of Positive predictivity (+P), applying PCA, yielded to an improvement for all SNR values as compared to no cleaning (+P=95.45% vs. 83.09% for SNR=0dB; +P=56.87% vs. 48.81% for SNR=−10dB). However, ICA filtering gave a higher improvement in +P for all SNR values (+P=100.00% for SNR=0dB; +P=61.38% for SNR=−10dB). An automatic method for selecting the components was proposed. By using this method, both PCA and ICA gave an improvement as compared to no filtering over all SNR values. ICA had a better performance (SNR=−5dB, improvement in +P of 8.33% for PCA and 22.92% for ICA).

Compressed Sensing for Real-Time Energy-Efficient ECG Compression on Wireless Body Sensor Nodes

Abstract: Wireless body sensor networks (WBSN) hold the promise to be a key enabling information and communications technology for next-generation patient-centric telecardiology or mobile cardiology solutions. Through enabling continuous remote cardiac monitoring, they have the potential to achieve improved personalization and quality of care, increased ability of prevention and early diagnosis, and enhanced patient autonomy, mobility, and safety. However, state-of-the-art WBSN-enabled ECG monitors still fall short of the required functionality, miniaturization, and energy efficiency. Among others, energy efficiency can be improved through embedded ECG compression, in order to reduce airtime over energy-hungry wireless links. In this paper, we quantify the potential of the emerging compressed sensing (CS) signal acquisition/compression paradigm for low-complexity energy-efficient ECG compression on the state-of-the-art Shimmer WBSN mote. Interestingly, our results show that CS represents a competitive alternative to state-of-the-art digital wavelet transform (DWT)-based ECG compression solutions in the context of WBSN-based ECG monitoring systems. More specifically, while expectedly exhibiting inferior compression performance than its DWT-based counterpart for a given reconstructed signal quality, its substantially lower complexity and CPU execution time enables it to ultimately outperform DWT-based ECG compression in terms of overall energy efficiency. CS-based ECG compression is accordingly shown to achieve a 37.1% extension in node lifetime relative to its DWT-based counterpart for “good” reconstruction quality.

Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans

Abstract: Background— The identification of sites of dominant activation frequency during atrial fibrillation (AF) in humans and the effect of ablation at these sites have not been reported. Methods and Results— Thirty-two patients undergoing AF ablation (19 paroxysmal, 13 permanent) during ongoing arrhythmia were studied. Electroanatomic mapping was performed, acquiring 126±13 points per patient throughout both atria and coronary sinus. At each point, 5-second electrograms were obtained to determine the highest-amplitude frequency on spectral analysis and to construct 3D dominant frequency (DF) maps. The temporal stability of the recording interval was confirmed in a subset. Ablation was performed with the operator blinded to the DF maps. The effect of ablation at sites with or without high-frequency DF sites (maximal frequencies surrounded by a decreasing frequency gradient ≥20%) was evaluated by determining the change in AF cycle length (AFCL) and the termination and inducibility of AF. The spatial distribution ...

A 30 $\mu$ W Analog Signal Processor ASIC for Portable Biopotential Signal Monitoring

Abstract: This paper presents the design and implementation of an analog signal processor (ASP) ASIC for portable ECG monitoring systems The ASP ASIC performs four major functionalities: 1) ECG signal extraction with high resolution, 2) ECG signal feature extraction, 3) adaptive sampling ADC for the compression of ECG signals, 4) continuous-time electrode-tissue impedance monitoring for signal integrity monitoring These functionalities enable the development of wireless ECG monitoring systems that have significantly lower power consumption yet that are more capable than their predecessors The ASP has been implemented in 05 μm CMOS process and consumes 30 μW from a 2 V supply The noise density of the ECG readout channel is 85 nV/√Hz and the CMRR is better that 105 dB The adaptive sampling ADC is capable of compressing the ECG data by a factor of 7 and the heterodyne chopper readout extracts the features of the ECG signals Combination of these two features leads to a factor 4 reduction in the power consumption of a wireless ECG monitoring system Furthermore, the proposed continuous-time impedance monitoring circuit enables the monitoring of the signal integrity

Physiological monitoring device

Abstract: The present invention relates to a physiological monitoring device 100. Some embodiments of the invention allow for long-term monitoring of physiological signals. Further embodiments may also allow for the monitoring of secondary signals such as motion. Abnormal heart rhythms, or arrhythmias, may cause various types of symptoms, such as loss of-consciousness, palpitations, dizziness, or even death. An arrhythmia that causes such symptoms is often an indicator of significant underlying heart disease. It is important to identify when such symptoms are due to an abnormal heart rhythm, since treatment with various procedures, such as pacemaker. implantation or percutaneous catheter ablation, can successfully ameliorate these problems and prevent significant symptoms and death.

A 345 µW Multi-Sensor Biomedical SoC With Bio-Impedance, 3-Channel ECG, Motion Artifact Reduction, and Integrated DSP

Abstract: This paper presents a MUlti-SEnsor biomedical IC (MUSEIC). It features a high-performance, low-power analog front-end (AFE) and fully integrated DSP. The AFE has three biopotential readouts, one bio-impedance readout, and support for general-purpose analog sensors The biopotential readout channels can handle large differential electrode offsets ( ${\pm} $ 400 mV), achieve high input impedance ( ${>}$ 500 M $\Omega$ ), low noise ( ${ 620 nVrms in 150 Hz), and large CMRR ( ${>}$ 110 dB) without relying on trimming while consuming only 31 $\mu$ W/channel. In addition, fully integrated real-time motion artifact reduction, based on simultaneous electrode-tissue impedance measurement, with feedback to the analog domain is supported. The bio-impedance readout with pseudo-sine current generator achieves a resolution of 9.8 m $\Omega$ / $\surd$ Hz while consuming just 58 $\mu$ W/channel. The DSP has a general purpose ARM Cortex M0 processor and an HW accelerator optimized for energy-efficient execution of various biomedical signal processing algorithms achieving 10 $\times$ or more energy savings in vector multiply-accumulate executions.