Nightingale V2: Low-power Compact-sized Multi-Sensor Platform for Wearable Health Monitoring.

Nightingale V2, a new wearable multi-sensor platform, is introduced in this paper. It can measure various bio-signals including ECG, BioZ, PPG, motion and heart sounds simultaneously at very low power consumption. Patient safety with optical and electrical sensors were carefully investigated. Preliminary data collection results as well as the sensor characterization results reveal that the Nightingale V2 would be suitable for wearable health monitoring applications.

Wearable, wireless EEG solutions in daily life applications: what are we missing?

Monitoring human brain activity has great potential in helping us understand the functioning of our brain, as well as in preventing mental disorders and cognitive decline and improve our quality of life. Noninvasive surface EEG is the dominant modality for studying brain dynamics and performance in real-life interaction of humans with their environment. To take full advantage of surface EEG recordings, EEG technology has to be advanced to a level that it can be used in daily life activities. Furthermore, users have to see it as an unobtrusive option to monitor and improve their health. To achieve this, EEG systems have to be transformed from stationary, wired, and cumbersome systems used mostly in clinical practice today, to intelligent wearable, wireless, convenient, and comfortable lifestyle solutions that provide high signal quality. Here, we discuss state-of-the-art in wireless and wearable EEG solutions and a number of aspects where such solutions require improvements when handling electrical activity of the brain. We address personal traits and sensory inputs, brain signal generation and acquisition, brain signal analysis, and feedback generation. We provide guidelines on how these aspects can be advanced further such that we can develop intelligent wearable, wireless, lifestyle EEG solutions. We recognized the following aspects as the ones that need rapid research progress: application driven design, end-user driven development, standardization and sharing of EEG data, and development of sophisticated approaches to handle EEG artifacts.

Soft, comfortable polymer dry electrodes for high quality ECG and EEG recording.

Conventional gel electrodes are widely used for biopotential measurements, despite important drawbacks such as skin irritation, long set-up time and uncomfortable removal. Recently introduced dry electrodes with rigid metal pins overcome most of these problems; however, their rigidity causes discomfort and pain. This paper presents dry electrodes offering high user comfort, since they are fabricated from EPDM rubber containing various additives for optimum conductivity, flexibility and ease of fabrication. The electrode impedance is measured on phantoms and human skin. After optimization of the polymer composition, the skin-electrode impedance is only ~10 times larger than that of gel electrodes. Therefore, these electrodes are directly capable of recording strong biopotential signals such as ECG while for low-amplitude signals such as EEG, the electrodes need to be coupled with an active circuit. EEG recordings using active polymer electrodes connected to a clinical EEG system show very promising results: alpha waves can be clearly observed when subjects close their eyes, and correlation and coherence analyses reveal high similarity between dry and gel electrode signals. Moreover, all subjects reported that our polymer electrodes did not cause discomfort. Hence, the polymer-based dry electrodes are promising alternatives to either rigid dry electrodes or conventional gel electrodes.

The impact of head movements on EEG and contact impedance: an adaptive filtering solution for motion artifact reduction.

Designing and developing a comfortable and convenient EEG system for daily usage that can provide reliable and robust EEG signal, encompasses a number of challenges. Among them, the most ambitious is the reduction of artifacts due to body movements. This paper studies the effect of head movement artifacts on the EEG signal and on the dry electrode-tissue impedance (ETI), monitored continuously using the imec's wireless EEG headset. We have shown that motion artifacts have huge impact on the EEG spectral content in the frequency range lower than 20 Hz. Coherence and spectral analysis revealed that ETI is not capable of describing disturbances at very low frequencies (below 2 Hz). Therefore, we devised a motion artifact reduction (MAR) method that uses a combination of a band-pass filtering and multi-channel adaptive filtering (AF), suitable for real-time MAR. This method was capable of substantially reducing artifacts produced by head movements.

Comparative evaluation of an ambulatory EEG platform vs. clinical gold standard.

Electroencephalography (EEG) testing in clinical labs makes use of large amplifiers and complex software for data acquisition. While there are new ambulatory electroencephalogram (EEG) systems, few have been directly compared to a gold standard system. Here, an ultra-low power wireless EEG system designed by Imec is tested against the gold standard Neuroscan SynAmps2 EEG system, recording simultaneously from the same laboratory cap prepared with electrode gel. The data was analyzed using correlation analysis for both time domain and frequency domain data. The analysis indicated a high Pearson's correlation coefficient (mean=0.957, median=0.985) with high confidence (mean P=0.002) for 10-second sets of data transformed to the frequency domain. The time domain results had acceptable Pearson's coefficient (mean=0.580, median =0.706) with high confidence (mean P=0.008).

Investigating the impact of force and movements on impedance magnitude and EEG.

The success of applying dry sensor technology in measuring electroencephalogram (EEG) signals will have a significant impact on a wider adoption of brain activity monitoring in ambulatory as well as real life solutions. The presence of motion artifacts is the major obstacle in applying dry sensors for long-term EEG monitoring. In this paper we assess the impact of external forces applied on a dry EEG electrode as well as the impact of head and body movements on the electrode-tissue contact impedance and the EEG signal. The data collection method and the preliminary correlation analysis are presented. The analysis demonstrates that the impedance magnitude and EEG changes are highly correlated when artifacts are induced by the application of force or head and body movements, only in case these artifacts are short (less than 3s) and exhibit regular pattern. The correlation between the EEG and impedance magnitude is lower for longer artifact segments, especially the ones containing artifacts with irregular movements or large variations in the applied force. This indicates a time-dependent, non-linear relation between the artifact-related phenomena, impedance magnitude, and EEG.

Towards mental stress detection using wearable physiological sensors.

Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.

Towards wireless emotional valence detection from EEG.

Intelligent affective computers can have many medical and non-medical applications. However today's affective computers are limited in scope by their transferability to other application environments or that they monitor only one aspect of physiological emotion expression. Here, the use of a wireless EEG system, which can be implemented in a body area network, is used to investigate the potential of monitoring emotional valence in EEG, for application in real-life situations. The results show 82% accuracy for automatic classification of positive, negative and neutral valence based on film clip viewing, using features containing information on both the frequency content of the EEG and how this changes over time.

Potential and challenges of body area networks for personal health.

This paper illustrates how body area network technology may enable new personal health concepts. A BAN technology platform is presented, which integrates technology building blocks from the Human++ research program on autonomous wireless sensors. Technology evaluation for the case of wireless sleep staging and real-time arousal monitoring is reported. Key technology challenges are discussed. The ultimate target is the development of miniaturized body sensor nodes powered by body-energy, anticipating the needs of emerging personal health applications.

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.