Individual identification via electrocardiogram analysis

Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations

Achievable tolerances in robotic feature machining operations using a low-cost hexapod

Portable robotic machine tools potentially allow feature machining processes to be brought to large parts in various industries, creating an opportunity for capital expenditure and operating cost reduction. However, robots lack the machining capability of conventional equipment, which ultimately results in dimensional errors in parts. This work showcases a low-cost hexapod-based robotic machine tool and presents experimental research conducted to investigate how the widely researched robotic machining challenges, e.g. structural dynamics and kinematics, translate to achievable tolerance ranges in real-world production to highlight currently feasible applications and provide a context for considering technology improvements. Machining trials assess the total dimensional errors in the final part over multiple geometries. A key finding is error variation which is in the sub-millimetre range, although, in some cases, upper tolerance limits < 100 μm are achieved. Practical challenges are also noted. Most significantly, it is demonstrated that dimensional machining error is mainly systematic in nature and therefore that the total error can be dramatically reduced with in situ measurement and compensation. Potential is therefore found to achieve a flexible, high-performance robotic machining capability despite complex and diverse underlying scientific challenges. Overall, the work presented highlights achievable tolerances in low-cost robotic machining and opportunities for improvement, also providing a practical benchmark useful for process selection

Detection of transient-evoked otoacoustic emissions and the design of time windows

A new approach to the design of time windows is presented for detection of transient-evoked otoacoustic emissions (TEOAE). The windows are designed with reference to a minimum mean square error criterion involving the correlation properties of the ensemble of responses. Latency information is introduced in the detection process by windowing at different scales that result from wavelet decomposition. The significance of both subject- and population-specific time windows is investigated. The detection performance is evaluated on a health screen database consisting of 4989 records. The results show that the present Approach to windowing yields a significantly better performance in separating normal-hearing subjects from hearing-impaired subjects when compared to detection based on unwindowed signals. With time windowing, the specificity increased with almost 15% at a fixed sensitivity of 90%

Exercise testing for non-invasive assessment of atrial electrophysiology in patients with persistent atrial fibrillation

The abstract with its heading should not be more than 75 mm long. This is equivalent to 18 lines of text. Leave 1 line space at the bottom of the abstract before continuing with the next heading. The autonomic nervous system modulates atrial electrophysiology in atrial fibrillation (AF). The purpose of this study was (1) to non-invasively characterize the effects of exercise on atrial fibrillatory rate as marker of atrial refractoriness in patients with persistent AF and (2) to identify clinical and electrocardiographic predictors for rate response. In 15 patients with persistent AF, mean fibrillatory rate assessed by spatiotemporal QRST cancellation and time-frequency analysis remained unchanged with exercise. There were, however, 6 responders (rate change > 2.5%), with either a rate increase (N=5, 25±9 fpm) or decrease (N=1, -13 fpm). Absolute fibrillatory rate change (%) correlated inversely with baseline fibrillatory rate (r= -0.543, p=.045). In conclusion, sympathetic activation by exercise modulates atrial electrophysiology in some patients which can be monitored using time-frequency analysis. Higher baseline fibrillatory rates are associated with less autonomic modulation indicating advanced electrical remodeling