No abstract

In this paper, we propose a fast novel nonlinear filtering method named Relative-Energy (Rel-En), for robust short-term event extraction from biomedical signals. We developed an algorithm that extracts short- and long-term energies in a signal and provides a coefficient vector with which the signal is multiplied, heightening events of interest. This algorithm is thoroughly assessed on benchmark datasets in three different biomedical applications, namely ECG QRS-complex detection, EEG K-complex detection, and imaging photoplethysmography (iPPG) peak detection. Rel-En successfully identified the events in these settings. Compared to the state-of-the-art, better or comparable results were obtained on QRS-complex and K-complex detection. For iPPG peak detection, the proposed method was used as a preprocessing step to a fixed threshold algorithm that lead to a significant improvement in overall results. While easily defined and computed, Rel-En robustly extracted short-term events of interest. The proposed algorithm can be implemented by two filters and its parameters can be selected easily and intuitively. Furthermore, Rel-En algorithm can be used in other biomedical signal processing applications where a need of short-term event extraction is present.

This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were processed. ECGs were used as ground truth and annotated for the following types of rhythm: sinus rhythm (SR), AF, and ventricular arrhythmias (VA). A total of 381/1370/415 10-s epochs were obtained for the three categories, respectively. After pre-processing and removal of segments corresponding to motion artifacts, two different types of feature were derived from the PPG signals: the interbeat interval-based features and the wave-based features, consisting of complexity/organization measures that were computed either from the PPG waveform itself or from its power spectral density. Decision trees were used to assess the discriminative capacity of the proposed features. Three classification schemes were investigated: AF against SR, AF against VA, and AF against (SR&VA). The best results were achieved by combining all features. Accuracies of 98.1/95.9/95.0 %, specificities of 92.4/88.7/92.8 %, and sensitivities of 99.7/98.1/96.2 % were obtained for the three aforementioned classification schemes, respectively. Graphical Abstract Atrial fibrillation detection using PPG signals.

Studies of gastrointestinal function during sleep are hampered by lack of applicable techniques. Recent development of a novel ambulatory telemetric capsule system, which can be used in conjunction with polysomnography, offers a solution to this problem. The 3D-Transit system consists of ingestible electromagnetic capsules traceable through a portable extracorporeal receiver while traversing the gut. During sleep monitored by polysomnography, gastrointestinal motility was concurrently investigated using 3D-Transit in nine healthy subjects. Overall, the amplitude of gastric contractions decreased with depth of sleep (light sleep, N2 versus deep sleep, N3; P<0.05). Progression through the small intestine did not change with depth of sleep (Kruskal–Wallis probability =0.1), and there was no association between nocturnal awakenings or arousals and the occurrence of colonic or small intestinal propagating movements. Basal colonic activity was suppressed during both deep sleep (P<0.05) and light sleep (P<0.05) when compared with nocturnal wake periods. In conclusion, the novel ambulatory 3D-Transit system combined with polysomnography allows minimally invasive and completely ambulatory investigation of associations between sleep patterns and gastrointestinal motility.