Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators

Early recognition of ventricular fibrillation (VF) and electrical therapy are key for the survivalof out-of-hospital cardiac arrest (OHCA) patients treated with automated external defibrilla-tors (AED). AED algorithms for VF-detection are customarily assessed using Holter record-ings from public electrocardiogram (ECG) databases, which may be different from the ECGseen during OHCA events. This study evaluates VF-detection using data from both OHCApatients and public Holter recordings. ECG-segments of 4-s and 8-s duration were ana-lyzed. For each segment 30 features were computed and fed to state of the art machinelearning (ML) algorithms. ML-algorithms with built-in feature selection capabilities wereused to determine the optimal feature subsets for both databases. Patient-wise bootstraptechniques were used to evaluate algorithm performance in terms of sensitivity (Se), speci-ficity (Sp) and balanced error rate (BER). Performance was significantly better for publicdata with a mean Se of 96.6%, Sp of 98.8% and BER 2.2% compared to a mean Se of94.7%, Sp of 96.5% and BER 4.4% for OHCA data. OHCA data required two times morefeatures than the data from public databases for an accurate detection (6 vs 3). No signifi-cant differences in performance were found for different segment lengths, the BER differ-ences were below 0.5-points in all cases. Our results show that VF-detection is morechallenging for OHCA data than for data from public databases, and that accurate VF-detection is possible with segments as short as 4-s

Diagnostico del Impacto del Palangre de Fondo en los Hábitats Bentónicos en los LICs de la RN20000

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Assessing the state of marine biodiversity in the Northeast Atlantic

The Northeast Atlantic, a highly productive maritime area, has been exposed to a wide range of direct human pressures, such as fishing, shipping, coastal development, pollution, and non-indigenous species (NIS) introductions, in addition to anthropogenically-driven global climate change. Nonetheless, this regional sea supports a high diversity of species and habitats, whose functioning provides a variety of ecosystem services, essential for human welfare. In 2017, OSPAR, the Northeast Atlantic Regional Seas Commission, delivered an assessment of marine biodiversity for the Northeast Atlantic. This assessment examined biodiversity indicators separately to identify changes in Northeast Atlantic biodiversity, but stopped short of determining the status of biodiversity for many species and habitats. Here, we expand on this work and for the first time, a semi-quantitative approach is applied to evaluate holistically the state of Northeast Atlantic marine biodiversity across marine food webs, from plankton to top predators, via fish, pelagic and benthic habitats, including xeno-biodiversity (i.e. NIS). Our analysis reveals widespread degradation in marine ecosystems and biodiversity, particularly for marine birds and coastal bottlenose dolphins, as well as for benthic habitats and fish in some regions. The poor biodiversity status of these ecosystem components is likely the result of cumulative effects of human activities, such as habitat destruction or disturbance, overexploitation, eutrophication, the introduction of NIS, and climate change. Bright spots are also revealed, such as recent signs of recovery in some fish and marine bird communities and recovery in harbour and grey seal populations and the condition of coastal benthic communities in some regions. The status of many indicators across all ecosystem components, but particularly for the novel pelagic habitats, food webs and NIS indicators, however, remains uncertain due to gaps in data, unclear pressure-state relationships, and the non-linear influence of some pressures on biodiversity indicators. Improving monitoring and data access and increasing understanding of pressure-state relationships, including those that are non-linear, is therefore a priority for enabling future assessments, as is consistent and stable resourcing for expert involvement

CPR artefact removal from VF signals by means of an adaptive Kalman filter using the chest compression frequency as reference signal

Chest compressions during Cardiopulmonary Resuscitation (CPR) generate important artefacts which impede the correct analysis of the ECG signal by an automated external defibrillator (AED). The suppression of the CPR artefact is thus necessary during resuscitation. The filtering technique proposed in the present paper is based on a Kalman adaptive scheme where the CPR artefact model follows the instantaneous frequency of the chest compressions, while the VF signal is modeled as a sum of two sinusoidal signals. The good results obtained for the increase of the signal to noise ratio and the excellent sensitivity scores provided by a commercial AED on the cleaned VF signals certifies the value of the method. 1